Engineering Innovations Volume 16

Scientific.Net

2026 Trans Tech Publications Ltd, Switzerland

Trans Tech Publications Ltd
Seestrasse 24c
CH-8806 Baech
Switzerland
https://www.scientific.net

Volume 16 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

Full text available online at https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Distributed worldwide by
Trans Tech Publications Ltd
Seestrasse 24c
CH-8806 Baech
Switzerland
Phone: +41 (44) 9221022
e-mail: sales@scientific.net

Table of Contents

Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Jetcy Elizabeth Philip, Jency Sara Kurian, Jeena B. Edayadiyil

Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Ian Dominic F. Tabañag, Lou Janssen L. Calotes, Josef Dante A. Dumalagan, John Zephaniel L. Merecido, Luis K. Cabatingan

Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Maria Theresa Isla-Cabaraban, Ivan Vergel Iligan, Romelie Albia, Czarine Maderal, Jenny Navarro, Gunnar Marc Shane Cabaraban

Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

Supriyadi Supriyadi, Nita Oni Indriani, Khumaedi Khumaedi

METEORO: Low-Cost IoT Weather Station

Douglas Adalberto Aguilar, Max Gabriel Sánchez Flores, Jhoan Daniel Martínez Marroquín, Oscar Eduardo Díaz Gómez

Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Muhammad Rasyid Ridla, Haidar Ali, Nandang Mufti, Saparullah Saparullah, M. Tommy Hasan Abadi, Naufal Fiego Fadhlullah, Erma Surya Yuliana, Muladi Muladi, Yoyok Adisetio Laksono

IoT Security: Solutions to Mitigate Attacks

Andrea Gisselle Menjivar

Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

Callum Scott-Russell, Simon M. Barrans, Paul Hudson

Game-Theoretical Viewpoint to Optimal Design

Vladimir Kobelev

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 1-10

doi: 10.4028/p-Xhqw9E

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-08-13

Revised:
2025-11-21

Accepted:
2025-11-24

Online:
2026-01-07

Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

Jetcy Elizabeth Philip , Jency Sara Kurian and Jeena B. Edayadiyil

Student, Amal Jyothi College of Engineering, Kanjirappally, Kottayam, Kerala, India

Assistant Professor, Amal Jyothi College of Engineering, Kanjirappally, Kottayam, Kerala, India

a*jetcyelizabethphilip@ajce.in, bjencysarakurian@amaljyothi.ac.in, cjeenabedayadiyil@amaljyothi.ac.in

Keywords: structural resilience, braced corrugated shear panel, seismic performance analysis, lateral load resistance, shear strength enhancement, energy dissipation capacity, finite element modeling (FEM), earthquake-resistant structures, steel structural systems, structural stability under dynamic loads.

Abstract

This study proposes a novel braced corrugated shear panel (BCSP) system aimed at enhancing the seismic resilience of steel structures. In contrast to conventional flat shear panels, the BCSP incorporates stiffening ribs and corrugated geometry to improve deformability, delay local and global buckling, and increase lateral load-carrying capacity under cyclic loading. Metal shear panels are widely recognized for their stable hysteretic behavior, particularly in high seismic regions; nevertheless, their performance can be further improved through optimized geometry. This research examines the influence of corrugation orientation and angle on the behavior of BCSPs subjected to cyclic loading, demonstrating that replacing traditional thin ductile shear panels with a corrugated configuration significantly enhances structural response. The results show that horizontal corrugation provides superior strength, stiffness, and ductility compared to vertical or inclined corrugation, while the combined effect of bracing and corrugation increases lateral load-resisting capacity and facilitates easier post-earthquake replacement. Overall, BCSPs with horizontal corrugation exhibit optimal performance and high structural resilience in earthquake-prone regions, offering a promising advancement for future steel structure design.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

The demand for seismic-resilient structural systems has grown substantially as modern buildings are increasingly expected not only to resist collapse but also to maintain functionality after major earthquakes. Advances in materials and structural configurations have focused on improving energy dissipation, delaying buckling, and enhancing post-event reparability. Within this context, steel shear panels have emerged as highly effective components due to their stable hysteretic behavior and ability to dissipate energy under cyclic loading.
Significant progress has been made in the development of corrugated steel panels and bracing systems aimed at improving lateral resistance. Corrugated profiles have demonstrated increased shear strength, stiffness, and buckling resistance compared to traditional flat panels, while bracing systems such as buckling-restrained braces (BRBs) and eccentrically braced frames (EBFs) have shown notable improvements in ductility and cyclic performance. Recent innovations-including hybrid braced-panel systems, demountable metallic shear walls, and partially connected or perforated steel plate systems-highlight how these technologies enhance stability, energy dissipation, and ease of post-earthquake repair. These developments collectively underscore the potential of integrating corrugated geometries with advanced bracing mechanisms to create robust and resilient seismic systems.
Despite these advancements, few studies have systematically compared how different corrugation orientations and angles influence the cyclic performance of braced corrugated panels, leaving a significant gap in identifying the most effective geometric configuration for seismic applications. Corrugation geometry plays a crucial role in controlling buckling behavior, stiffness distribution, and
the overall energy dissipation capacity of steel panels; therefore, understanding its influence in the presence of bracing systems is essential for optimizing performance. Addressing this research gap, the present study conducts a detailed investigation into the cyclic behavior, post-buckling response, and overall seismic resilience of braced corrugated shear panels (BCSPs). The analysis focuses on how variations in corrugation orientation-such as horizontal, vertical, and inclined profiles and corrugation angles alter key structural responses including strength, stiffness degradation, ductility, and hysteretic energy dissipation. By examining these parameters, the study aims to determine the corrugation configuration that offers the most favorable balance between stability, deformability, and repairability. Ultimately, the outcomes of this research are intended to contribute to the development of more efficient, resilient, and easily replaceable seismic force-resisting systems, advancing the design of steel structures in earthquake-prone regions and supporting rapid post-event recovery.

II. Methodology

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

The research employed Finite Element Analysis (FEA) to investigate the cyclic behavior of braced corrugated shear panels (BCSPs). Numerical simulations were conducted using ANSYS Workbench 2022 R2. The research procedure comprised the following steps.
2.1 Modeling Approach: Three-dimensional finite element models of both braced ductile shear panels (BDSPs) and the proposed braced corrugated shear panels (BCSPs) were developed. Thin steel plates were modelled using SHELL181 elements, whereas SOLID185 elements were used for boundary frames and bracing components.Adaptive meshing ensured a balance between computational efficiency and result accuracy, particularly in regions prone to high stress concentration and local buckling.
2.2 Boundary Conditions and Loading Protocol: The bottom nodes of the lower beam were fully restrained in all translational and rotational degrees of freedom. Lateral displacement was applied to the top interface to induce in-plane shear deformation.The cyclic loading protocol recommended by FEMA was adopted to capture stiffness degradation, strength deterioration, and hysteretic response under repeated loading cycles.
2.3 Parametric Study: A detailed parametric study was performed by substituting the BDSP configuration with the BCSP system. The parameters investigated included:
2.3.1 Corrugation orientation: horizontal, vertical, and inclined
2.3.2 Corrugation angle: and

These variations were examined to evaluate their effects on panel strength, stiffness, ductility, and energy dissipation capacity.

III. Parametric Study

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

To validate the accuracy of the developed Finite Element Analysis (FEA) models, the cyclic behavior of corrugated braced shear panels was examined. The influence of corrugation angle and orientation on the overall structural performance was investigated. The modelling approach and material properties proposed by Guohua Sun et al. (2021) were adopted; however, the thin ductile shear panel used in the reference study was replaced with a corrugated shear panel for the present analysis.

Fig. 1. Moment-resisting steel frame with innovative ductile thin shear panels.

Fig. 1. Moment-resisting steel frame with innovative ductile thin shear panels.

Specimen Details: The experimental specimens comprised a thin rectangular ductile shear panel with concentric X-braces and stiffening ribs. The dimensions of the shear panel and the material properties used in the FEA models were derived from the experimental study (Table I).

Fig. 2. Test specimen details.

Fig. 2. Test specimen details.

Table 3.1. Details of specimen for validated Model with Guohua Sun. et.al. [2021].

Specimen | Dimension of the shear panel, h × l (mm) | Thickness of shear panel, tw (mm) | Height- to the thickness of the shear panel, λ | Thickness of flange, tf (mm) | Thickness of stiffening rib, tsr (mm) | Spacing of stiffening rib, Dh/Dv (mm)
BDSP 3 | 450 × 350 | 3 | 117 | 6 | 3 | ------
BDSP 5 | 450 × 350 | 3 | 36.3 | 6 | 3 | 145/110

Material Properties: Table 3.2 presents the mechanical properties of Q235B steel used in the validation study. The yield stress, ultimate tensile strength, and elastic modulus were consistent with the experimental data.

Table 3.2. Material properties of the validated model with Guohua Sun. et.al. [2021].

Specimen | Dimension of the shear panel, h × l (mm) | Thickness of shear panel, tw (mm) | Height- to the thickness of the shear panel, λ | Thickness of flange, tf (mm) | Thickness of stiffening rib, tsr (mm) | Spacing of stiffening rib, Dh/Dv (mm)
BDSP 3 | 450 × 350 | 3 | 117 | 6 | 3 | ------
BDSP 5 | 450 × 350 | 3 | 36.3 | 6 | 3 | 145/110

Loading Conditions: The same FEMA cyclic loading protocol was applied in both the FEA and experimental tests, with boundary conditions involving full restraint at the bottom interface and lateral displacement applied to the top interface (illustrated in Fig. 1).

Table 3.3. Loading condition with Guohua Sun. et.al. [2021].

LENGTH | DRIFT RADIAN | DRIFT PER | DRIFT DISPLACEMENT (mm)
0.84 | 0.00375 | 0.375 | 3.15
0.84 | 0.005 | 0.5000 | 4.20
0.84 | 0.0075 | 0.7500 | 6.30
0.84 | 0.01 | 1.0000 | 8.40
0.84 | 0.015 | 1.5000 | 12.61
0.84 | 0.02 | 2.0000 | 16.81

The study was conducted to evaluate the influence of corrugation orientation and corrugation angle on the performance of Braced Corrugated Shear Panels (BCSP). The parametric analysis was performed on BCSP models with varying orientations and corrugation angles to assess their effect on load-bearing capacity, ductility, and lateral stiffness.

A. Effect of Corrugation Orientation

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

The corrugation orientation plays a significant role in the structural performance of BCSPs under cyclic loading. Three different corrugation orientations were considered: horizontal, vertical, and inclined . The key results obtained from the analysis are compiled and presented in Table I.

Horizontal Corrugation: Panels with horizontal corrugation demonstrated superior performance, showing a higher load-bearing capacity compared to vertical corrugation and a improvement over inclined corrugation.

Vertical Corrugation: Vertical orientation showed intermediate performance, providing moderate ductility and stiffness.

Inclined Corrugation: Inclined panels exhibited the lowest load-carrying capacity but still demonstrated sufficient ductility for seismic applications.

Table 3.4. Comparison of Load-Bearing Capacity for Different Corrugation Orientations.

Orientation | Maximum Load (kN) | Maximum Displacement (mm)
Horizontal | 412.68 | 8.41
Vertical | 389.10 | 8.39
Inclined | 359.38 | 8.42

As illustrated in Figure 3, the hysteretic curves for horizontally oriented BCSPs showed the fullest loops, indicating better energy dissipation and improved ductility.

Fig. 3. Hysteretic Curve of braced corrugated shear panel with different orientation.

Fig. 3. Hysteretic Curve of braced corrugated shear panel with different orientation.

The skeleton curves for BCSPs with various corrugation orientations (horizontal, vertical, and inclined) are illustrated in Figure 4. Based on the analysis, the following observations can be made:

Horizontal Orientation: This configuration displayed the highest peak loads at each drift level, with the maximum load-bearing capacity reaching 412.68 kN at a drift ratio of . This indicates superior lateral stiffness and energy dissipation compared to the other orientations.

Vertical and Inclined Orientations: The vertical orientation showed moderate load-bearing capacity, while the inclined configuration demonstrated the lowest peak loads, indicating reduced lateral stiffness. However, both configurations still provided sufficient load-carrying capacity for moderate seismic applications.

Fig. 4. Skeleton Curves for Different Corrugation Orientations".

Fig. 4. Skeleton Curves for Different Corrugation Orientations".

B. Effect of Corrugation Angle

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

To examine the influence of corrugation angle, horizontal BCSP specimens with corrugation angles of , and were analyzed. As summarized in Table II, an increase in corrugation angle resulted in enhanced load-bearing capacity and stiffness of the panels.

Corrugation Angle: The panel with a corrugation angle exhibited the highest loadcarrying capacity, with a improvement over the panel with a angle and a improvement over the panel with a angle.

Corrugation Angle: This angle provided balanced performance between strength and ductility, showing substantial load-bearing capacity with moderate deformation.

Corrugation Angle: The panel with the lowest angle had the least stiffness and strength but still performed adequately for low-to-medium seismic conditions.

Table 3.5

Corrugation Angle | Maximum Load (kN) | Maximum Displacement (mm)
30° | 314.49 | 8.41
60° | 423.60 | 8.41
90° | 427.12 | 8.41

Table 3.5 indicates that panels incorporating a corrugation angle exhibit superior resistance to lateral deformation and buckling compared to the other configurations. The hysteresis curves for the various corrugation angles are illustrated in Figure 3, depicting the cyclic response of the panels under incrementally applied lateral displacements.

Corrugation Angle: The BCSP with a corrugation angle displayed a relatively narrow hysteresis loop, indicating lower energy dissipation and reduced ductility. The maximum load at drift was recorded as 314.49 kN . The narrow loops also suggest less capacity to absorb and dissipate energy under cyclic loading, which is less desirable for structures in seismic regions.

Corrugation Angle: The hysteresis curve for the corrugation angle exhibited a wider loop compared to the panel, showing enhanced energy dissipation and greater ductility. The peak load reached 423.60 kN , with minimal pinching of the hysteresis loop, indicating better stability and performance under cyclic loads.

Corrugation Angle: The BCSP with a corrugation angle provided the widest hysteresis loops, which is indicative of superior energy dissipation and ductility. The maximum load reached 427.12 kN at a drift, with the loops showing minimal pinching and a stable cyclic response. The performance of the corrugated panel suggests that it is the most suitable configuration for applications requiring high lateral stiffness and energy absorption, such as in earthquake-resistant structures.

Fig. 5. Hysteresis Curves for BCSPs with , and Corrugation Angles.

Fig. 5. Hysteresis Curves for BCSPs with , and Corrugation Angles.

The influence of corrugation angle on the skeleton curves was analyzed, as illustrated in Figure 6. Three corrugation angles- , and -were examined, and the results reveal a pronounced effect of corrugation angle on the structural performance of BCSPs.

Corrugation Angle: This configuration exhibited the highest peak load, reaching 427.12 kN at a drift ratio. The higher angle provided greater resistance to lateral deformation, enhancing the overall buckling resistance and load-carrying capacity.

and Angles: The angle performed slightly better than the configuration, with a maximum load of 423.60 kN , while the angle exhibited the lowest peak loads. This suggests that increasing the corrugation angle improves both the stiffness and strength of the BCSP.

Fig. 6. Skeleton Curves for Different Corrugation Angles.

Fig. 6. Skeleton Curves for Different Corrugation Angles.

C. Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

The parametric study results indicate that corrugation orientation and angle significantly influence the load-bearing capacity and overall performance of BCSPs. Horizontal corrugation with a angle provided the best performance, demonstrating superior lateral stiffness, load-carrying capacity, and energy dissipation compared to other configurations. These findings suggest that BCSPs with horizontal corrugation and a high corrugation angle are optimal for structures in high seismic regions.

D. Results & Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

The parametric study revealed significant differences in the performance of braced corrugated shear panels based on corrugation orientation and angle.

Effect of Corrugation Orientation: Horizontal corrugation provided the highest loadcarrying capacity, with a improvement over vertical orientation and a improvement over inclined corrugation (Figure 3). Horizontal corrugation also demonstrated higher stiffness and ductility under cyclic loading.

Effect of Corrugation Angle: As the corrugation angle increased from to , the inplane stiffness and load-carrying capacity of the BCSP increased. The panel with a corrugation angle exhibited the highest ultimate strength, as shown in Table I. This was attributed to enhanced buckling resistance provided by the increased corrugation angle.
The FEA analysis also indicated that horizontal corrugated panels at provide superior seismic performance, with significant improvements in energy dissipation and lateral stiffness compared to other configurations.
The hysteresis curves clearly show that increasing the corrugation angle from to significantly enhances the performance of BCSPs in terms of ductility, energy dissipation, and cyclic stability. Panels with higher corrugation angles exhibited wider loops with minimal pinching, indicating a better ability to sustain cyclic deformations and absorb seismic energy.

Energy Dissipation: The corrugation angle showed the greatest ability to dissipate energy, making it ideal for structures subjected to seismic loading.

Ductility: The wider loops in the angle configuration demonstrated superior ductility, allowing the panel to undergo greater deformation without losing stability.

Stiffness and Strength: The BCSP maintained the highest stiffness and strength under cyclic loading, as evidenced by the peak loads in the hysteresis curves.
Thus, the BCSP with a corrugation angle provides the best performance in terms of both loadbearing capacity and seismic resilience, making it an optimal choice for earthquake-prone regions.

Fig. 7. Hysteretic curve of horizontally corrugated BCSP with & BDSP with and without stiffener.

Fig. 7. Hysteretic curve of horizontally corrugated BCSP with & BDSP with and without stiffener.

The skeleton curves clearly illustrate the superior performance of horizontally corrugated BCSPs with a higher corrugation angle. The horizontal corrugation not only increased the load-bearing capacity but also provided enhanced ductility and energy dissipation, making it an ideal configuration for high seismic applications. Both orientation and angle significantly affect the cyclic performance of BCSPs. Horizontal corrugation with a higher angle (e.g., ) optimizes the strength and stiffness of the panel, allowing it to absorb and dissipate more seismic energy. The skeleton curves further validate the results obtained from the parametric study, confirming that these configurations are optimal for improving structural resilience under lateral loads.

Fig. 8. Skeleton curve of horizontally corrugated BCSP with & BDSP with and without stiffener.

Fig. 8. Skeleton curve of horizontally corrugated BCSP with & BDSP with and without stiffener.

V. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

This study demonstrates that horizontally corrugated Braced Corrugated Shear Panels (BCSPs) provide superior seismic performance compared to Braced Ductile Shear Panels (BDSPs), both with and without stiffeners. The key findings are as follows:

Horizontal corrugation at significantly enhances structural performance, yielding the highest load-carrying capacity, stiffness, and buckling resistance. A exhibits a 38% increase in ultimate load capacity compared to stiffened braced thin ductile shear panels.

BCSPs achieve better deformation control, exhibiting a reduction in maximum lateral displacement relative to stiffened BDSPs, thus improving stability under cyclic loading.

Corrugation angle strongly influences capacity: Increasing the angle from to and progressively improves load-carrying capacity, while having no significant effect on maximum displacement.

Finite Element Analysis (FEA) validation confirms consistency with experimental trends, indicating that stiffened BDSPs benefit from added load resistance; however, BCSPs-
particularly those with horizontal corrugation-demonstrate superior strength, stiffness, and cyclic stability.
Overall, horizontally corrugated BCSPs with corrugation offer the most effective configuration for seismic load resistance, combining enhanced stiffness, strength, energy dissipation, and reduced lateral deformation. These characteristics confirm their potential as a practical and resilient solution for earthquake-resistant steel structures.

References

The original version of this paper is available on https://www.scientific.net/EI.16.1.pdf

Sun, G., Bao, C., Liu, W., & Fang, Y. Cyclic behavior of an innovative braced ductile thin shear panel. Structures, 32 (2021), 973-986. https://doi.org/10.1016/j.istruc.2021.03.075.

Emami, F., Mofid, M., & Vafai, A. Experimental study on cyclic behavior of trapezoidally corrugated steel shear walls. Engineering Structures, 48 (2013), 750-762. .

Atlayan, O., & Charney, F. A. Hybrid buckling-restrained braced frames. Journal of Constructional Steel Research, 96 (2014), 95-105. .

Farzampour, A., Laman, J. A., & Mofid, M. Behavior prediction of corrugated steel plate shear walls with openings. Journal of Constructional Steel Research, 114 (2015), 258-268. .

Wei, M.-W., Liew, J. Y. R., Xiong, M.-X., & Fu, X.-Y. Hysteresis model of a novel partially connected buckling-restrained steel plate shear wall. Journal of Constructional Steel Research, 125 (2016), 74-87. .

Rahnavard, R., Hassanipour, A., Suleiman, M., & Mokhtari, A. Evaluation of eccentrically braced frames with single and double shear panels. Journal of Building Engineering, 10 (2017), 13-25. .

Dou, C., Pi, Y.-L., & Gao, W. Shear resistance and post-buckling behavior of corrugated panels in steel plate shear walls. Thin-Walled Structures, 131 (2018), 816-826. .

Tapia-Hernández, E., & García-Carrera, S. Inelastic response of ductile eccentrically braced frames. Journal of Building Engineering, 26 (2019), 100903. .

Emamyari, A., Sheidaii, M. R., Kookalanifar, A., Showkati, H., & Akbarzadeh, N. Experimental study on cyclic behavior of stiffened perforated steel shear panels. Structures, 27 (2020), 2400-2410. .

Yossef, N. M. A new approach to estimate the shear strength of curved corrugated steel webs. Structures, 24 (2020), 400-414. .

Sun, G., & Zhu, Y. Cyclic testing of an innovative self-centering X-braced ductile shear panel. Engineering Structures, 244 (2021), 112732. .

Feng, L., Sun, T., & Ou, J. Elastic buckling analysis of steel-strip-stiffened trapezoidal corrugated steel plate shear walls. Journal of Constructional Steel Research, 184 (2021), 106833. .

Li, Z., Ge, L., Qi, Y., Geng, Y., & Teng, J. Design and experimental study of a bucklingrestrained steel plate shear wall with novel panels for enhanced capacity and energy dissipation. Engineering Structures, 244 (2021), 112812. .

Quan, C., Wang, W., Li, Y., & Lu, Z. Cyclic behavior of demountable metallic corrugated shear panel dampers. Journal of Building Engineering, 61 (2022), 105228. .

Yu, Y., Lin, S., Zhao, F., Tian, P., & Jiang, L. A built-up type horizontally corrugated steel plate shear wall with a special shape. Engineering Structures, 250 (2022), 113458. .

Quan, C., Wang, W., & Li, Y. Hysteretic model and resilient application of corrugated shear panel dampers. Thin-Walled Structures, 178 (2022), 109477. .

Sun, H.-J., Guo, Y.-L., Wen, C.-B., & Zuo, J.-Q. Local and global buckling prevention design of corrugated steel plate shear walls. Journal of Building Engineering, 68 (2023), 106055. .

Wen, C.-B., Guo, Y.-L., Zuo, J.-Q., & Sun, H.-J. Global shear stability capacity of trapezoidally corrugated steel plate shear walls with boundary elements. Journal of Building Engineering, 72 (2023), 106553. .

Li, Y., Wang, W., Su, S., Quan, C., Xu, J., Jia, Y., & Mi, J. Seismic performance assessment of eccentrically braced steel frames using demountable metallic corrugated shear panel dampers. Journal of Constructional Steel Research, 207 (2023), 107972. .

Xiang, Y., Zhou, X., Shi, Y., Zhou, J., Ke, K., & Deng, F. Seismic performance of cold-formed thin-walled steel frames with K-shaped braced shear panels. Thin-Walled Structures, 184 (2023), 110449. .

Xiang, Y., Zhou, X., Ke, K., Shi, Y., & Xu, L. Experimental research on the seismic performance of cold-formed thin-walled steel frames with braced shear panels. Thin-Walled Structures, 182 (2023), 110210. .

Gusella, F., Mei, A., & Orlando, M. An innovative ductile bracing system easily repairable after a seismic event. Procedia Structural Integrity, 44 (2023), 790-797.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 11-36

doi: 10.4028/p-4kSdat

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-10-20

Revised:
2025-05-10

Accepted:
2025-06-23

Online:
2026-01-07

Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

lan Dominic F. Tabañag , Lou Janssen L. Calotes , Josef Dante A. Dumalagan , John Zephaniel L. Merecido , Luis K. Cabatingan

Philippine Council for Industry, Energy, and Emerging Technology Research and Development, Department of Science and Technology (DOST-PCIEERD), Bicutan, Taguig City, 1631, Philippines

Engineering and Industrial Research Division, National Research Council of the Philippines, Department of Science and Technology (DOST-NRCP), Bicutan, Taguig City, 1631, Philippines

Department of Chemical Engineering, University of San Carlos, Talamban, Cebu City 6000, Philippines

elkcabatingan@usc.edu.ph

Keywords: briquette quality, briquetting, lignin, binder, sugarcane bagasse.

Abstract

As the world shifts from a fossil-based to bio-based economy, energy production via the valorization of biomass residues is promising. However, raw biomass utilization for energy production proves to be challenging owing to their low bulk densities and hygroscopic nature. These problems are addressed through briquetting. This work focuses on the utilization of sugarcane bagasse (SCB) - an abundant agricultural residue in the Philippines - with lignin as a binder for solid fuel briquettes. The effects of SCB-to-lignin ratio and compaction pressure on the fuel characteristics and quality of the briquette were investigated. Briquetting experiments were carried out in a Carver press at a constant pressing temperature of and varying compaction pressures (CP and 11 MPa ) and binder content ( and ). Briquetting of SCB resulted in an increase in bulk density by 7 to 8 times ( ) compared to loose SCB ( ). From ANOVA, it was found out that CP significantly affects bulk density ( ). The higher heating value (HHV) and compressive strength (CS) rose to as high as , and , respectively. It was found out that BC significantly affects the HHV ( ) but has an insignificant effect on the CS ( ) while CP has insignificant effects on both HHV and CS ( ). Overall, SCB-lignin briquettes are able to overcome storage and handling challenges through improved bulk density, stacking capability of up to briquettes at the lowest achieved CS, retarded moisture uptake rate ( per hr ) and an equilibrium moisture content of , making this fuel viable for domestic usage as determined by current briquetting standards.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

At present, the world is strongly dependent on the use of fossil fuels for energy production and continued dependence will ultimately lead to depletion of fossil reserves, as well as aggravate the effects of global warming. A statistical review of world energy by British Petroleum Global showed that the fossil fuel demand in 2023 constitutes of the world's energy consumption. Fossil fuels include oil, gas and coal contributing , and , respectively in the global energy mix[1]. Around the world, initiatives have been done to move from today's fossil-based economy to a more sustainable economy that is based on renewable resources such as biofuels from biomass residues as alternatives for fossil fuels and coal. Utilization of biomass residues not only effectively closes the carbon cycle [2] but also eliminates the problem biofuel production poses on the food industry where the production of biofuels competes with the food industry for raw materials [3].

The Philippines, being an agricultural country, generates a considerable amount of biomass residues. According to a 2020 report by the Philippine Statistics Authority (PSA), of the total
agricultural output is attributed to crop production. Sugarcane has the highest production percentage out of all the agricultural crops, amounting 17.408 million metric tons for the two quarters of 2020 [4]. From this thriving agricultural crop, 15.1 kg of dry trash per 100 kg stalks or harvested cane could be recovered for utilization as a bioenergy source [5]. A type of residue that is produced from the processing of sugarcane is sugarcane bagasse (SCB). It is the fibrous residue of sugarcane after the crushing and the extraction of juice. SCB is mainly composed of cell-soluble matter, cellulose, hemicellulose, lignin, ash, crude protein, and glucose [6].

Over the last two decades, the country has endeavored to improve the management and operation of the biomass residues through the implementation of several national laws such as the "Renewable Energy Act of 2008" (RA 9513). In the aspect of utilizing biomass residues to generate energy, SCB is directly burned to generate heat. Although direct combustion practically consumes the entirety of the biomass fuel and gives off energy, it is possible that the fuel is not utilized to its fullest potential due to certain factors such as high moisture content, irregular shape and sizes, and low bulk density. These factors pose difficulty for transportation, storage, handling and energy content [7]. Thus, a more efficient method would be to densify the biomass. Biomass densification is a means of increasing the energy density of biomass by mechanical or thermochemical means before feeding it for combustion [8]. Mechanical densification involves the application of pressure to densify the material. Briquetting and pelletization are examples of mechanical densification techniques. This is a relatively simple and cost-effective densification technique compared to thermochemical densification such as pyrolysis which is described as a relatively complex process that entails additional energy and cost [9]. Bales, pellets, and briquettes are products of mechanical densification whereas bio-oil and bio-char are products of thermochemical densification [9].

Briquetting is a mechanical treatment to compress loose biomass into a product with higher density, energy content and with less moisture [10]. Briquettes and their characteristics mainly depend on the feedstock, process conditions and equipment used. For instance, the HHV of the fuel is mainly dictated by the inherent calorific value of its biomass components. The compacting pressure affects mainly the briquette strength, density and consequently loss of moisture of the biomass fuel [13]. The moisture uptake and equilibrium moisture content of the fuel is highly dictated by the storage conditions (e.g. relative humidity, temperature) and feedstock characteristics. The particle size of the components also affects the compressive strength and density of briquettes [13].

Briquettes may also be enhanced with binders. Binders aid in the agglomeration of the biomass particles through adhesion and cohesion forces, attractive forces between solid particles, mechanical interlocking and solid bridge type mechanisms [14]. This results in improved mechanical strength of the briquette while also reducing the energy required for compression of biomass [15]. A promising binder to be used in solid fuel applications is lignin. Lignin is a biopolymer which comprises of lignocellulosic material. It is a waste product of industries that are based on cellulose, hemicelluloses, and their derivatives [16]. Such industries include pulp and paper, textile, and bioethanol production. Utilization of this by-product as a binder and fuel additive is attractive since it is capable of forming solid bridges between particles which increases the mechanical integrity of the densified biomass [17] while positively contributing to the fuel calorific value with increasing binder concentrations [18,19]. A study by Hu et al. [20] showed that bio-char pellets added with lignin as binder have increased compressive strength, decreased compression energy, and lowered ignition temperature. However, moisture uptake increased with increasing binder concentrations but still within acceptable limits. The improvement in these properties showed that lignin binders show good potential for utilization in biofuels.

The goal of this work was to improve biomass briquette quality with the addition of lignin from industrial black liquor. Despite the potential of lignin as a fuel additive and binder, studies in literature focusing on lignin-binder biofuels have been scarce. In order to address this gap, as well as the viability of using lignin-binder SCB briquettes for fuel applications, this study aimed to investigate effects of SCB:lignin ratio and compression pressure on the briquette quality (bulk density, higher heating value and compressive strength) and fuel characteristics (briquette proximate composition, combustion indices, burning rate and equilibrium moisture content). A 32 factorial design was
employed to evaluate the effect of SCB:lignin ratio ( ) and compaction pressure ( 5 MPa , ) at constant briquetting conditions of at 30-minutes pressing time.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Sample Acquisition and Preparation.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

SCB was sourced from BOMEDCO, a sugar-milling company in Bogo, Cebu. The collected samples were sun-dried then milled using a Wiley Mill (Thomas-Wiley Model 4, New Jersey, USA) equipped with a mesh aperture screen. The milled SCB were stored in polypropylene boxes for subsequent characterization and briquetting.

Lignin (LGN) was sourced from the waste degumming liquor of the fiber-processing plant of the Department of Science and Technology - Philippine Textile Research Institute (DOST-PTRI) in Taguig City. The precipitation procedure followed was based on the study by Alriols et al. [21]. One (1) liter of liquor was transferred to a 2-L glass beaker. The liquor was then acidified to pH 2 by slowly adding concentrated sulfuric acid. Brown precipitates of lignin are expected to appear during the pH adjustment. The resulting mixture was placed in a water bath until the precipitate settled at the bottom of the vessel and formed a big clump. The supernatant liquid was removed and the precipitate was filtered using a cheesecloth. The filter cake was then washed with warm water and the pH of the spent wash was measured using a pH meter after each washing. The washing step was stopped after the pH of the spent wash became constant (around pH 5.5 ). The washed solids were then dried in a vacuum tray dryer at and 300 mmHg until a constant weight reading was achieved in the dryer balance. The dried solids were then reduced in size with a 1000 W food processor at (BN1000W Gorenje) and stored in a polypropylene container for subsequent characterization and briquetting.

SCB and LGN Characterization.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The dried SCB was characterized in terms of the mean particle size, loose and tapped bulk density, proximate composition namely, moisture content (MC), volatile matter (VM), fixed carbon (FC) and ash, higher heating value (HHV), whereas LGN was characterized in terms of the proximate composition and HHV only.

SCB and LGN Characterization: Particle Size Distribution.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Pre-weighed standard Tyler sieves with sieve apertures of , and were stacked in descending order of mesh size. About 50 g of milled SCB was placed on top of the sieve having 2 mm aperture size and covered. The stack of sieve trays was then mounted to a sieve shaker (Mod. A5911, Interest Benelux, Netherlands), and was shaken for 15 minutes. After shaking, the mass of SCB retained on each of the sieve was determined [13]. The mean particle size was then calculated using Equation 1.

where is the mass of sampled retained on a particular sieve tray, is the average size of the mesh through which the SCB samples have passed through and on which it was retained, and as the total mass of the sample.

SCB and LGN Characterization: Loose and Tapped Density Determination.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The loose and tapped densities of the raw SCB were determined based on the standard method in ASTM D7481-09 [22]. A pre-weighed and pre-dried graduated cylinder was placed on an analytical balance and tared, SCB was poured into the cylinder until a set mass was obtained. The loaded cylinder was weighed to the nearest 0.1 mg . The loose density was determined using Equation 2.

where is the mass of the loaded graduated cylinder, and is the mass of the empty cylinder, and is the volume occupied by the loose biomass. For tapped bulk density , the cylinder was tapped for 500 , and 1250 times from a height of 3 mm onto a flat and hard surface to settle the loose biomass and their corresponding volume measurements were recorded. If the difference between the volume of the biomass tapped for 500 and 1250 times was less than 1 mL , the volume reading tapped at 1250 times will then serve as the tapped volume of the biomass. If the difference was greater than 1 mL , additional tapping (increments of 1250 taps) will be done until the difference of less than 1 mL was achieved. The value was then used as the tapped volume and was then used in Equation 3.

SCB and LGN Characterization: Proximate Analysis.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The standard test method for chemical analysis of wood charcoal by the American Society for Testing and Materials (ASTM) procedure D1762-84 [23] was used for proximate analysis. SCB, lignin, and SCB briquettes were characterized in terms of their proximate components, namely moisture, volatile matter, ash, and fixed carbon. The proximate analysis was carried out twice for each sample following the method described in NREL/TP-510-42622 [24] and ASTM D1762-84 [23]. To ensure that any residual materials adhering to the crucibles and their covers were volatilized, the crucibles and covers were pre-fired by heating them at for 1 hour, and cooling to room temperature, prior to the analysis. About 1 gram of sample (SCB, Lignin, or SCB briquettes) was weighed (to the nearest 0.0001 g ) into the pre-fired and pre-weighed crucibles. Moisture content was determined by placing the sample-containing crucibles in a convection oven (Memmert Oven, Model UN50) at for 2 hours then cooled in a desiccator for 30 minutes prior to weighing using an analytical balance (Kern ABJ-NM/ABS-N, Model ABS 220-4N). The drying procedure was repeated until a constant weight was obtained, that is, the change in the calculated moisture content is less than . The percent moisture content, MC (wt.%) was then calculated using Equation 4.

where is the mass of the sample before drying, is the mass of the dry crucible, and is the mass ( g ) of the crucible containing the sample, is the mass ( g ) of the crucible containing the sample after MC determination. Volatile matter content was then determined by covering the crucibles containing the samples obtained from moisture analysis with pre-fired crucible lids before placing in a furnace (PF3/SPEC, Vecstar, UK) set at . With the furnace door open, the crucibles were placed on the outer ledge of the furnace for 2 minutes, then moved to the edge of the furnace for 3 minutes. Afterwards, the crucibles were then transferred to the rear of the furnace with the furnace door closed. The crucibles were then taken out from the furnace after 6 minutes, allowed to cool for 15 minutes in a stainless-steel tray, then placed into the desiccator for 30 minutes before weighing. The percentage of volatile matter, VM (wt.%), was calculated using Equation 5. The lid was not included in the weighing.

where is the mass ( g ) of crucible containing the sample after heating at and MC is the fractional moisture content determined previously. Afterwards, the uncovered crucibles containing the sample was heated in a muffle furnace (PF3/SPEC, Vecstar, UK) at for 4 hours. After heating, the crucibles containing the residue were taken out of the furnace, allowed to cool for

15 minutes in a stainless-steel tray, then placed into the desiccator for 30 minutes before weighing. The percentage of ash, Ash (wt.%) was calculated using Equation 6.

where is the mass of the crucible containing the sample after heating at . The percentage of fixed carbon of the sample, FC (wt.% of fixed carbon in sample), was calculated by difference using Equation 7(7).

SCB and LGN Characterization: Higher Heating Value Determination.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The higher heating value of the pre-treated samples were determined using an adiabatic bomb calorimeter (Parr 1108 Oxygen Combustion Bomb) and procedures prescribed by the Parr Instruments operating manual [25, 26] which are compliant with ASTM D2015-96 [27]. About 0.71.1 g of sample, accurately measured to the nearest 0.1 mg , was used for every analysis. Nickelchromium fuse wire (Parr 45C10) was used to ignite the samples. About 10 cm of the wire was used for each run and the length of the wire after ignition was recorded for correction of the actual heat released by the consumed wire. Temperature readings were recorded prior to firing, at 30 -second intervals for 5 minutes after firing, then at 1 -minute intervals for the next 7 minutes. The correction for nitric acid formation was adjusted using standard sodium carbonate titration while there was no correction for sulfur [28]. The HHV was determined using Equation 8.

where is the calorimeter constant is the temperature change from before ignition to the temperature reading at the 12 th minute after ignition is the heat of formation of nitric acid ( kJ ) which is determined by the volume of titrant ( of titrant), is the change in wire length measured before and after ignition (cm), is the heat of combustion of the fuse wire ( for Parr 45C10), and is the mass of the sample or standard benzoic acid (grams) used in the ignition. Equation 9 was used to express HHV in drybasis.

Briquetting.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The mold used is an assembly of 3 parts namely the mold piston Fig. 1a, the mold cylinder Fig.1b, and the mold base Fig. 1c. The mold was designed to produced briquettes with dimensions compliant to the Austrian ONORM M7135 standard for briquettes.

Sugarcane bagasse and lignin binder mixtures of 25 grams with different ratios ( lignin, 4 lignin, lignin, and lignin) were prepared. Exact amounts of SCB and LGN for a specific ratio are illustrated in Table 1. A beaker was placed on a top loading balance and tared. SCB was poured first into the beaker until a specified mass was obtained. LGN was then added to the beaker until the total mass of the mixture reached 25 g . It was then mixed thoroughly using a spatula. The prepared mixtures were carefully poured into two of the six cylinders ( 42.5 mm inner diameter) using a funnel. The remaining cylinders were loaded with pure SCB ( ) to act as fillers to avoid mold damage. After each compartment was loaded with the mixtures, the piston was gently placed on the cylinders. The mold was placed on the base of the hydraulic press (Carver Hydraulic Press, Model C) preheated at (Figure 1e). The mold was centered well on the preheated base. Once the briquette mold was in place, the hydraulic jack was pumped to obtain the desired compaction pressure ( ). After the pressure was reached, the pressed biomass was held at
the desired pressure for 30 minutes. After 30 minutes, the release knob on the hydraulic press was turned counter clockwise to release the pressure. After releasing the pressure, the briquette mold was removed from the press and the briquettes were removed from the mold. The weight and the dimensions of the briquettes were measured directly after compaction and then stored in re-sealable polyethylene bags for subsequent characterization and testing.

Table 1. Formulations of briquettes and number of briquettes produced per mixture proportion.

Mixture proportions | Mass of lignin (g) | Mass of SCB (g) | No. of briquettes produced
0% LGN | 0 | 25 | 6
4% LGN | 1 | 24 | 6
8% LGN | 2 | 23 | 6
12% LGN | 3 | 22 | 8

In order to assess the briquette quality, European standards for the domestic use of biomass briquettes were employed. The Austrian ONORM M7135 and the EN 14961-3 were considered in this study. Although these standards are based on compressed wood and compressed bark, which may not be necessarily applicable for herbaceous biomass like SCB, they provide a means to classify the quality of the briquette [13]. Details of each standard are listed in Table 2.

Table 2. European standards for the domestic use of biomass briquettes [29].

Standard
Diameter/Length
(mm/mm)
Bulk
Density
(g/cm³)
Moisture
content
(wt.%)
Heating
Potential
(MJ/kg)
Ash content
(wt.%)
Sulfur
content
(wt.%)

Ö-NORM
M7135
(Austria)
40-100/up to 1000
≥1
≤10
≥18
≤0.7
≤0.08

EN 14961-3
(Europe)
>25-125/ up to 400
≥0.9
≤15
≥15.3
≤0.7
≤0.04

Fig. 1. Redrawn schematic diagram based on measurements used for fabrication.: (a) Mold piston isometric, top & front view; (b) 6-cylinder mold isometric, top, & front view; (c) mold base isometric, top, & front view; (d) briquette mold assembly isometric view; (e) Carver press.

Fig. 1. Redrawn schematic diagram based on measurements used for fabrication.: (a) Mold piston isometric, top & front view; (b) 6-cylinder mold isometric, top, & front view; (c) mold base isometric, top, & front view; (d) briquette mold assembly isometric view; (e) Carver press.

Briquetting: Briquette Density.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

After the briquetting process, the briquettes were weighed and corresponding dimensions were measured. The diameter and height of the briquettes were measured using a Vernier caliper. In measuring the diameter and the height of the briquette, 2 sides of the briquette were measured. The average of the two sides were then calculated and the average value was then used as the and the . The briquette density was calculated using Equation 10 .

where is the mass of briquette immediately after briquetting. The density ratio and degree of compaction are then determined to evaluate the briquetting performance.

Briquetting: Moisture Reabsorption.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The moisture reabsorption was determined using procedures prescribed by Conag et al. [28]. Briquette samples were oven dried at for 3 hours. The dried briquette sample was weighed and thus taken as the initial mass of the dried briquette. The briquette sample was exposed to atmospheric conditions for a test period of 15 days. Mass of the briquettes were recorded at fixed intervals ( 1 -hour intervals for the first 12 hours, then 12-hour intervals in the succeeding days). Weight (in grams) acquired by the briquette was taken as reabsorbed moisture. Wet bulb temperature, dry bulb temperature and relative humidity (RH) readings were also determined using a sling psychrometer at the same intervals as weight readings.

Briquetting: Normalized Burning Rate.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

An iron stand with a wire mesh was mounted on a receiving tray on top of a top loading balance. An alcohol lamp was positioned such that it does not contribute to the total system mass. Briquette samples of known weight were loaded on the wire mesh and the lamp ignited. The rate of mass loss of the briquette was monitored at 10 -second intervals using a stopwatch until no change in mass was observed. Ash was not removed for the entirety of the burning period. Readings were recorded as soon as the flame contacted the briquette.

Fig. 2. Normalized burning rate setup.

Fig. 2. Normalized burning rate setup.

The determination of the burning time was based on the work of Chaney et al. [30]. There are three phases of the burning process: Phase 1 is the ignition phase where no drastic change in mass occurs; phase 2 is the steady state flaming combustion phase where the fuel is enveloped in a luminous flame (refer to Fig. 2) and it starts with a sharp decrease in mass which continues to decrease over time and; phase 3 is when the flame dies and the briquette decomposes further by char combustion mechanism. Only phase 2 of the burning process was considered. Equation 13 was used to determine the normalized burning rate.

where and initial and final mass of briquette, respectively, and is the time elapsed during the steady state flaming combustion phase.

Briquetting: Compressive Strength.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The compressive strength of the briquette was measured using the Universal Testing Machine (UTM) WEW-600, Serial no. 195, following the procedures by Emerhi [31]. The briquette samples were mounted to the UTM and the compressive load was applied to the sample. The load was increased until the briquette started to fracture and the compressive load was recorded immediately. Equation 14 was used to calculate the compressive strength, CS.

where , the compressive load exerted the moment the briquette started to fracture in Newtons, which was specifically determined by using the stress-strain diagram, and A is the surface area of the briquette sample ( ). From the compressive load data , the number of stackable briquettes was calculated.

where is the mass of briquette, is the gravitational acceleration constant and the number of stackable briquettes.

Briquetting: Briquette Combustion Indices.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The proximate analysis and higher heating value of the briquetted samples are determined based on the procedures described in the previous subsections. The potential of the briquettes to be co-fired with conventional coal was assessed by obtaining the fuel ratio FR [32], combustibility index CI [33], and the volatile ignitability VI [34]. These were computed using Equations 17, 18, and 19.

Experimental Design and Statistical Analysis.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

In this study, the Minitab® 19 software [35] was used to map out the design of the experiment and subsequent statistical analysis. Three response variables namely: bulk density, higher heating value (HHV) and compressive strength (CS), are of interest in this work. A 32-full factorial experimental design was employed by varying two input factors: compaction pressure (CP) and lignin content (LC); with three levels each: low, intermediate, and high. The runs are done in replicates of two, giving a total of 18 replicates along with 6 replicates of binder-less briquettes used as control. The factors along with their corresponding levels are tabulated in Table 3.

Table 3. Factors and corresponding levels of the 32 -full factorial experimental design.

Level | Compaction Pressure (MPa) | Lignin Content (wt. %)
Low | 5 | 4%
Intermediate | 8 | 8%
High | 11 | 12%

An analysis of variance (ANOVA) on the gathered data was also done to identify the significance of the main and interaction effects.

Results and Discussions

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Raw Material Characterization.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Physical properties such as bulk density, particle size, and proximate composition of both SCB and lignin, as well as their higher heating values (HHV) are summarized in Table 4. The physical properties such as bulk density and particle size of the biomass are important considerations for transportation and storage purposes. Sugarcane bagasse (as received) was analyzed to have a moisture content of . The samples were sun-dried and milled passing through a mesh and physical properties such as bulk density and particle size were determined.

81.60 ± 0.93
59.6Kr;

17.84 ± 0.93
39.6 Kr ;

0.56 ± 0.00
0.8 Kr ;

22.6 ± 0.1
23.6 - 25.58
[19]

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

on dry basis; value for Kraft lignin; value for Klason lignin; value for Willstatter lignin
Lignin used by this study was extracted using an alkaline extraction process devised by DOSTPTRI and precipitated using an acid precipitation method by Alriols et al. [21]. This is different from the extraction processes used by the cited references in Table 4 which were obtained using Kraft, Klason, and Willstatter lignin extraction techniques. The main difference between Klason and Willstatter hydrolysis extraction methods is that the former uses dilute and the latter uses dilute HCl .

Raw Material Characterization: SCB Density and Particle Size.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Bulk densities of SCB as determined by this work is (loose) and (tapped), which is only slightly higher compared to literature with values of (loose) and (tapped) [34]. The loose and tapped density are used to characterize the flowability of particles. At large scale productions of briquettes, the ease of handling the raw materials would be a significant factor to efficiency, thus it is important to determine the loose and tapped densities.

The particle size of biomass is important in terms of storage and transportation since fuel with very small particle size could get easily blown by the wind thus needing additional storage equipment and more expensive equipment to transport. For briquetting, the particle size of the feedstock also has an effect on the quality of the produced briquette.

Fig. 3. SCB particle size distribution from sieve analysis.

Fig. 3. SCB particle size distribution from sieve analysis.

The milled SCB used for briquetting in this study has an average particle size of with a particle size distribution as shown in Fig.3. Particles with sizes ranging from 0.45 to 0.85 mm make up of the distribution. Generally, biomass particles of 6 to 8 mm in size give the best briquetting results. However, it is also desirable to have a random distribution of particle size to make sure that there is an adequate amount of small particles that is present for embedding in the larger particles [41]. The longer fiber lengths of the biomass promote mechanical interlocking between the particles increasing the strength of the resulting briquette [42]. The SCB used has a wide particle size distribution, with particles having sizes greater than 0.45 mm making up while those less than 0.45 mm making up 40.99%. The presence of particles with different sizes improves the packing dynamics and contributes to a high strength [43], making the SCB feedstock used suitable for producing strong briquettes.

Raw Material Characterization: Proximate Analysis and Higher Heating Value.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

After drying and milling, a separate sample of SCB was further characterized for its proximate components. For solid fuels, moisture is an unwanted component as it significantly lowers the heating value of the solid fuel and may result in incomplete combustion of the combustible matter, the deposition of unburnt carbon around the equipment and might cause a difficulty in ignition. Decreasing the moisture content of the biomass before briquetting is also important in complying with briquette standards. The moisture content of as-received SCB decreased from to after drying and milling, therefore is desirable since the chosen standards for briquette quality allows at most moisture. Also, a study by Grover & Mishra [41] observed that when a feedstock has 8-
moisture, the briquettes would be strong, free of cracks and the briquetting process would be smooth. Compared to other biomass used for densification listed in Table 5, moisture of SCB was within the range of values found in literature. Aside from moisture, the amount of volatile matter (VM) and fixed carbon (FC) significantly influences the heating value of the biomass. The VM and the FC represent the components of carbon, hydrogen and oxygen present in the biomass. VM volatilizes when heated, leaving FC as the remaining source of carbon in the biomass. Heating value is proportional to the carbon and hydrogen content of the biomass thus, HHV is assumed to be a function of FC and VM. Compared to other biomass, The FC of SCB falls within the range of values found in literature, however, the VM of SCB is relatively higher compared to literature. This explains why the HHV of SCB has a larger HHV value compared to the mentioned biomass, thus making it a desirable feedstock for solid fuels. Ash is a non-combustible component that lowers the heating value of the solid fuel as its amount ( ) increases. Also, the ash content of a biomass is an indicator of slagging behavior of biomass. Generally, the greater the ash content, the greater the slagging behavior. In Table 5, SCB has the lowest ash content compared to the other biomass. [18, 19, 37, 44, 45].

In this study, SCB and lignin were found to have heating values of and , respectively. Both materials are found to have HHV values close to their respective literature values in Table 4 [19, 28, 37]. The difference between the raw material HHVs can be traced back to the difference in their respective proximate components. The VM of SCB is only slightly higher compared to that of the lignin samples, however, there is a large difference between their FC and ash contents as observed in Table 4. The FC of SCB is lower than that of the FC of lignin and the ash content of SCB is higher compared to that of the SCB samples. This explains the higher HHV of lignin compared to SCB where it coincides with a study done by Cordero et al. [46] that VM, ash and FC content has increasing significance to the HHV value of lignocellulosic and carbonaceous materials. Lignin contains very low ash content, low MC , and high values of FC and MC , thus making it a desirable feedstock for biomass fuels. In addition, it has good binding properties making it a very suitable fuel additive and binder.

Table 5. Proximate compositions and higher heating values of common feedstocks used in densification.

Biomass Feedstock | Proximate Composition (%wt.) | HHV (MJ/kg) | Reference
| Moisture | Ash | FC | VM |  |
Sugarcane bagasse | 8.14 ± 0.10 | 2.26 ± 0.11a | 14.65 ± 0.90a | 83.10 ± 0.24a | 18.19 ± 0.30a | This study
Rice bran | 11.40 ± 0.08 | 16.17 ± 0.71 | 17.14 ± 0.19 | 66.69 ± 0.64 | 17.18 ± 0.14 | [13]
Rice husk | - | 16.22 | 16.08 | 67.70 | 15.44a | [20]
Rice straw and Sugarcane leavesb | 4.2 – 6.2 | 7.84 – 12.85 | 9.06 – 13.63 | 68.18 – 74.67 | 16.33 – 17.83 | [47]
Corn cob | 9.71 | 2.97 | 71.21 | 16.11 | 16.65 | [48]
Pigeon pea stalk | 5.96 | 10.62a | 7.25a | 83.13a | 14.81a | [49]
Cotton stalk | 10.2 | 12.13a | 14.00a | 73.87a | 15.72a | [49]
Soy stalk | 11.84 | 10.39a | 7.22a | 82.39a | 14.66a | [49]

on dry basis range of compositions from pure rice straw to pure sugarcane leaves.

Briquette Characterization: Proximate Analysis.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Compared to as received loose SCB ( ), and dried SCB samples ( , majority of the briquettes had lower moisture contents ranging from to . This is due to water evaporating from the heating of the mold at for 30 minutes during the briquetting process. All the briquettes pass the moisture content standard for Ö-NORM M7135 ( ) and EN 14961-3 ( ). The other incombustible component of the solid fuel briquette is the ash content. Ash content from the briquettes produced range from which are higher than the
prescribed ash content by the above-mentioned standards ( ). However, the ash content of the briquettes produced, which have an average ash content of , are relatively lower compared to other common biomass fuels ( , average of ) [51].

Fig. 4. Proximate composition of solid fuel briquettes with varying compaction pressure ( ) and binder content ( ).

Fig. 4. Proximate composition of solid fuel briquettes with varying compaction pressure ( ) and binder content ( ).

Fig. 4 shows the proximate composition of all the briquettes studies. There is no significant change in the proximate composition of the briquettes with the addition of lignin. This may be due to the fact that the SCB and lignin have similar compositions (refer to Table 4). The values of VM and FC range from and , respectively. The amounts of VM and FC are responsible for the heating value of the solid fuel. However, it is not enough to determine the total amount of VM and FC that the solid fuel has but it is also rather important to calculate the ratio of FC and VM, also called the fuel ratio, a combustion characteristic of a solid fuel. Further discussions on the combustion characteristics and other implications of the proximate composition are discussed in the previous subsections.

Briquette Characterization: Density.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The density of the briquette is an important property to consider when producing briquettes, it mainly affects the storage requirements of these solid biofuels. Briquettes with high density are desirable since they take up less space when stored and they are more energy dense compared to the loose biomass [7, 49]. Standards for briquettes require their densities to be greater than at the minimum, these are based on European Standards EN 14961-3. Fig. 5 shows the densities of the briquettes at different lignin contents and compaction pressures.

Fig. 5. Briquette density at various binder contents and compaction pressures. Error bars represent standard deviations for two replicates ( ).

Fig. 5. Briquette density at various binder contents and compaction pressures. Error bars represent standard deviations for two replicates ( ).

By doing an analysis of variance (ANOVA) with a confidence interval, only the compaction pressure has a significant effect on the density ( ) while lignin content has no significant effect on the density ( ). Effects of interactions between the two factors are also not observed. Increasing the pressure from 5 to 8 MPa for briquettes with binder content increases from 0.843 to increase while further increasing the pressure to 11 MPa brings the density up to ( increase). Higher pressures exert more force onto the biomass, forcing the particles to fill the void spaces within the briquette and thereby increasing its density [13]. As shown in Fig. 5, only briquettes made with compaction pressures of 8 and 11 MPa meet the standard ( ) while those made using a pressure of 5 MPa fall short of the standard with densities of around . Compared to the loose biomass, which has a density of (loose), the densities of the briquettes range from to . The SCB was compressed by about 85 to with densities of about 7 to 8 times the density of the raw biomass.

Briquette Characterization: Higher Heating Value.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The HHV determines the energy the biomass gives off when burned which means a high HHV is desirable for briquettes and fuels in general. Fig. 6 shows the HHVs of the briquettes. From the graph, it can be observed that, for the ranges chosen in the experiment, HHV increases as the lignin content is increased while increasing compaction pressure does not have a significant effect on the HHV between briquettes of the same lignin content. For instance, increasing the lignin content from to for a briquette with a compaction pressure of 8 MPa increases the HHV from to 19.4 ( increase) while increasing the compaction pressure from 5 MPa to 11 MPa for a briquette with 4% lignin content does not affect the HHV much ( to ).

Fig. 6. Higher heating value of briquettes at various lignin contents and compaction pressures. Error bars represent standard deviations for replicates of two ( ).

Fig. 6. Higher heating value of briquettes at various lignin contents and compaction pressures. Error bars represent standard deviations for replicates of two ( ).

By analysis of variance at a confidence level of , only the lignin content significantly affects the HHV ( ) while the effects of the compaction pressure are not found to be statistically significant to the HHV of the briquette ( ). Effects of interactions between the two factors are not observed. Increasing the lignin content would logically increase the HHV since the heating value of the lignin binder is higher than the raw SCB . On the other hand, the compaction pressure would not directly affect the HHV other than the leaching out of the extractives within the biomass [13]. Several studies have shown that the heating value of biomass is proportional to its lignin content [18, 52,53] since the heating value of lignin is higher than the other two components of lignocellulosic biomass (cellulose and hemicellulose). The higher heating value of lignin compared to the other components is due to its higher carbon content [50], thereby the addition of lignin to the SCB before briquetting would contribute to an increase in the overall HHV of the mixture. Generally, all of the briquettes produced have HHV values that fall between the HHV of raw SCB ( dry basis) and the HHV of pure lignin ( dry basis), which is to be expected. The European standard (EN 14961-3) for non-industrial wood briquettes requires the HHV to be a minimum of wet basis [54], briquettes produced in this work have a minimum HHV of wet basis and can go up to wet basis which is above the standard, given that it was set for wood briquettes.

Briquette Characterization: Compressive Strength.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The compressive strength is an important metric of mechanical strength in order to estimate the tolerable load a briquette can withstand [11]. In this regard, no standard minimum strength has been determined for commercialized briquettes. However, briquettes with high compressive strength are desirable.

Fig. 7. Compressive strength of briquettes at various lignin contents and compaction pressures. Error bars represent standard deviations for replicates of two ( ).

Fig. 7. Compressive strength of briquettes at various lignin contents and compaction pressures. Error bars represent standard deviations for replicates of two ( ).

Fig. 7 shows the compressive strength of briquettes at different binder contents and compaction pressure. No apparent trend was observed over all the data, however there is a positive trend observed when lignin content is increased for briquettes compressed at 11 MPa . The highest compressive strength obtained was also at by a briquette with lignin content and compressed at 11 MPa . By analysis of variance, both of the factors, compaction pressure and lignin content, do not have significant effects on the compressive strength at a confidence level of ( p > 0.05 ). Effects of interactions between the factors were not observed. The results in this work are contrary to that of a previous study with a similar range of pressures [13] and even to another study with a wider range of pressures [55], both of which prove that the compaction pressure significantly affects the briquette strength. Ideally, briquette strength increases as the compaction pressure increases [56], but this may have been affected by the compacting procedure, especially since an external binder such as lignin has been added, which affects the layers distribution in the briquette and its strength [56]. Some studies [57-59] have theorized that the softening of lignin by high temperatures and its subsequent cooling while under pressure is a key factor in improving the briquette strength with lignin. The briquetting procedure employed in this study, which is mainly based on the work of Navalta et al. [13], does not include a cooling step in the total compression time. Lignin is an amorphous thermoplastic material which undergoes plastic deformation at its glass transition temperature range and low compaction pressures [59], at this point the lignin fills the gaps between particles. Cooling under pressure allows the softened lignin to solidify and form stronger solid bridges between the particles in the briquette while preventing it from going back into its relaxed state [57]. In future works, it may be advisable to include a cooling step in the procedure to see if there is any effect on the strength.

The range used in the study for lignin concentrations may have been too low for it to exhibit any significant effect on the strength. However, some studies which also use lignin as an additive in low pressure briquetting/pelletizing makes use of a similar range of lignin concentrations [50,60,61] and shows a clear positive effect in the improvement of briquette strength. The main difference between this study and the cited studies is that the other studies make use of wood biomass in their
briquettes/pellets while the biomass used in this study is an herbaceous biomass. Studies on nonwoody biomass briquettes with lignin as an additive are scarce. A recent study by Chang and Byung [62] on non-woody biomass pellets made from an energy crop, Miscanthus sacchariflorus, with Kraft lignin as an additive had a similar observation with this work. The study concluded that the addition of lignin additive up to showed only a little to no increase in the compressive strength of the pellet which the study attributed to the weak bonding between the lignin additive and the biomass. The studies mentioned, however, did not statistically determine the significance of the effect of lignin as an additive in briquettes/pellets.

Nevertheless, the briquette with the lowest compressive strength ( lignin, 5 MPa ) obtained in this work can accommodate up to stacked 25 -g briquettes (diameter ), which is more than sufficient for practical transport and storage of briquettes. In a previous study by Navalta et al. [13], at least of briquettes could be stacked even with the lowest compressive strength achieved, also noting that the quantity of stackable briquettes is more than enough for practical transport and storage.

Fuel Characteristics of the Best Briquette.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

As a result of the investigation performed, the conditions that produced the best briquette with the highest HHV and CS obtained in this study were found to be at a formulation with lignin content, pressed at a compaction pressure of 11 MPa . Further characterization was performed on the best briquette obtained. The additional briquette characterization steps are summarized in Table 6, and results are then compared across various briquetting systems found in literature.

Fuel Characteristics of the Best Briquette: Moisture Reabsorption Rate and Equilibrium Moisture Content.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Since high moisture content is unwanted in solid fuels, one way to assess a solid fuel quality is by determining the moisture uptake rate and the equilibrium moisture content the fuel can hold at normal conditions. To determine these, a control briquette ( Lignin, 11 MPa ) and the best briquette was exposed to an environment with temperature ranges of and , and relative humidity (RH) of .

Fig. 8. Equilibrium moisture content (a) and moisture reabsorption rate (b) of binderless briquettes compressed at the different compaction pressures and the optimum briquette ( 11 MPa ). Error bars represent standard deviations for replicates of two ( ).

Fig. 8. Equilibrium moisture content (a) and moisture reabsorption rate (b) of binderless briquettes compressed at the different compaction pressures and the optimum briquette ( 11 MPa ). Error bars represent standard deviations for replicates of two ( ).

As presented in Fig. 8, the equilibrium moisture of the best briquette ( ) is found to be higher than that of binder-less briquettes compressed at and . It was also observed that the addition of a binder (lignin) increased the equilibrium moisture. This is due to the presence of oxygen-containing functional groups of lignin. Comparing the briquette with lignin content compressed at Lignin with the binder-less briquette compressed at 11 MPa , the briquette with lignin had a larger equilibrium moisture content by . In a similar study conducted by Hu et al. [63], it was observed that increasing the binder (lignin) content of the rice husk bio-char pellets from 5% to 20% increased the equilibrium moisture content from 4.69% to .

The control briquette exhibits a moisture reabsorption rate per hour, while the rate of moisture reabsorption is much slower for the briquettes densified at the determined best conditions ( ) at per hour. In the study by Hu et al. [63], lignin binder, rice husk bio-char pellets yielded reabsorption rates of per hour. Compared to values obtained from literature, the produced SCB briquettes have higher values of equilibrium moisture, but lower values of rate of moisture reabsorption. This implies that the briquettes could be stored for a longer time compared to binderless briquettes since the rate of moisture uptake is slow. However, long storage periods would cause swelling and disintegration due to a higher equilibrium MC content compared to the control briquettes. Nevertheless, the equilibrium moisture of the best briquettes still complies with the chosen standards.

Fuel Characteristics of the Best Briquette: Burning Rate.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The most straightforward technique of unlocking the energy potential in biomass feedstock is through combustion. Thus, investigating the solid fuel burning characteristics is essential in trying to describe and understand the behavior of the fuel when subjected to combustion. In this study, the burning characteristics are mainly gauged by the burning rate and the combustion indices.

The burning rate (BR) of the best briquette is in the range of , which translates to a normalized burning rate (NBR) of mass loss/min, with typically short ignition times ranging from 0.17-0.50 minutes and burning times ranging from 12.33-14.83 minutes. A study by Navalta et al. on bagasse-rice bran briquettes yielded similar results with an NBR of mass loss/ min, ignition times ranging from 0.17-1.83 minutes and burning time of 3.67-10.67 minutes [13]. Comparing to different briquettes in literature, starch binder empty fruit bunches briquette (EFB) had a BR of and ignition time of 6 minutes with ignition aid [65], and asphalt binder EFB briquettes with BR of and ignition time of 13 minutes with ignition aid [65]. The briquettes in this study have lower burning rates and shorter ignition times. However, burning time of coal-biomass briquette systems prove to be longer, spanning from 60-100 minutes for maize cob-coal and groundnut shell-coal briquettes [66]. This implies that the briquettes produced are consumed faster compared to coal-based briquettes and large amounts of fuel are required to sustain burning. This poses a disadvantage in industrial fuels. However, the ease of ignition is a convenient feature for fuels targeted for domestic/household usage.

Fuel Characteristics of the Best Briquette: Combustion Indices.

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

For the briquette combustion indices, the fuel ratio (FR), combustibility index (CI), and volatile ignitability (VI) are considered in this work. The rationale behind obtaining these indices revolves around the idea that the volatile matter content in the fuel is used as a basis to determine combustibility and eventually select better solid fuel alternatives for boiler applications [32] and ultimately, these indices give an idea as to the potential for industrial applicability of the produced solid fuel briquettes.

In this study, the best briquette bears an FR value of . FR ranging from are commonly used in coal-fired power plants [34]. In thermoelectric power plants, the FR of subbituminous coal is in the range of 0.5-1.0 [33]. The briquettes produced do not meet the FR values to qualify for usage in power plants. This implies that the solid fuel contains high quantities of volatile matter and that it leads to challenges in energy recovery due to rapid combustion leading to poor boiler efficiencies and unwanted gaseous emissions [13,67]. This is true for solid fuels with low FR. However, mechanical densification techniques such as briquetting can be used in tandem with other densification techniques in order to improve FR and other combustion indices [13]. An example would be torrefaction, a type of chemical densification technique. In a study by Conag et al. [34], torrefied SCB were determined to have an FR ranging from 0.22-1.68. The VI, which is a gauge for the energy imparted by the total volatile matter, must be at least . For the best briquette produced in this study, the VI value is at . This indicates that the briquettes have sufficient volatile matter for ease of ignition, as supported by the FR value, proximate analysis, and the short ignition time observed in burning rate experiments. This is further supported by the fact that biomass is considered as a more reactive fuel than coal, giving a much faster combustion rate during the devolatilization phase [45]. The CI can be used to evaluate the suitability of the material for mixed combustion in coal-fired thermal power plants [33]. The CI appropriate for co-firing must be in the range of [33]. The best briquette yielded a CI value of . The CI values of the briquette is comparable to those of raw biomass feedstock. In a study of Conag et al., unprocessed SCB had a CI of . On the other hand, torrefaction of SCB significantly reduced the CI to an acceptable value of , noting that torrefaction at provided the best results [34]. Ultimately, it is obvious that the best briquette produced in this work is not fit for co-firing in industrial boilers.

Table 6. Fuel characteristics of various densified solid fuels.

Solid Fuel | Compaction rate (MPa/mm) | Compaction pressure (MPa) | Compaction temperature (°C) | Biomass particle size (mm) | Binder Content /Ratio | Briquette diameter (mm) | Compressive strength (MPa) | Equilibrium Moisture (wt. %) | Moisture reabsorption rate (wt. % per h) | Burning rate (g/min) | PR | VI (MJ/kg) | CI (MJ/kg)
Lignin-binder, SCB briquettes (12% Lignin - 11 MPa) | 0.37 | 11 | 150 | 0.61 ± 0.01 | 12 | 43.30 ± 0.04 | 113.13 ± 2.81 | 8.0189 ± 0.1473 | 0.0611 ± 0.0017 | 1.30-1.58 | 0.2040 | 15.91-89 | 10.4-19.88
SCB briquettes (0% Lignin - 11 MPa) | 0.37 | 11 | 150 | 0.61 ± 0.01 | 0 | 43.38 ± 0.11 | 95.39 ± 3.36 | 6.8308 ± 0.3381 | 0.1660 ± 0.0106 | 1.56-1.63 | 0.1700 | 14.71-59 | 11.4-6898
RB -SCB briquettes [13] | 0.27 | 8 - 12 | 150 | 0.22 ± 0.00; 0.54 ±0.01 | 0 | 42.5 | 50.21 ± 122.37 | 4.2065-6.4919 | 0.3840-1.0800 | 1.21-1.80 | 0.20-0.25 | 14.1-14.7 | 59.1-87.6
Mustard stalk briquettes [64] |  | 112.34 |  | 0.352 | 5 - 20 | 41.5 - 43.5 | 6.52 | 3.56a-8.50b |  |  |  |  |
Mixed waste (rice leaves/grasses, briquettes) [64] |  | 123.42-112.34 |  | 0.378 | 5 - 20 | 41.5 - 43.5 | 6.54 | 3.09a-4.85b |  |  |  |  |
Saw mill wood waste briquettes [64] |  | 123.42-112.34 |  | 0.561 | 5 - 20 | 41.5 - 43.5 | 5.43 | 2.49a-4.39b |  |  |  |  |
Lignin-binder, rice husk bio-char pellets [63] |  | 123.42 |  |  | 5 - 20 | 20 |  | 4.69-6.48 | 0.2345-0.3240* |  | 3.6470-5.5086 | 13.33-87-17.99-04 | 3.31-22-5.00-48
Starch-binder, EFB briquette [65] |  | 95.54 |  |  |  |  |  | 6.01 |  | 4.35 |  |  |
Asphalt-binder, EFB briquette [65] |  |  |  |  |  |  |  | 10 |  | 3.7 |  |  |

Conclusions and Recommendations

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The effects of SCB:lignin ratio and compaction pressure on the quality and fuel characteristics of briquettes were investigated in this work. SCB briquettes were found to have improved bulk density (7 to 8-fold increase), high compressive strength and retarded moisture uptake. Compaction pressure is shown to have a significant effect on the briquette density ( ). On the other hand, both compaction pressure and lignin content do not exhibit significant effects on the compressive strength . While the addition of lignin binder lowers the moisture reabsorption rate, the opposite is true for the equilibrium moisture content.

As for the fuel characteristics, the HHV of briquettes are shown to increase with increasing lignin content ( ). Proximate analysis reveals that the briquette and feedstock compositions are identical, showing high levels of volatile matter. Thus, the briquettes exhibit relatively short burning times compared to coal and coal-biomass briquettes. Combustion indices determine that SCB briquettes are not suitable for industrial applications.

To further improve the study, it is recommended that other sources of lignin be explored, and lignin binder range explored in this study could be expanded (> 12%) to better understand its effects on the compressive strength and other briquette characteristics. Process conditions during briquetting could also be altered such as the pressing temperature, time, and pressure to further optimize the briquetting process. Furthermore, various biomass densification techniques can also be explored, such as a combination of briquetting (mechanical densification) and torrefaction (thermochemical densification) techniques.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

The authors would like to acknowledge the support provided by the Bogo-Medellin Milling Company for the allowing us to source sugarcane bagasse from their facility as raw material for this research and the Department of Science and Technology - Philippine Textile Research Institute for the provision of waste degumming liquor and for allowing us to use their research facility for the acquisition of lignin which was used as a raw material for our study. And lastly, to the University of San Carlos - Research Development Extension and Publications Office (RDEPO) for the grant provided to I.D.F Tabañag.

Appendix A: Research Design

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Experimental Flow Diagram of This Study

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

Experimental Design Diagram of This Study

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

References

The original version of this paper is available on https://www.scientific.net/EI.16.11.pdf

British Petroleum Company, BP statistical review of world energy. British Petroleum Company, 2018.

J. Orloff, "The Definition of Bioenergy," 2018.

D. J. Tenenbaum, "Food vs. fuel: diversion of crops could cause more hunger." National Institute of Environmental Health Sciences, 2008.

"Performance of Philippine Agriculture January-March 2018."

A. W. Go and A. T. Conag, "Utilizing sugarcane leaves/straws as source of bioenergy in the Philippines: A case in the Visayas Region," Renew. Energy, 2018.

Y. R. Loh, D. Sujan, M. E. Rahman, and C. A. Das, "Review Sugarcane bagasse - The future composite material: A literature review," Resour. Conserv. Recycl., vol. 75, pp. 14-22, 2013.

J. S. Tumuluru, C. T. Wright, J. R. Hess, and K. L. Kenney, "A review of biomass densification systems to develop uniform feedstock commodities for bioenergy application," Biofuels, Bioproducts and Biorefining, vol. 5, no. 6. pp. 683-707, 2011.

C. Karunanithy, Y. Wang, K. Muthukumarappan, and S. Pugalendhi, "Physiochemical Characterization of Briquettes Made from Different Feedstocks," Biotechnol. Res. Int., vol. 2012, pp. 1-12, 2012.

A. Bauen and M. Londo, "Bioenergy - a Sustainable and Reliable Energy Source," no. April 2015, pp. 1-108, 2012.

P. K. Toan, N. D. Cuong, N. T. Quy, and P. K. Sinh, "Application of Briquetting Technology To Produce Briquettes from Agricultural Residues and By-products," pp. 1416-1419, 2000.

M. Ibrahim, U. O. Aroke, and A. Abdulrasheed, "Investigation of Mechanical & Combustible Properties of Briquettes Produced From Rice Husk Using Polystyrene Foam Adhesive As a," vol. 3, no. 2, 2015.

S. V. Bhagwanrao, M. Singaravelu, and others, "Bulk density of biomass and particle density of their briquettes.," Int. J. Agric. Eng., vol. 7, no. 1, pp. 221-224, 2014.

C. J. L. G. Navalta, K. G. C. Banaag, V. A. O. Raboy, A. W. Go, and L. K. Cabatingan, "Solid Fuel from co-briquetting of Sugarcane Bagasse and Rice Bran Solid Fuel," 2018.

M. Rahman, K. Khan, M. N. Ali Moral, and A. N. M. M. Rahman, "Biomass Briquetting Technology and Environmental Pollution." 2003.

N. H. Azim, A. Subki, and Z. N. B. Yusof, "Abiotic stresses induce total phenolic, total flavonoid and antioxidant properties in Malaysian indigenous microalgae and cyanobacterium," Malays. J. Microbiol., vol. 14, no. 1, pp. 25-33, 2018.

C. Holley, Binder and Binder Systems for Agglomeration, vol. 2. Madison: The Institute for Briquetting and Agglomeration, 1983.

N. Kaliyan and R. V. Morey, "Densification Characteristics of Corn Stover and Switchgrass," vol. 0300, no. 06, 2006.

A. Demirbaş, "Relationships between lignin contents and heating values of biomass," Energy Convers. Manag., vol. 42, no. 2, pp. 183-188, 2001.

A. Demirbas, "Relationships between Heating Value and Lignin, Moisture, Ash and Extractive Contents of Biomass Fuels," Energy Explor. Exploit., vol. 20, no. 1, pp. 105-111, 2002.

Q. Hu, J. Shao, H. Yang, D. Yao, X. Wang, and H. Chen, "Effects of binders on the properties of bio-char pellets," Appl. Energy, vol. 157, no. June 2015, pp. 508-516, 2015.

A. García, M. G. Alriols, R. Llano-Ponte, and J. Labidi, "Energy and economic assessment of soda and organosolv biorefinery processes," Biomass and Bioenergy, vol. 35, no. 1, pp. 516525, 2011.

ASTM, "ASTM D7481-09 Standard test methods for determining loose and tapped bulk densities of powders using a graduated cylinder," Annu. B. ASTM Stand., no. October, p. American Society for Testing and Materials, West C, 2009.

ASTM, "Standard Test Method for Chemical Analysis of Wood Charcoal," ASTM Int., vol. 84, no. 2, pp. 1-2, 2011.

A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter, and D. Templeton, "Determination of ash in biomass," Cole Boulevard, Golden, Colorado, 2008.

Parr Instrument Company, "Oxygen Bomb Calorimeter," Combustion, no. 204, 2008.

Parr, "Operating Instructions for the 1108 Oxygen Combustion Bomb," no. 205, pp. 1-12, 2007.

American Society for Testing and Materials, "ASTM D2015: Standard Test Method for Gross Calorific Value of Solid Fuel by the Adiabatic Bomb Calorimeter," US Gov., vol. 552, no. 5, p. 5, 1996.

A. T. Conag, J. E. R. Villahermosa, L. K. Cabatingan, and A. W. Go, "Energy densification of sugarcane leaves through torrefaction under minimized oxidative atmosphere," Energy Sustain. Dev., vol. 42, no. 6, pp. 160-169, 2018.

P. Krizan, The Densification Process of Wood Waste. 2015.

J. O. Chaney, M. J. Clifford, and R. Wilson, "An experimental study of the combustion characteristics of low-density biomass briquettes," in Proceedings of 3rd International Conference of Appropriate Technology, Kigali, Rwanda, 2008, pp. 12-15.

E. A. Emerhi, "Physical and combustion properties of briquettes produced from sawdust of three hardwood species and different organic binders," Adv. Appl. Sci. Res., vol. 2, no. 6, pp. 236-246, 2011.

N. Oka, S. Shinozaki, M. Shibaoka, C. G. Thomas, C. Division, and P. O. B. N. Ryde, "The Influence of Rank and Maceral Composition on Ignition and Char Burnout of Pulverized Coal 1 . Energy R & D Labo ., Energy Development Dept ., IOI ~| ITSU KOSAN CO ., LTD . Investigation of combustion characteristics for different ranks of coal was con," vol. 15, pp. 213-224, 1987.

T. I. Ohm, J. S. Chae, J. K. Kim, and S. C. Oh, "Study on the characteristics of biomass for cocombustion in coal power plant," J. Mater. Cycles Waste Manag., vol. 17, no. 2, pp. 249-257, 2015.

A. T. Conag, J. E. R. Villahermosa, L. K. Cabatingan, and A. W. Go, "Energy densification of sugarcane bagasse through torrefaction under minimized oxidative atmosphere," J. Environ. Chem. Eng., vol. 5, no. 6, pp. 5411-5419, 2017.

"Minitab LLC © 2019, Minitab Release 19.1.0: Statistical Software for Windows 2010." 2019.

B. Dass and P. Jha, "Biomass characterization for various thermo-chemical applications," Int. J. Curr. Eng. Sci. Res., vol. 2, no. 2, pp. 59-63, 2015.

J. Parikh, S. A. Channiwala, and G. K. Ghosal, "A correlation for calculating HHV from proximate analysis of solid fuels," Fuel, vol. 84, no. 5, pp. 487-494, 2005.

E. A. Mousa, H. M. Ahmed, and C. Wang, "Novel Approach Towards Biomass Lignin Utilization in Ironmaking Blast Furnace," ISIJ Int., vol. 57, no. 10, pp. 1788-1796, 2017.

D. J. Horst, J. J. Ramírez Behainne, P. P. de Andrade Júnior, and J. L. Kovaleski, "An experimental comparison of lignin yield from the Klason and Willstatter extraction methods," Energy Sustain. Dev., vol. 23, no. 1, pp. 78-84, 2014.

Y. H. Leang and H. Y. Saw, "Proximate and functional properties of sugarcane bagasse," Agro Food Ind. Hitech, vol. 22, no. 2, Suppl. S, pp. 5-8, 2011.

P. D. Grover and S. K. Mishra, "Biomass Briquetting: Technology and Practices. Regional wood energy development programme in Asia GCP/RAS/154/NET, Field document No. 46," Reg. Wood Energy Dev. Program. Asia, no. 46, pp. 1-48, 1996.

L. Tabil, P. Adapa, and M. Kashaninejad, "Biomass Feedstock Pre-Processing - Part 2: Densification," Biofuel's Eng. Process Technol., pp. 439-464, 2011.

C. Ludwig, "How and why solids agglomerate," Chem. Eng., no. January, pp. 156-160, 1994.

V. Panwar, B. Prasad, and K. L. Wasewar, "Biomass Residue Briquetting and Characterization," J. Energy Eng., vol. 137, no. 2, pp. 108-114, 2011.

A. Tamilvanan, "Preparation of Biomass Briquettes using Various Agro- Residues and Waste Papers," J. Biofuels, vol. 4, no. 2, p. 47, 2013.

T. Cordero, F. Marquez, J. Rodriguez-Mirasol, and J. Rodriguez, "Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis," Fuel, vol. 80, no. 11, pp. 1567-1571, 2001.

P. Jittabut, Physical and Thermal Properties of Briquette Fuels from Rice Straw and Sugarcane Leaves by Mixing Molasses, vol. 79, no. x. Elsevier B.V., 2015.

H. Jin, Y. Lu, L. Guo, C. Cao, and X. Zhang, "Hydrogen production by partial oxidative gasification of biomass and its model compounds in supercritical water," Int. J. Hydrogen Energy, vol. 35, no. 7, pp. 3001-3010, 2010.

R. Mythili and P. Venkatachalam, "Briquetting of agro-residues," J. Sci. Ind. Res. (India)., vol. 72, no. 1, pp. 58-61, 2013.

W. T. N. Boschetti, A. M. M. L. Carvalho, A. de C. O. Carneiro, L. C. Santos, and L. de B. Q. Poyares, "Potential of kraft lignin as an additive in briquette production," 2019.

A. Demirbas, "Potential applications of renewable energy sources, biomass combustion problems in boiler power systems and combustion related environmental issues," Prog. Energy Combust. Sci., vol. 31, no. 2, pp. 171-192, 2005.

D. A. da Silva, V. C. Almeida, L. C. Viana, U. Klock, and G. I. B. De Muñiz, "Evaluation of the Energy-related Properties of Tropical Wood Waste Using NIR Spectroscopy," vol. 21, no. 4, pp. 561-568, 2014.

A. Oliveira, A. Carneiro, B. Vital, W. Almeida, B. Pereira, and M. Cardoso, "Quality parameters of Eucalyptus pellita F. Muell. Wood and charcoal," Sci. For. Sci., vol. 38, pp. 431439, 2010.

Deutsches Pelletinstitut, "Manual for the certification of wood briquettes for the end-user market," ENplus Briquettes, no. January, 2013.

S. J. Mitchual, K. Frimpong-Mensah, and N. A. Darkwa, "Effect of species, particle size and compacting pressure on relaxed density and compressive strength of fuel briquettes," Int. J. Energy Environ. Eng., vol. 4, no. 1, pp. 1-6, 2013.

J. Kers et al., "Determination of physical, mechanical and burning characteristics of polymeric waste material briquettes," Est. J. Eng., vol. 16, no. 4, p. 307, 2010.

S. Eriksson and M. Prior, "The briquetting of agricultural wastes for fuel.," FAO environment and energy paper, no. No. 11. Food and Agriculture Organization of the United Nations, Rome, p. 131 pp., 1990.

T. Hardianto, F. F. Pambudi, and A. R. Irhamna, "A study on lignin characteristic as internal binder in hot briquetting process of organic municipal solid waste," vol. 030013, no. July, 2018.

N. Kaliyan and R. V. Morey, "Natural binders and solid bridge type binding mechanisms in briquettes and pellets made from corn stover and switchgrass," Bioresour. Technol., vol. 101, no. 3, pp. 1082-1090, 2010.

W. T. N. Boschetti, A. do C. P. Lopes, R. A. Ribeiro, R. Q. Reyes, and A. de C. O. Carneiro, "Kraft lignin as an additive in pine and eucalyptus particle composition for briquette production," vol. 43, no. 2, pp. 1-8, 2019.

B. L. C. Pereira, A. de C. O. Carneiro, A. M. M. L. Carvalho, B. R. Vital, A. C. Oliveira, and W. D. Canal, "Influence of adding kraft lignin in eucalyptus pellets properties.," Floresta, vol. 46, no. 2, pp. 235-242, 2016.

H. M. Chang and H. U. Byung, "Effect of Process Parameters and Kraft Lignin Additive on The Mechanical Properties of Miscanthus Pellets," vol. 45, no. 6, pp. 0-2, 2017.

Q. Hu, J. Shao, H. Yang, D. Yao, X. Wang, and H. Chen, "Effects of binders on the properties of bio-char pellets," Appl. Energy, vol. 157, no. January, pp. 508-516, 2015.

G. C. Wakchaure and I. Mani, "Effect of binders and pressures on physical quality of some biomass briquettes," J. Agric. Eng. Res., vol. 46(4), no. October-December, 2009, pp. 24-30, 2009.

K. Ugwu and K. Agbo, "Evaluation of binders in the production of briquettes from empty fruit bunches of Elais Guinensis," vol. 2, no. 4, pp. 176-179, 2013.

T. U. Onuegbu, N. O. Ilochi, I. M. Ogbu, F. O. Obumselu, and I. Okafor, "Preparation of environmental friendly bio-coal briquette from groundnut shell and maize cob biomass waste: comparative effects of ignition time and water boiling studies," Curr Res Chem, vol. 4, pp. 110118, 2012.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

values obtained in this study **assumed test period (as stated by Hu et al. [63]) expressed on dry basis.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 37-61

doi: 10.4028/p-I5Ftv2

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-10-30

Revised:
2025-05-06

Accepted:
2025-06-11

Online:
2026-01-07

Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Maria Theresa Isla-Cabaraban , Ivan Vergel Iligan , Romelie Albia , Czarine Maderal , Jenny Navarro and Gunnar Marc Shane Cabaraban

¹Department of Chemical Engineering, Xavier University - Ateneo de Cagayan, Cagayan de Oro City, Philippines

Department of Electrical Engineering, Xavier University - Ateneo de Cagayan, Cagayan de Oro City, Philippines

Department of Science and Technology (DOST), Philippine Council for Industry, Energy and Emerging Technology Research and Development (PCIEERD), Gen. Santos Avenue, Bicutan, Taguig City

matheresa.cabaraban@pcieerd.dost.gov.ph, vergeliligan@gmail.com,

czarine.maderal@gmail.com, 20150007935@my.xu.edu.ph, gcabaraban@xu.edu.ph

Keywords: blue power, salinity gradient power, pressure retarded osmosis, process simulation, LCOE, techno-economic analysis.

Abstract

Salinity gradient power (SGP), also known as blue energy, represents a promising renewable energy (RE) source that can diversify global RE portfolios and address energy security challenges. The Philippines, with its extensive network of estuaries where river outflows meet the sea, offers a strategic opportunity for deploying SGP technologies like the pressure retarded osmosis (PRO). PRO utilizes the osmotic energy produced when freshwater diffuses through a semipermeable membrane into seawater, generating pressurized flow that can drive turbines for electricity generation. This paper explores the energetic and economic feasibility of stand-alone PRO-based power generation in the Philippines, highlighting its potential to support the country's transition to RE. The work employed a process simulation that integrates unit operation models with mass and energy streams. Key economic indicators were evaluated, including net present value (NPV) for total project feasibility, internal rate of return (IRR) for the annualized return rate, and discounted payback period (DPP) for the time needed to recover investments. The levelized cost of PRO energy (LCOE), reflecting the lifetime cost per kWh produced, was compared against benchmarks from other RE sources. Results indicate the viability of PRO, demonstrating a positive NPV at a discount rate, with a 7 -year DPP and an IRR of . The study found that the LCOE value of per kWh , using cellulose triacetate membranes with approximately efficiency, is cost-competitive with other RE sources. However, advancements in membrane properties, particularly durability and water permeability, are essential to improving both cost-effectiveness and scalability of PRO power plants. This investigation emphasizes the potential of blue energy as a RE source uniquely suited to archipelagic countries like the Philippines. By prioritizing innovation in membrane technology, PRO can transition from an emerging technology to a cornerstone of sustainable energy strategies, aligning with global carbonization efforts.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The Department of Energy's Philippine Energy Plan for 2023-2050 projects that the Philippines' annual peak power demand will increase from in 2022 to by 2050 [1]. To meet this rising demand, the Philippine government aims to boost its total installed power capacity from 453.8 TWh in 2020 to 111.5 TWh in 2050. The new capacities for power generation under this scheme will result in a composition that will consist of coal, natural gas, and renewable energy (RE) by 2050 .

Coal-fired power plants dominated the mix at in 2022 . This dependence on coal for energy is correlated with harmful emissions that have resulted in a global climate crisis [2]. The fight against
climate change and its disastrous impacts made necessary shifting toward clean energy sources. A transition to an energy infrastructure that is based on renewable technologies should have considerable positive effects on the economy at the national and international levels.

The International Renewable Energy Agency (IRENA) underscores the need to achieve a RE share in the world's electricity production by 2030 to realize the goals of the Paris Agreement [3]. This approach is crucial in reducing emissions and keeping the global average temperature rise to by the end of this century. The RE sector in the Philippines includes hydropower, geothermal, solar, biomass, and wind resources [1]. However, significant issues remain: solar and wind are often constrained by their inherent intermittence, which depends on solar radiation and wind speed. Therefore, the development of effective energy storage solutions is crucial, although most of the current systems suffer from the issues of high costs [4] as well as the possible contributions towards overall greenhouse gas (GHG) emissions [5].

Here, salinity gradient energy (SGE), also called blue energy, has the potential to be an alternative green energy source. Blue power can substantially impact the global energy balance in the future. Research suggests that the total volume of all rivers pouring into the sea has the potential to generate blue energy between 1.4 and 2.6 TW that can sufficiently meet the power requirements of over of the global population [6]. For the Philippines, this technology presents and opportunity to enhance energy security, reduce reliance on imported fossil fuels (accounting to of the energy mix in 2022 [1]), and deliver reliable power to coastal communities.

In addition to its technical potential, the potential of blue energy, particularly through the implementation of SGE, offers considerable environmental benefits. Unlike coal-fired plants, SGE systems generate net-zero carbon emissions, significantly mitigating the contributions to climate change. Furthermore, SGE operates continuously, unaffected by weather variability, making it a reliable complement to the intermittent nature of solar and wind sources. Notably, research conducted by Mashrafi et al [7]demonstrates that PRO results in the lowest net GHG emissions among RE technologies, as it does not require an energy storage system or the extensive use of rare earth elements, a requirement often associated with other RE technologies such as solar PV and wind. This distinctive advantage highlights the promising role of SGE in the transition towards sustainable energy solutions.

SGE is derived from the osmotic pressure difference from varying ionic concentrations typically between freshwater and seawater. Two main SGE technology types, reverse electrodialysis (RED) and pressure-retarded osmosis (PRO), utilize semi-permeable membranes to harness the osmotic potential and generate electricity through a hydraulic turbine [8-13]. RED generates electricity from the controlled mixing of two water bodies with different salinities. The typical RED configuration involves the arrangement of cation and anion exchange membranes assembled in stacks between a pair of electrodes to form high- and low-salinity compartments [14]. The salinity difference between the saltwater and freshwater passing through these membranes results in the transport of positively and negatively charged ions, creating a net charge transport that can be efficiently converted into electrical energy through redox reactions [9].

Similarly, PRO technologies generate energy from the difference in salt concentration between saltwater and freshwater. In PRO, a semi-permeable membrane separates the draw (saltwater) and feed (freshwater) solutions, allowing the osmotically driven permeation of the feed to the draw chamber. The pressure surge on the seawater side drives the hydraulic turbine to generate electricity [9]. PRO is being explored as a promising technology to enable blue power [7,15], and is the subject of investigation in this work.

Several studies have focused on assessing the economics of PRO technology using different approaches and measurement standards. In a study by Mashrafi et al [12], a life cycle cost analysis (LCCA) was used to evaluate the economic feasibility of the PRO system. This system sourced brine from a seawater desalination plant and assessed two scenarios for transferring treated wastewater from nearby plants as feed solutions. The LCCA findings revealed a relatively high levelized cost of energy (LCOE) for the PRO system, ranging from to . This cost is not
competitive compared to mainstream RE sources like wind ( ) and solar ( ). The study identified transmission and pretreatment costs as significant contributors to the high LCOE.

Al-Musawi et al. [16] used the LCOE to assess the economic viability of PRO technology across different hyper-saline (defined as having ) draw solution concentrations and varying flow rates. The results showed that, like other RE sources, the LCOE for PRO ranged from to , implying that osmotic energy has the potential to be harnessed. The lower limit of the LCOE was achieved when the draw solution concentration was .

The work of Khasawneh et al [17] integrated three PRO power plants using different combinations of feed and draw solutions. While all plants were found to be technically viable for power generation, only the PRO plant running at a high osmotic pressure difference was deemed economically feasible with an LCOE of . This configuration maximizes power density and minimizes the required membrane area. As per their conclusions, the project's financial viability depended heavily on the prices of membranes and energy recovery devices. Though the study was carried out on high salinity differences, it provided important insights into how optimizing system components and membrane performance improves the economic feasibility of PRO systems. These findings may also hold for moderate salinity gradients.

These findings were reinforced by Matsuyama et al [18], who examined the performance of a commercial hollow fiber PRO membrane module using seawater as the draw solution and treated sewage as the feed solution. They reported an estimated cost of power generation of when operating with of seawater per day. Improving membrane water permeability and salt permeability could achieve this level of operation.

The study of Obode et al [19] evaluated the performance of both single- and multi-stage PRO systems, using LCOE as the primary economic indicator. Findings indicate that while high salinity water for draw solution is promising for electricity generation, its economic viability is heavily contingent on membrane properties and system configuration. At the current membrane properties, the LCOE for PRO stands at , rendering it non-competitive compared to other RE sources. However, assuming ideal membrane properties, the LCOE dropped to , demonstrating significant cost-reduction potential by improving membrane technology.

Given the Philippines' extensive coastlines, numerous estuaries, and river networks, SGE has a large potential as a RE source. To the authors' knowledge, there has been no assessment of the potential of this blue energy source in the country. This paper presents the first exploration of the potential of SGE as a source of blue power for the Philippines and its contribution to the country's energy mix. It evaluates the energetic and economic viability of implementing a full-scale 1 MW PRO power plant, considering in the analysis Cagayan de Oro River as a freshwater source and Macajalar Bay as a saltwater source (Fig. 1).

Fig. 1. Location and conceptual layout of the PRO power plant in Cagayan de Oro City, Philippines.

Fig. 1. Location and conceptual layout of the PRO power plant in Cagayan de Oro City, Philippines.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

This section presents details of the PRO power plant conceptual model, the procedure used to determine energy requirements, and the assumptions applied to carry out the assessment of economic feasibility. A flow diagram illustrating the methodology is presented in Fig. 2.

The proposed PRO power plant consisted of a pretreatment system, the PRO system, pumps and circulation system, and the hydraulic pressure converter system. The diagram showing the various processes of the PRO power plant which is the subject of the present study is given in Fig. 3.

The PRO facility commences its operation by sourcing seawater and river water, which undergo filtration treatments before entering the PRO system. To maintain equipment integrity and prolong membrane life, it is crucial for streams to contain minimal solids. Ultrafiltration efficiently removes particles and suspended solids from the feed water (river water) and draw solution (seawater) entering the PRO system, thus preventing membrane fouling caused by these contaminants [20-22]. This results in more stable and efficient operation, ultimately reducing maintenance costs.

A booster pump is utilized to propel seawater toward the membrane. To minimize the pumping energy needed by the booster pump, the incoming seawater stream first passes through the pressure exchanger [19,23], where it is pressurized by the brackish solution discharged from the PRO module [18]. Water flows across the semi-permeable membrane in the PRO module due to the osmotic pressure difference, transforming the chemical potential from the salinity differences into hydraulic pressure. This pressure is harnessed to drive a turbine, which is connected to an electric generator that generates electricity from the turbine's mechanical energy. Through osmosis, of river water permeates the membrane [24], increasing the seawater flow and creating a pressurized brackish solution. This solution is then split into two streams: of the flow is used to drive the pressure exchanger, while the remaining 1/3 powers the Francis-type turbine to generate electricity [14]. The non-permeating portion of the intake feed water stream, known as the freshwater bleed, is recycled back into the process for reuse.

Fig. 2. Methodological framework for evaluating the salinity gradient energy.

Fig. 2. Methodological framework for evaluating the salinity gradient energy.

The PRO System.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

At the heart of the PRO is the osmotic-driven flow of water through the semi-permeable membrane from a low concentration feed water into a pressurized high-concentration draw solution. Throughout this process, water permeating through the membrane progressively dilutes the draw solution while concentrating the feed water. This process continues until an equilibrium in chemical potential is attained across the membrane.

The types of membranes used in PRO are essential for optimizing energy generation efficiency. For optimal performance, membranes should exhibit high water permeability, superior salt rejection
to sustain the osmotic driving force, minimized structural parameters to reduce internal concentration polarization-which is a significant contributor to fouling-and sufficient mechanical strength to withstand the hydraulic pressures encountered during operation [25]. Over the past decade, multiple studies have highlighted significant advancements in the properties of novel PRO membranes [26], accompanied by performance validation. These investigations cover a range of membrane types, including hollow fiber membranes [27,28], innovative thin-film composite membranes designed to achieve high-power density [29,30], and high-strength hollow fibers enhanced with carbon quantum dots incorporated into the membrane matrix [31]. Additionally, zwitterionic modification of membrane surfaces has demonstrated remarkable hydrophilicity, which effectively reduces the adsorption of foulants [32]. The incorporation of nanomaterials into membranes has also led to enhanced resistance to membrane fouling and improved hydrophilicity, resulting in higher permeability [26,33].

Fig. 3. Process flow diagram for the PRO power generation system in Cagayan de Oro City, Philippines.

Fig. 3. Process flow diagram for the PRO power generation system in Cagayan de Oro City, Philippines.

The proposed PRO power plant described in this study will be located at the delta where the Cagayan de Oro River flows into Macajalar Bay. This location strategically utilizes the salinity gradient between the bay's saline waters and the river's outflow to maximize the osmotic pressure for PRO power generation. The salinity of the feed water and draw solution was expressed in terms of the total dissolved solids (TDS), with NaCl being the most abundant dissolved salt.

The van't Hoff equation serves as a critical tool in the analysis and optimization of PRO systems. This equation is based on the assumption of ideal solution behavior, which indicates that solute interactions are negligible. In the case of NaCl as the solute, this assumption holds true for mass fraction concentrations in both the feed water ( 0.00013 ) and the draw solution ( 0.02949 ) below 0.09 [34]. The osmotic pressure difference between the feed water (f) and draw solution (d) was determined via the van't Hoff equation is valid [17,24,35].

where i is the ionic concentration per dissociated solute molecule (for ), R is the universal gas constant, T is the temperature in is the molar mass of the solute, and S is the salinity in serves as the theoretical upper limit for the driving force facilitating the transport of water [36].

In the modeling of PRO systems, the is mitigated by the externally applied hydraulic pressure ( ). This interaction establishes a net driving force ( ) that dictates the water flux across the membrane. The complete theoretical osmotic pressure difference cannot be achieved. To maximize the power density of the semi-permeable membrane, it is optimal to utilize half of the osmotic pressure difference [24,37].

Water flux through the membrane, in , was calculated using the following [17,18,38]:

where A is the water permeability coefficient of the membrane. The volumetric discharge rate of the brackish water ( ) was calculated based on the assumed intake draw solution to feed water ratio of 1.5 [24,39].

since . is the total membrane area, which was determined based on power plant capacity of 1 MW and power density of the cellulose triacetate membrane [24,40].

Pumps and Circulation System.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The mechanical energy balance described in Eq. 5 assumes steady-state operation and adiabatic conditions for the pump. It also assumes negligible changes in the fluid's potential energy and turbulent flow.

The total energy loss due to friction accounts for losses in turbulent flow through equivalent lengths of pipes with relative roughness ( ), and through bends and fittings ( ).

where f is the friction factor, is the length of pipe, v is the fluid velocity, and D is the pipe diameter.

where k is a fitting-specific coefficient.
The effective pump work, which factors in an overall pump efficiency of was calculated as:

Hydraulic Pressure Conversion System.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The pressure exchanger is a crucial energy-recovery device in the PRO system, functioning similarly to a fluid piston. It enhances efficiency by transferring energy from the pressurized draw solution to the incoming feed water through continuously rotating ducts, all while preventing mixing. This recycling of hydraulic energy minimizes the external energy required to pressurize the feed solution, thus improving the net power output of the PRO system.

Recent studies highlight the importance of pressure exchangers in hybrid PRO configurations, particularly when integrated with reverse osmosis (RO) or desalination plants. In these setups, energy from saline brine can be harnessed to reduce operational costs [28,41]. For instance, Kim et al. [28] revealed that the incorporation of isobaric pressure exchangers in PRO-RO systems can lead to energy savings of approximately , significantly boosting economic feasibility. Nevertheless, optimizing the pressure exchanger efficiency relies heavily on accurate flow rate matching and the minimization of irreversible losses [28,42,43], especially in systems with fluctuating salinity gradients.

Initially, the pressure exchanger is supplied with low-pressure draw water and a portion of brackish water from the PRO module. Subsequently, it transfers the high-pressure energy from the brackish water to the low-pressure energy of the draw water. Upon exiting the pressure exchanger, the highpressure draw water is directed toward the membrane module.

The pressure of the draw water at the exit was determined by applying the overall mechanical energy balance as detailed in Equation 5, accounting for negligible changes in kinetic energy and energy losses arising from friction.

where j is a stream identifier, is the mass flow rate of the stream, is the stream density, and and are the pressures of the streams entering and exiting the pressure exchanger. The stream densities used in the calculations were and for draw water and brackish water, respectively. Considering a efficiency for the pressure exchanger [44], the exiting draw water pressure was computed from Eq. 10.

where is the pressure exchanger efficiency.
Analogous to calculating effective pump work, effective hydraulic turbine work was determined using Eq. 11.

where was assumed to be [26].

Economic Analysis.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Determining economic viability requires undertaking a detailed examination of the project's expected cost structure and revenues to evaluate its economic sustainability over time. This analysis involved quantifying capital expenditures (CapEx), operating expenses (OpEx), and revenue forecasts as well as performing sensitivity analysis on these elements to assess possible risks and uncertainties. The aim is to ensure that the investment provides an adequate return relative to the financial commitments across the lifespan of the investment. Key financial indicators, including net present value (NPV), internal rate of return (IRR), discounted payback period (DPP), and levelized cost of energy (LCOE), offer a comprehensive assessment of the project's profitability and competitiveness in the energy market.

Preliminary cost estimation techniques were employed to approximate the initial costs of the proposed PRO power plant. CapEx estimates were derived from a combination of vendor quotations and established costing models [45,46], yielding a baseline calculation for essential components such as pre-treatment systems, piping, membrane modules, pressure exchangers, and turbines.

The power law was employed to extrapolate the costs of equipment with a known capacity based on the costs associated with a comparable piece of equipment of different capacities, as illustrated by Eq. 12 [46].

Similarly, historical costs that may have fluctuated due to shifts in economic conditions were adjusted using appropriate cost indices, as shown in Equation 13.

, which stands for the membrane module's overall cost, was calculated by multiplying the module's unit cost [47] with the number of modules required to produce the planned power output.

OpEx was determined by combining expenses for membrane replacement, labor, maintenance, and repair. In this work, it was assumed that the membranes would last years [19,48,49], and the power plant using PRO would have a lifespan of 15 years . The annual operating cost for membrane replacement ( ) was estimated as [17,19].

Annual costs of equipment maintenance and repairs was set at of the fixed capital investment [17].

The DPP accounts for the time value of money, thus providing a more precise assessment of investment viability than the conventional payback period. NPV was calculated by discounting the expected future cash flows to their present value at a discount rate (r) of 10%, as shown in Eq. 15.

where is the net cash flow during period t . The IRR was estimated by finding the r that makes the net NPV of all cash flows from the investment equal to zero. It represents the project's expected annual return, useful for assessing whether the investment meets the required r of . The LCOE represents the minimum cost of energy required to achieve break even over the operation lifespan of the PRO system [50]. This metric was calculated from Eq. 16.

where the total energy production of the system is 1 MW . System up-time was set at 7,896 hours.

A sensitivity analysis was conducted to determine how fluctuations in key parameters affect the system's performance and economic feasibility. This analysis ensures the factors that have the greatest impact on the results are determined.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Table 1 presents the computed power output from the PRO system utilizing feedwater sourced from the Cagayan de Oro River and draw solution derived from Macajalar Bay. The plant achieved an annual net energy generation of 7.322 GWh. Based on this output, the facility's energy efficiency was determined to be . The substantial net output points to the PRO system's viability as a RE source, demonstrating its capacity to generate considerable electricity with comparatively minimal operational energy input.

Table 1. Flow rates and net energy generation of PRO power plant in Cagayan de Oro City, Philippines.

ΔΠosm | bar | 25.96
ΔPh | bar | 12.98
Qf | 10⁶ m³/d | 0.07193
Qd | 10⁶ m³/d | 0.1079
Qbr | 10⁶ m³/d | 0.1726
Wactual,t | GWh/y | 7.896
Wactual,p | GWh/y | 0.5740
Net energy | GWh/y | 7.322

The analysis of the potential energy generation from the full-scale PRO system reveals promising results for blue energy production, indicating a strong potential for efficiency in harnessing osmotic energy. Nevertheless, the financial outlook is equally crucial to determining its overall feasibility. Fig. 4(a) presents the contribution of the various components to the CapEx of a PRO power plant. A substantial portion of the budget is directed toward the pressure exchanger ( ) and the PRO module ( ) [17], critical components that affect the overall efficiency of the osmotic energy conversion process.

Fig. 4(b) shows how the operational cost components are distributed in a PRO power plant. Membrane replacement, as well as maintenance and repair, dominate the OpEx, each accounting for of the total cost. This highlights the critical role that membrane performance and durability play [17,19], as well as the need for routine maintenance, in driving OpEx. The high cost of membrane replacement indicates that the membrane deteriorates over time from exposure to draw solutions or contaminated feed water. Maintenance and repair expenses refer to routine servicing that the system
carries out to ensure that it works in a reliable manner. A focus on future optimization initiatives must be placed on improving the durability of membranes and developing better maintenance methods to address the low efficiency and high operational costs resulting from the wear and tear of such elements.

Fig. 4. Breakdown of (a) capital and (b) operating costs for a PRO power plant.

Fig. 4. Breakdown of (a) capital and (b) operating costs for a PRO power plant.

A projected NPV of shows that the power plant using PRO technology will create significant value over its lifetime, more than covering the present-day equivalent of the initial investment. This suggests that the investment is financially viable and warrants further pursuit.

The IRR of exceeds the typical discount rate of , implying that the project could deliver returns higher than comparable investments with similar risk profiles. Investors will have an interesting project in this case, since it has the potential for a fair return rate.

The 7.03-year DPP is justified, owing to the large capital outlay and the long operational lifetime of the project. However, competition from other RE initiatives that offer better payback periods must be considered as well. For instance, solar photovoltaic (PV) systems exhibit DPPs ranging from 1 to 5 years [51-53], which provides a more rapid return on investment and may render the 7 -year DPP less appealing to potential investors. Despite the 7 -year DPP, a higher IRR than the cost of capital, coupled with a positive NPV, indicates that the investment remains viable.

The energy industry in the Philippines relies heavily on coal and oil imports [1], especially in the production of thermal power. This makes the economy vulnerable to the changes in the global pricing of fossil fuels that tend to be affected by political conflicts, supply chain interruptions, and demand changes. These weaknesses have a detrimental impact on electric power pricing, something that could be complicated even further by fluctuating exchange rates. The PRO power plant is clearly wellpositioned in this energy market with an LCOE of . This dependence on imports poses a critical risk to the stability and predictability of the country's energy market. In the current energy market context, an LCOE of positions the PRO power plant as highly competitive. The rising cost of conventional energy sources, driven by fluctuating fuel prices, has made alternative energy sources that are cheaper or comparable in cost to conventional options more desirable to investors.

Sensitivity Analysis.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

A sensitivity analysis was carried out to gain a clearer understanding of how changes in key assumptions might affect the overall profitability of the plant. The project's ability to withstand
market changes was assessed by looking into these potential fluctuations, and the main risk factors that can affect its financial results were determined.

Fig. 5. Influence of parameter variations on the net present value of the PRO power plant.

Fig. 5. Influence of parameter variations on the net present value of the PRO power plant.

Fig. 5 shows the sensitivity of the NPV for the PRO power plant project to variations in key cost parameters, namely CapEx, OpEx, selling price, land cost, and discount rate. Each parameter was altered by , and the effects on NPV are presented.

The electricity selling price serves as the primary factor influencing the NPV of the project. It exhibits the strongest direct correlation with NPV; for example, a change in the selling price can result in substantial fluctuations in NPV, as demonstrated in Table 2. However, this relationship comes with significant risks. The revenue generated from the sales of electricity exposes the project's financial performance to market price volatility. Such volatility is driven by various factors, including competition and shifts in energy demand, representing a considerable challenge. Additionally, when electricity prices decline to around per kWh , especially in conjunction with high discount rates exceeding 10%, it often results in a negative NPV, as further emphasized in Table 2.

Securing strategic electricity pricing through long-term contracts or favorable market conditions may improve the project's profitability. Implementing sound revenue optimization strategies, for example, standardizing power purchase agreements (PPAs) or establishing carbon-pricing mechanisms is crucial because of the significant impact of electricity selling price on the NPV. Other determinants such as variation in energy demand, changes in legislation or competition from other RE sources have an impact on the economic performance of the PRO power plant as well. Thus, it will be imperative to manage price risks and to guarantee stable proceeds from electricity sales to enhance the viability and profitability of the project in the long term.

CapEx represents a crucial initial investment necessary for the success of a project. It has been noted that there exists a moderate negative correlation with CapEx and the NPV. An increase in CapEx necessitates greater or more sustained cash flow, along with a larger initial capital outlay. Conversely, a decrease in the CapEx lowers the breakeven point, enabling the project to quickly generate positive returns. However, there are significant risks associated with this expenditure. Given that NPV is sensitive to CapEx, mismanagement during the early stages of the project implementation
can erode profit margins and delay the project's DPP, thereby reducing its appeal to investors. Implementing an efficient project design coupled with the optimum procurement strategies and adequate risk management is imperative in achieving project profitability.

While OpEx may not have the same pronounced effect on the power plant's NPV, the continuous operating costs incurred over the lifespan of the power plant, such as membrane replacement, labor, maintenance, and repair, are critical considerations in long-term financial planning. OpEx is particularly relevant given the demands of technology. The power plant based on PRO technology relies on the continuous functionality of the membranes to efficiently convert osmotic pressure into useful energy. However, issues such as fouling and scaling reduce overall energy output [22,54-56] and simultaneously increase operational costs, affecting profitability. To mitigate the adverse effects of fouling, equipment needs to be routinely serviced, and membranes replaced regularly. These recurrent and periodic maintenance activities contribute to OpEx [7,57,58]. Furthermore, unplanned maintenance can result in downtime, complicating operational efficiency and overall performance. The sensitivity of NPV to OpEx highlights the need to optimize operational efficiency and reduce downtime throughout the plant's lifespan. Several strategies can be employed such as optimizing advanced membrane technologies and implementing predictive maintenance to be able to reduce costs and improve the performance metrics.

When considering land costs, it is important to recognize that while they may appear marginal, they possess significant strategic value. Compared to other financial variables like CapEx, OpEx, and the pricing of electricity sales, land acquisition costs have a relatively minor impact on the project's overall profitability. Land use for PRO power plants tends to be significantly lower than conventional RE installations, such as solar PV or wind energy systems [7], which require extensive land areas to accommodate large arrays of solar panels or turbine farms [59-62]. While the effect of land costs on the project's NPV is marginal, it is nevertheless a critical consideration in the site selection for the PRO facility. The volatility in land prices in coastal areas is influenced by factors such as proximity to water for productive uses and stringent regulatory environment. The project's overall viability could be affected where land prices are high or where regulations increase land purchase costs. The PRO system's use of the osmotic pressure gradient between river water and seawater for energy production makes it suitable for integration into existing coastal or industrial infrastructure. Extensive land acquisition is thus circumvented, which reduces the vulnerability to land pricing fluctuations on the project's overall economic viability. Consequently, even though land acquisition is not a primary driver of NPV fluctuations, strategic site selection and cost-efficient land procurement approaches are important for enhancing project financial performance.

In contrast to CapEx and OpEx, which experience a pronounced decrease in NPV as their values increase, the effect of changes in the discount rate on NPV is relatively minimal within the to range. However, this impact becomes critical at higher rates. For example, at a discount rate, the cost of electricity at per kWh results in a negative NPV of million. Moreover, when discount rates exceed , it becomes necessary to raise electricity prices to maintain the project's viability.

Several key risks are associated with this scenario. A primary concern is the potential for escalating financing costs, which can arise from macroeconomic changes such as inflation and interest rate increases. Additionally, the long lifespans of these projects can intensify the effects of discounting on future cash flows, highlighting the importance of careful consideration of these factors in financial planning. The findings suggest that the performance of a power plant utilizing PRO technology is not highly sensitive to short-term variations in market interest rates, provided those changes remain within a reasonable range. Nonetheless, it is important to recognize that, although the discount rate may appear to have limited impact within the narrow range tested, its influence on NPV becomes more significant when considering larger deviations, as evidenced in Table 2.

At a selling price of , the NPV remains positive at lower discount rates, but experiences a swift decline as the discount rate increases. This implies that for projects seeking returns below this price level, variations in the discount rate have strong, if not compelling, implications on the NPV of the project. Such sensitivity indicates that even with high discount rates, the project's financial
prospects are still at risk. It suggests that lower electricity prices do not allow the project's cash flows to cover the more expensive cost of capital. Consequently, the financial viability of the project is critically tied to sustaining lower discount rates.

Table 2. Sensitivity of NPV [million $] to discount rate and electricity selling price for a PRO power plant in Cagayan de Oro City, Philippines.

Discount rate (r)
Electricity selling price [$ per kWh]
$0.267
$0.270
$0.284

6%
86.15
139.09
385.94

8%
32.29
84.10
325.69

10%
(21.57)
29.11
265.45

12%
(75.44)
(25.88)
205.21

14%
(129.30)
(80.87)
144.97

An increase in the discount rate, driven by macroeconomic shifts or rising financing costs, results in a proportional decrease in NPV at all levels of electricity prices. This aligns with established financial theory, which posits that a higher discount rate - which represents the opportunity cost of capital - diminishes the present value of projected future cash flow, particularly for long-term projects like PRO power plants, where cash inflows are realized over an extended period. For all the electricity selling prices analyzed, the project's NPV stays positive even at and discount rates indicating that the project is financially viable. Given the long operational lifespan of PRO power plants and the fact that their primary revenue streams come from steady, long-term energy production, securing low financing costs and minimizing risk premiums are crucial for maintaining project viability.

However, as the discount rate increases to and , NPVs become negative at lower electricity prices. The project's viability, therefore, relies on sufficiently high electricity pricing that offsets the adverse effects caused by an increase in the discount rate. The project would then need financial optimization or modifications in operational parameters to ensure profitability.

To address the 7-year payback period and enhance the financial viability of the PRO power plant project in Cagayan de Oro City, several strategic recommendations are proposed. First, securing longterm power purchase agreements (PPAs) with local off-takers, such as the Cagayan Electric Power and Light Co. (CEPALCO), industrial consumers, or wastewater treatment facilities is essential. Such agreements will help stabilize revenue streams, thereby mitigating the project's exposure to fluctuations in the spot market. By negotiating government-backed PPAs at or above per kWh, project developers can establish predictable cash flow, which, in turn, will improve the project's NPV and lower the overall cost of capital.

Second, the project's economic outlook may be substantially improved through the utilization of incentives offered under the Philippines' Renewable Energy Act of 2008 (Republic Act No. 9513). Eligible PRO plants may qualify for feed-in tariffs (FITs), which guarantee above-market rates for clean electricity. Additional incentives include the provision of corporate income tax holidays and value-added tax exemptions on imported membranes and RE equipment. Moreover, the duty-free importation of advanced membrane modules can significantly reduce CapEx. Furthermore, funding opportunities provided by the Philippines' Department of Science and Technology (DOST) and the Department of Energy (DOE) could play a crucial role in offsetting research and development costs during the demonstration phase. Such financial support not only diminishes associated financial risks but also facilitates the acceleration of technology maturation processes.

Advocacy for PRO-specific subsidies under RA 9513 should prioritize facilities that utilize nonfreshwater feed streams, such as municipal brine effluent and industrial wastewater, to align with national water-energy nexus objectives. To further enhance returns and promote long-term sustainability, project developers should be encouraged to invest in infrastructure that effectively reduces non-revenue water (NRW), including advanced leak detection and recovery systems. These initiatives will not only improve operational efficiency but also bolster the case for sustained policy
support and private investment in PRO, recognizing it as a strategic complement to the Philippines' RE portfolio.

A third avenue for enhancing financial viability lies in the exploration of carbon finance mechanisms, such as the Clean Development Mechanism (CDM) under the United Nations Framework Convention on Climate Change (UNFCCC) or emerging domestic carbon-trading platforms. These mechanisms may provide an additional revenue stream by monetizing reductions in GHG emissions.

Operations can also be optimized, and costs reduced through targeted strategic initiatives addressing CapEx and OpEx. On the CapEx side, it is advisable for the City Government and project partners to negotiate bulk purchasing or leasing arrangements with membrane suppliers, particularly those engaged in the Mindanao desalination sector, thereby securing more favorable pricing terms. The implementation of a modular PRO plant design would facilitate phased execution aligned with municipal demand projections, allowing for the amortization of CapEx across multiple budget cycles. Additionally, situating the PRO facility in proximity to the Macajalar Bay desalination and wastewater treatment complexes could effectively lower land acquisition costs while enabling the utilization of intake, discharge, and utility infrastructure.

In terms of OpEx, investing in advanced, fouling-resistant membranes may prolong service life and reduce the frequency of replacements. The integration of Internet of Things (IoT)-enabled sensors within the membrane modules will allow for real-time monitoring of pressure differentials and salt passage rates, thus facilitating the development of predictive maintenance schedules intended to minimize unplanned downtime. Finally, establishing a training partnership with local technical schools and the Cagayan de Oro City Water District (COWD) would promote a specialized workforce, thereby reducing reliance on external contractors and enhancing in-house operational efficiency.

Potential Environmental Impacts of the PRO Project

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Positive Impacts.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The project presents a promising pathway toward cleaner energy production by offering a lowcarbon alternative to traditional fossil fuel-based power generation. It aligns with the Philippines' RE goals, aiming to diversify the energy mix, address the country's growing electricity demand, and reduce dependence on imported fossil fuels. By integrating a clean, locally sourced energy solution into the grid, the plant supports national objectives of reducing GHG emissions and promoting sustainable economic development. Utilizing the natural osmotic pressure difference between seawater drawn from Macajalar Bay and freshwater from the Cagayan de Oro River, PRO has the potential to significantly decrease GHG emissions, making it a compelling component in global efforts to combat climate change. While the current scale of PRO deployment is modest compared to solar and wind energy, its low operational carbon footprint positions it as a valuable contributor in a diversified RE portfolio.

In contrast to traditional thermal power plants, which release considerable amounts of air pollutants like sulfur dioxide, nitrogen oxides, and particulate matter [63,64], the PRO technology generates minimal emissions during operation. The lack of combustion processes alleviates concerns associated with smog formation, acid rain, and respiratory health risks for nearby communities. Consequently, PRO provides a cleaner and safer operational profile that is in line with increasingly stringent environmental regulations and the growing public demand for sustainable energy solutions.

Another advantage of the PRO technology is in its capacity to integrate seamlessly with existing water treatment infrastructure. When used in conjunction with desalination or wastewater treatment plants, PRO systems can effectively utilize process byproducts that often present management challenges, such as brine or brackish water. This synergy not only enhances resource efficiency but also lowers the operational costs associated with water treatment facilities. Additionally, employing PRO to recover energy from waste streams addresses two vital sustainability goals: improving energy
efficiency while simultaneously minimizing the environmental impacts associated with waste disposal.

Negative Impacts.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Filtration processes for seawater and river water play a vital role in preventing membrane fouling in PRO systems. However, these processes inevitably generate waste streams, including removed suspended solids, which can smother benthic habitats, diminish light penetration, and adversely affect fish and invertebrate populations [65,66].

The mixing of water with the draw solution results in a brackish effluent that, if not managed properly, could severely impact estuarine ecosystems. Elevated salinity levels may disrupt the delicate equilibrium between freshwater and saltwater systems, posing a threat to aquatic biodiversity, particularly in sensitive regions such as the Cagayan de Oro River estuary. Species that are specifically adapted to particular salinity ranges might experience stress, suffer population declines, or even face local extinction [67,68]. Additionally, long-term alterations in ecosystems could lead to reduced fishery productivity, adversely affecting the livelihoods of communities that rely on estuarine resources.

The proposed plant's demand for freshwater resources may further strain an already stressed water system in Cagayan de Oro City, where non-revenue water levels have exceeded due to leakage and illegal connections [69]. Increased competition for limited water supplies could exacerbate tensions between the facility and local communities, especially during periods of drought or low river flow.

Strategies to Mitigate Negative Impacts.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The potential ecological risks to estuarine ecosystems linked to the discharge of removed solids during the filtration processes of the proposed PRO plant necessitate stringent compliance with regulatory frameworks designed to minimize ecological harm and safeguard downstream users. This includes adherence to the Water Quality Guidelines and General Effluent Standards of 2016 [70]. Consequently, it is imperative that robust environmental regulations govern not only the permissible limits on discharges but also the practice for treating and disposing of residual solids.

The establishment of wetland buffers between discharge points and estuaries presents a natural and effective strategy for mitigating the environmental impacts associated with PRO technology. These vegetated buffers provide an element of passive remediation that requires minimal energy input compared to engineered systems, thereby aligning with the low-carbon principles characteristic of RE projects. Wetlands, which are often populated by resilient plant species such as mangroves and seagrasses, can significantly reduce the risk of salinity imbalances in sensitive estuarine ecosystems by sequestering excess salts and nutrients from discharged brine or wastewater.

Furthermore, the development of partnerships with local industries represents a strategic approach to the sustainable management of brine or brackish water. By repurposing these high-salinity streams for industrial applications, it is possible to substantially reduce the volume of environmentally detrimental discharges. For instance, coastal salt production facilities could effectively utilize concentrated brine, as exemplified in the Mega-ton Water System [71], converting what would otherwise be a waste byproduct into a valuable resource. Similarly, aquaculture facilities that focus on salt-tolerant species can incorporate brackish water into their operations, thereby enhancing resource efficiency. Collaborative efforts of this nature not only advance the goals of environmental sustainability but also yield reciprocal economic advantages, thereby promoting circular economy principles within the local community.

To alleviate river water demand, particularly in regions already experiencing water stress, such as Cagayan de Oro, a strategic approach would involve utilizing treated wastewater as a source of feed water. By capitalizing on wastewater streams from the various industries, particularly those located along the Macajalar Bay area, the PRO system can circumvent the additional extraction of freshwater resources. This approach not only reduces the ecological risks typically associated with diverting or altering estuarine salinity regimes, such as biodiversity loss, vegetation die-off, disruptions to nutrient cycling [72-74]but also brings economic advantages.

Comparative Analysis between PRO Technology and Traditional Renewables

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Cost.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The LCOE of per kWh points to the economic competitiveness of the PRO technology compared to other RE alternatives [16,75]. It is noteworthy that this potential niche of the PRO may be augmented through advancements in membrane technology [7,16], as well as the incorporation of the PRO into hybrid systems to reduce the overall costs associated with the process [76,77].

The LCOE for solar PV systems and offshore wind technologies have seen a significant decline over the last few years. This reduction can be attributed to technological advancements and economies of scale, which have improved efficiency and lowered costs overall. However, in some regions, the LCOE for solar PV and other renewables continues to be at or above per kWh [78-80]. Such heterogeneity can be attributed significantly to locational issues such as the availability of renewables, economic and financial conditions and policy instruments. For instance, in regions with less optimal solar resources, higher land costs, or weaker grid infrastructure, solar PV can be priced closer to or even above the per kWh threshold [81]. Offshore wind usually has a higher LCOE because of the substantial capital and operational costs involved in installing and maintaining turbines in marine settings. However, the LCOE for offshore wind can exhibit considerable variability, especially in regions where maritime logistical challenges, grid integration complexities, and permitting procedures entail increased costs [82,83].

Table 3. Levelized cost of energy (LCOE) comparison of renewable energy technologies, emphasizing the competitiveness of pressure-retarded osmosis (PRO), solar photovoltaic (PV), and offshore wind systems.

Technology | LCOE range ($ per kWh) | Key cost factors | Notes
PRO (blue energy) | 0.17* | Membrane efficiency, hybrid system integration, R&D advancements | Competitive with renewables; costs may decrease further with membrane tech improvements [7,16,76,77].
Solar PV | 0.026 – 0.422 | Resource availability [84], land costs, grid infrastructure [85], regional policy incentives | Declining costs globally, but regionally variable (e.g. higher in areas with weak solar resources) [78–81].
Offshore wind | 0.016 – 0.285 | Marine installation/ maintenance, grid integration, logistics [86] | High capital/operational costs; location-dependent challenges inflate LCOE in some regions [82,83].

this study

Scalability.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

While the PRO technology presents an innovative approach to RE generation, it faces notable challenges. The semipermeable membrane is a crucial component in ensuring efficient energy production, and achieving commercial viability necessitates membranes that can sustain power densities above . The Statkraft project, a real-world pilot PRO plant, demonstrated that this threshold is indeed achievable; it successfully reached power densities of approximately when integrated into the Mega-ton project, which primarily focused on seawater desalination [71,77]. Moreover, PRO is geographically limited to areas with salinity gradients, such as estuaries and desalination brine outfalls, which significantly restricts its broader applicability. While PRO currently
serves as a small-scale solution, its modular design allows localized deployment near costal industries (e.g. desalination facilities) or wastewater treatment plants [76].

In contrast, solar and wind energy technologies have established themselves on a global scale, with solar power boasting an installed capacity of around and wind energy at around worldwide [87]. Their modular designs enable deployment across a wide variety of environments, i.e. from residential rooftops to expansive offshore farms, highlighting their versatility and scalability.

Environmental Impact.

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

PRO systems operate with minimal carbon emissions, thereby positioning them as a comparatively clean energy source. Nevertheless, ecological concerns remain, particularly regarding potential disruption to salinity regimes in estuarine ecosystems and competition for water resources. Importantly, the environmental footprint of PRO can be significantly mitigated through integration with desalination facilities or wastewater treatment processes, where high-salinity brine streams can be effectively repurposed, reducing the standalone environmental effects.

While solar and wind energy play crucial roles in global decarbonization efforts, they are not without their own environmental challenges. The production of solar panels necessitates extensive mining of silica-containing materials and metals such as cadmium, aluminium, copper, nickel, gallium, selenium, and tellurium, leading to substantial carbon footprints [88]. Furthermore, photovoltaic panels can become hazardous e-waste at their end-of-life phase, posing environmental and health risks [89]. Wind turbines also contribute to the mortality of birds and bats [90,91] and face recyclability challenges, especially concerning composite turbine blades [92]. Additionally, the installation of both solar and wind energy systems requires substantial land take, which amplifies their environmental footprint due to land-use competition and habitat disruption [93,94].

Conclusions and Recommendations

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

This study gives an assessment of the technical and economic feasibility of a full-scale PRO facility for SGP generation in the Philippines, utilizing the Cagayan de Oro River as the feedwater source and Macajalar Bay as the draw solution. The findings reveal that the PRO system achieves a remarkable energy efficiency of , resulting in an annual net energy output of 7.322 GWh . Moreover, the estimated LCOE of ranks PRO technology at a competitive level with other RE sources, but only under certain circumstances.

The financial assessment indicates a positive NPV of , an IRR of , and a DPP of 7.03 years. These metrics suggest that the PRO power plant is a sound investment. However, the extended DPP may deter investors from supporting the project, especially when it is compared with PV systems and other RE options with faster returns. Nevertheless, the sensitivity analysis highlights the resilience of the PRO system, with NPV being particularly sensitive to factors like electricity selling prices, CapEx, and OpEx. The LCOE and profitability of the proposed PRO power plant can be improved by enhancements in membrane technology and operational efficiencies.

The viability of this project is contingent upon strategic interventions that address its projected seven-year payback period alongside inherent ecological risks. Financial assessments highlight the necessity for stabilizing revenue streams through long-term PPAs with stakeholders such as the CEPALCO or industrial consumers. Such agreements can effectively mitigate exposure to market volatility and enhance the project's NPV. Additionally, the implementation of government incentives outlined in the RA 9513, including feed-in tariffs, tax exemptions, and grants, serves to augment the economic feasibility of the project by alleviating CapEx and operational risks.

From an environmental perspective, PRO technology emerges as a promising low-carbon alternative to fossil fuel reliance, thereby aligning with national decarbonization objectives. However, the ecological consequences, particularly those associated with potential disturbances to estuarine ecosystems due to salinity imbalances and competition for water resources, necessitate the establishment of proactive mitigation strategies. Measures such as repurposing treated wastewater, developing wetland buffers, and forging industrial partnerships for brine reutilization represent best practices reflective of circular economy principles. These strategies not only decrease freshwater
demand but also convert waste streams into valuable resources, ultimately enhancing both environmental integrity and economic viability.

A comparative analysis with solar and wind energy technologies reveals the distinctive advantages of PRO. Despite the inherent limitations posed by geographic dependencies on salinity gradients, PRO's LCOE at per kWh and its minimal land-use requirements afford it a unique position as a complementary energy solution within coastal or industrial contexts. In contrast to solar and wind energy, which contend with challenges related to material-intensive manufacturing processes and habitat disruption, the integration of PRO technology with existing water infrastructure mitigates standalone environmental impacts. Moreover, advancements in membrane technology, as demonstrated by initiatives such as Statkraft's pilot plant, suggest substantial potential for improved power densities and expanded applicability across diverse environments.

Based on the findings of this study, the following recommendations are proposed:

Membrane technology enhancement. The economic viability of the proposed PRO power plant can be significantly improved by enhancing membrane performance, especially relating to water permeability [18] and fouling resistance. The use of more durable and efficient membrane technologies has the potential to reduce OpEx associated with membrane replacement and maintenance.

Optimization of operational efficiency. Looking into the large contribution of the OpEx, particularly with respect to membrane maintenance and system efficiency, it is surmised that the use of more advanced predictive maintenance and automation of the systems would significantly reduce the occurrence of downtime and lower OpEx. In addition, applying novel techniques to optimize energy conversion processes as well as minimizing energy losses can further improve the system's performance.

Market strategies for electricity pricing. The sensitivity analysis underscored the substantial impact of electricity selling prices on the project's financial viability. To mitigate revenue risks and achieve financial viability, it is necessary to either secure long-term PPAs or target markets with a more stable electricity price. This strategic improvement would internally increase the return on investment and would equally help with reducing market risk exposure.

Government and policy support. It is evident that the PRO technology has an immense potential to aid the growth of the RE sector in the Philippines, hence the need for policies from the government to reinforce and broaden the incentives for RE initiatives, one of which is blue energy sources of energy such as PRO. Adding PRO technology in the mix of the RE resources deployed in the country will not only help to diversify energy sources in the country and lower its dependence on external fossil fuels, but also contribute to the improvement of energy self-sufficiency and environmental sustainability.

Expansion of site assessments. Though the present research is limited to Cagayan de Oro City, it is recommended that other prospective sites in the Philippines, especially those with notable salinity gradient regions, be explored in-depth. This could further enhance the comprehension of SGP generation in the entire country and assist in locating areas that would be most advantageous for the use of PRO technology.

Future Outlook

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

The proposed PRO power plant in Cagayan de Oro City presents a blueprint for scaling decentralized energy solutions for coastal communities. However, expanding its capacity beyond 1 MW entails addressing various technical, economic, and environmental challenges. The development and testing of next-generation membranes with customized surface chemistries and hierarchical pore structures will be critical for maintaining high water flux and salt rejection under elevated hydraulic pressures. To scale capacities to , membrane areas will need to reach approximately 10,000 . Thus, pilot-testing modular membrane skids in both serial and parallel configurations will be crucial for validating performance under real-world fouling conditions and pressure cycles. Current pilot projects, such as the Statkraft prototype and the Megaton Water Initiative, have
demonstrated power densities exceeding , suggesting significant potential for scalability provided that membrane costs are reduced and modular designs are optimized.

Hybrid systems integrating PRO with solar, wind, or desalination infrastructure could significantly boost energy output and grid stability, especially in coastal regions that exhibit salinity gradients. For instance, integrating PRO with desalination plants could mitigate brine disposal issues while simultaneously producing clean water and energy, particularly relevant to water-stressed areas like the Middle East and North Africa. In summary, while PRO is unlikely to rival solar and wind as dominant RE technologies, it offers significant promise as a complementary solution in specific niche applications, particularly in coastal areas with significant salinity gradients or at industrial sites looking to repurpose waste brine streams.

Comprehensive life-cycle and cost-benefit assessments at scales exceeding 1 MW are needed to quantify the long-term environmental and economic viability of PRO in tropical regions. The global relevance will be further enhanced by evaluating hybrid configurations, such as PRO-solar and PROwind co-location, to optimize RE yield per unit of land or water resource.

Globally, PRO's niche is found in regions where freshwater converges with seawater, such as river deltas, industrial coastal zones, and areas near hypersaline lakes. Countries with extensive coastlines, including Indonesia, Brazil, and India, can utilize PRO to diversify their RE portfolios while addressing localized challenges related to the energy-water nexus. However, achieving scalability necessitates tackling site-specific ecological risks, such as salinity disruption, through adaptive strategies like dynamic brine dilution and AI-driven discharge management. Insights gained from the integration of treated wastewater as feedwater underscore the importance of adopting circular economy approaches, which can be effectively replicated in regions such as California or Israel, where water reuse is a priority.

Economically, achieving economies of scale will depend on reducing CapEx through standardized membrane manufacturing and leveraging green financing mechanisms. The proposed project in Cagayan de Oro City, with its LCOE of per kWh , aligns with costs observed in early-stage solar and wind projects, suggesting PRO could follow a similar cost-reduction trajectory with sustained R&D. International collaboration, such as joint ventures under ASEAN's RE frameworks or European alliance for osmotic energy, could accelerate innovation and policy alignment.

Ultimately, the future of PRO depends on effectively transitioning from pilot-scale achievements to commercial viability. By integrating osmotic energy into hybrid systems, advocating supportive policies, and emphasizing ecological protections, PRO has the potential to become a cornerstone of sustainable energy strategies in coastal regions worldwide. Rather than competing with established renewable sources, PRO can complement them. The proposed Cagayan de Oro project highlights that PRO's global relevance is not in replacing solar or wind, but in filling critical gaps in the renewable landscape, particularly at the intersection of water and energy sustainability.

References

The original version of this paper is available on https://www.scientific.net/EI.16.37.pdf

Department of Energy, Philippine Energy Plan: Transitioning to Reliable, Clean and Resilient Energy (Volume 1), 2023. https://doe.gov.ph/sites/default/files/pdf/pep/PEP_20232050_(Volume_I).pdf (accessed October 28, 2024).

T.Q. Donaghy, N. Healy, C.Y. Jiang, C.P. Battle, Fossil fuel racism in the United States: How phasing out coal, oil, and gas can protect communities, Energy Res Soc Sci 100 (2023) 103104. .

International Renewable Energy Agency (IRENA), Tripling renewable power and doubling energy efficiency by 2023: Crucial steps toward 1.5 C, (2023).

I. Gwayi, S.P. Ayeng'o, C.Z.M. Kimambo, Selection of electrochemical and electrical energy storage systems for off-grid renewable energy mini-grids: A review, Clean Eng Technol 25 (2025) 100906. .

Y. Jiao, D. Månsson, Greenhouse gas emissions from hybrid energy storage systems in future renewable power systems - A Swedish case based on consequential life cycle assessment, J Energy Storage 57 (2023) 106167. .

A. Emdadi, P. Gikas, M. Farazaki, Y. Emami, Salinity gradient energy potential at the hyper saline Urmia Lake - ZarrinehRud River system in Iran, Renew Energy 86 (2016) 154-162. .

S. AL Mashrafi, N. Diaz-Elsayed, J. Benjamin, M.E. Arias, Q. Zhang, An environmental and economic sustainability assessment of a pressure retarded osmosis system, Desalination 537 (2022) 115869. .

J. Lee, Y. Shin, J. Kim, S. Hong, Feasibility and challenges of high-pressure pressure retarded osmosis applications utilizing seawater and hypersaline water sources, Desalination 581 (2024) 117578. .

T. Withers, S.P. Neill, Salinity Gradient Power, Comprehensive Renewable Energy, Second Edition: Volume 1-9 1-8 (2022) 50-79. .

M. Essalhi, A. Halil Avci, F. Lipnizki, N. Tavajohi, The potential of salinity gradient energy based on natural and anthropogenic resources in Sweden, Renew Energy 215 (2023) 118984. .

X. Zhou, W. Bin Zhang, J.J. Li, X. Bao, X.W. Han, M. Myintzu Theint, X.J. Ma, An electrochemical system for salinity gradient energy harvesting, Energy Convers Manag 255 (2022) 115315. .

M.K. Purkait, M. Sharma, P.P. Das, C.-T. Chang, Potential for power production using salinity gradients, Blue Energy Extraction Using Salinity Gradients (2024) 1-26. .

M. Sharma, P.P. Das, A. Chakraborty, M.K. Purkait, Clean energy from salinity gradients using pressure retarded osmosis and reverse electrodialysis: A review, Sustainable Energy Technologies and Assessments 49101687. .

S. Chae, H. Kim, J. Gi Hong, J. Jang, M. Higa, M. Pishnamazi, J.Y. Choi, R. Chandula Walgama, C. Bae, I.S. Kim, J.S. Park, Clean power generation from salinity gradient using reverse electrodialysis technologies: Recent advances, bottlenecks, and future direction, Chemical Engineering Journal 452 (2023) 139482. .

M. Malankowska, Z. Su, K. Karlsen, M. Flaskjær Buhl, H. Guo, L.S. Pedersen, M. Pinelo, Novel membrane modifications for pressure retarded osmosis as a new way for sustainable power generation from salinity gradients, Chem Eng Sci 297 (2024) 120221. .

O.A.H. AL-Musawi, A.W. Mohammad, H.B. Mahood, E. Mahmoudi, W.L. Ang, A.A.H. Kadhum, Energy Comparison and Cost Estimation of Pressure-Retarded Osmosis Using Spiral Wound Membrane, Desalination Water Treat (2024) 100732. .

Q.A. Khasawneh, B. Tashtoush, A. Nawafleh, B. Kan'an, Techno-Economic Feasibility Study of a Hypersaline Pressure-Retarded Osmosis Power Plants: Dead Sea-Red Sea Conveyor, Energies (Basel) 11 (2018) 3118. .

K. Matsuyama, R. Makabe, T. Ueyama, H. Sakai, K. Saito, T. Okumura, H. Hayashi, A. Tanioka, Power generation system based on pressure retarded osmosis with a commerciallyavailable hollow fiber PRO membrane module using seawater and freshwater, Desalination 499 (2021) 114805. .

E.I. Obode, A. Badreldin, S. Adham, M. Castier, A. Abdel-Wahab, Techno-Economic Analysis towards Full-Scale Pressure Retarded Osmosis Plants, Energies (Basel) 16 (2022) 325. .

D. Dardor, M. Al Maas, J. Minier-Matar, A. Janson, A. Abdel-Wahab, H.K. Shon, S. Adham, Evaluation of pretreatment and membrane configuration for pressure-retarded osmosis application to produced water from the petroleum industry, Desalination 516 (2021) 115219. .

G. O'Toole, L. Jones, C. Coutinho, C. Hayes, M. Napoles, A. Achilli, River-to-sea pressure retarded osmosis: Resource utilization in a full-scale facility, Desalination 389 (2016) 39-51. .

M. Tagliavini, M.U. Babler, Simulation of spiral-wound pressure retarded osmosis for harvesting osmotic power: Module-level modeling and implications of feed pre-treatment, Desalination 574 (2024) 117184. .

T.M. Mansour, T.M. Ismail, K. Ramzy, M. Abd El-Salam, Energy recovery system in small reverse osmosis desalination plant: Experimental and theoretical investigations, Alexandria Engineering Journal 59 (2020) 3741-3753. .

A. Naghiloo, M. Abbaspour, B. Mohammadi-Ivatloo, K. Bakhtari, Modeling and design of a 25 MW osmotic power plant (PRO) on Bahmanshir River of Iran, Renew Energy 78 (2015) 51-59. .

M. Malankowska, Z. Su, K. Karlsen, M. Flaskjær Buhl, H. Guo, L.S. Pedersen, M. Pinelo, Novel membrane modifications for pressure retarded osmosis as a new way for sustainable power generation from salinity gradients, Chem Eng Sci 297 (2024) 120221. .

S.N. Rahman, H. Saleem, S.J. Zaidi, Progress in membranes for pressure retarded osmosis application, Desalination 549 (2023) 116347. .

J.H. Low, J. Zhang, W.P. Li, T. Yang, C.F. Wan, F. Esa, M.S. Qua, K. Mottaiyan, S. Murugan, M. Aiman, A. Dhalla, T.S. Chung, C. Gudipati, Industrial scale thin-film composite membrane modules for salinity-gradient energy harvesting through pressure retarded osmosis, Desalination 548 (2023) 116217. .

Y. Kim, G. Lee, J. Byun, S. ho Lim, S. Lee, Y.G. Park, Optimizing energy efficiency in desalination: Performance evaluation of seawater reverse osmosis and pressure retarded osmosis hybrid systems, Desalination 601 (2025) 118557. .

S.J. Moon, S.M. Lee, J.H. Kim, S.H. Park, H.H. Wang, J.H. Kim, Y.M. Lee, A highly robust and water permeable thin film composite membranes for pressure retarded osmosis generating at 21 bar, Desalination 483 (2020) 114409. .

A. Shadravan, M. Amani, A. Jantrania, Feasibility of thin film nanocomposite membranes for clean energy using pressure retarded osmosis and reverse electrodialysis, Energy Nexus 7 (2022) 100141. .

W. Gai, D.L. Zhao, T.S. Chung, Novel thin film composite hollow fiber membranes incorporated with carbon quantum dots for osmotic power generation, J Memb Sci 551 (2018) 94-102. .

Y. Shi, M. Zhang, H. Zhang, F. Yang, C.Y. Tang, Y. Dong, Recent development of pressure retarded osmosis membranes for water and energy sustainability: A critical review, Water Res 189 (2021) 116666. .

M. Javadi Azad, A.R. Pouranfard, D. Emadzadeh, W.J. Lau, E. Alipanahpour Dil, Simulation of forward osmosis and pressure retarded osmosis membrane performance: Effect of TiO2 nanoparticles loading on the semi-permeable membrane, Comput Chem Eng 160 (2022) 107709. .

Y.Y. Liang, Review of analytical and numerical modeling for pressure retarded osmosis membrane systems, Desalination 560 (2023) 116655. .

F.J. Aschmoneit, C. Hélix-Nielsen, Submerged-helical module design for pressure retarded osmosis:A conceptual study using computational fluid dynamics, J Memb Sci 620 (2021) 118704. .

G. Han, S. Zhang, X. Li, T.S. Chung, Progress in pressure retarded osmosis (PRO) membranes for osmotic power generation, Prog Polym Sci 51 (2015) 1-27. .

S. Yagnambhatt, S. Khanmohammadi, J. Maisonneuve, Demonstration of a real-time maximum power point tracker for salt gradient osmotic power systems, Appl Energy 376 (2024) 124205. .

A. Shadravan, M. Amani, A. Jantrania, Feasibility of thin film nanocomposite membranes for clean energy using pressure retarded osmosis and reverse electrodialysis, Energy Nexus 7 (2022) 100141. .

F. Helfer, C. Lemckert, Y.G. Anissimov, Osmotic power with Pressure Retarded Osmosis: Theory, performance and trends - A review, J Memb Sci 453 (2014) 337-358. .

H.W. Chung, J. Swaminathan, L.D. Banchik, J.H. Lienhard, Economic framework for net power density and levelized cost of electricity in pressure-retarded osmosis, Desalination 448 (2018) 13-20. .

D. Li, Z. Mo, A.G. Fane, Q. She, A multifunctional desalination-osmotic energy storage (DOES) system for managing energy and water supply, Desalination 581 (2024) 117608. .

F. Di Michele, E. Felaco, I. Gasser, A. Serbinovskiy, H. Struchtrup, Modeling, simulation and optimization of a pressure retarded osmosis power station, Appl Math Comput 353 (2019) 189-207. .

N. AlZainati, S. Yadav, A. Altaee, S. Subbiah, S.J. Zaidi, J. Zhou, R.A. Al-Juboori, Y. Chen, M.H. Shaheed, Impact of hydrodynamic conditions on optimum power generation in dual stage pressure retarded osmosis using spiral-wound membrane, Energy Nexus 5 (2022) 100030. .

A. Das, A.K. Rao, S. Alnajdi, D.M. Warsinger, Pressure exchanger batch reverse osmosis with zero downtime operation, Desalination 574 (2024) 117121. .

J.R. Couper, W.R. Penney, J.R. Fair, S.M. Walas, Chemical Process Equipment: Selection and Design, 2nd ed., Gulf Professional Publishing, 2005.

M.S. Peters, K.D. Timmerhaus, R.E. West, Plant Design and Economics for Chemical Engineers, 5th ed., McGraw-Hill Chemical Engineering Series, 2003.

R. Makabe, T. Ueyama, H. Sakai, A. Tanioka, Commercial Pressure Retarded Osmosis Systems for Seawater Desalination Plants, Membranes (Basel) 11 (2021) 69. .

I. Ibrar, S. Yadav, O. Naji, A.A. Alanezi, N. Ghaffour, S. Déon, S. Subbiah, A. Altaee, Development in forward Osmosis-Membrane distillation hybrid system for wastewater treatment, Sep Purif Technol 286 (2022) 120498. .

A. Fadaei, Y. Noorollahi, P. Pakzad, H. Yousefi, Development, 4E-analysis, and optimization of a seawater thermal energy-driven desalination system based on seawater source heat pump, multi-effect desalination, and pressure retarded osmosis with reduced effluent concentration, Energy Convers Manag 298 (2023) 117746. .

J. Xu, Y. Liang, X. Luo, J. Chen, Z. Yang, Y. Chen, Towards cost-effective osmotic power harnessing: Mass exchanger network synthesis for multi-stream pressure-retarded osmosis systems, Appl Energy 330 (2023) 120341. .

N. Saddari, N.S. Agyemang Derkyi, F. Peprah, Technical and Economic analysis of solar PV electricity generation under the net metering scheme at Sunyani Teaching Hospital (STH), Ghana, Renewable and Sustainable Energy Transition 6 (2025) 100097. .

F. Kyeremeh, Z. Fang, F. Liu, F. Peprah, Techno-economic analysis of reactive power management in a solar PV microgrid: A case study of Sunyani to Becheam MV feeder, Ghana, Energy Reports 11 (2024) 83-96. .

N.H. Leite, C.P. Guzman Lascano, H.G. Valente Morais, L.C. Pereira da Silva, Impact of the net-metering policies on solar photovoltaic investments for residential scale: A case study in Brazil, Renew Energy 231 (2024) 120788. .

S.M. Matta, M.A. Selam, H. Manzoor, S. Adham, H.K. Shon, M. Castier, A. Abdel-Wahab, Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes, Renew Energy 189 (2022) 66-77. .

S. Lee, Y.C. Kim, S.-J. Park, S.-K. Lee, H.-C. Choi, Experiment and modeling for performance of a spiral-wound pressure-retarded osmosis membrane module, Desalination Water Treat 57 (2016) 10101-10110. .

S.J. Einarsson, L. Guan, L.N. Sim, T.H. Chong, B. Wu, Fouling behaviours and mitigation in pressure-retarded osmosis processes with geothermal water/brine-based draw solutions, Journal of Water Process Engineering 51 (2023) 103485. .

N.A. Pham, D.Y.F. Ng, K. Goh, Z. Dong, R. Wang, Assessing the potential of integrally skinned asymmetric hollow fiber membranes for addressing membrane fouling in pressure retarded osmosis process, Desalination 520 (2021) 115347. .

S. Liu, W. Song, M. Meng, M. Xie, Q. She, P. Zhao, X. Wang, Engineering pressure retarded osmosis membrane bioreactor (PRO-MBR) for simultaneous water and energy recovery from municipal wastewater, Science of The Total Environment 826 (2022) 154048. .

A.S.M.M. Hasan, P. Kesapabutr, B. Möller, Bangladesh's pathways to net-zero transition: Reassessing country's solar PV potential with high-resolution GIS data, Energy for Sustainable Development 81 (2024) 101511. .

O. Turkovska, K. Gruber, M. Klingler, C. Klöckl, L. Ramirez Camargo, P. Regner, S. Wehrle, J. Schmidt, Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning, One Earth 7 (2024) 1741-1759. .

Y. Wang, B. Liu, H. Peng, Y. Jiang, Locating the suitable large-scale solar farms in China's deserts with environmental considerations, Science of The Total Environment 955 (2024) 176911. .

D. Covelli, E. Virgüez, K. Caldeira, N.S. Lewis, Oahu as a case study for island electricity systems relying on wind and solar generation instead of imported petroleum fuels, Appl Energy 375 (2024) 124054. .

S. Castelluccio, S. Fiore, C. Comoglio, Environmental reporting in Italian thermal power plants: insights from a comprehensive analysis of EMAS environmental statements, J Environ Manage 359 (2024) 121035. .

R. Kumawat, L. Gidwani, K.B. Rana, Comparative analysis of life cycle assessment of biogaspowered and coal-powered power plant for optimized environmental operation, Heliyon 10 (2024) e39155. .

X. Jiang, X. Song, Q. Yu, J. Yang, T. Wang, H. Lu, J. Zeng, X. Guo, Effects of environmental changes on vegetation growth and macroinvertebrate communities of eelgrass meadows in Northern China, Glob Ecol Conserv 59 (2025) e03554. .

J. Theilen, V. Sarrazin, E. Hauten, R. Koll, C. Möllmann, A. Fabrizius, R. Thiel, Environmental factors shaping fish fauna structure in a temperate mesotidal estuary: Periodic insights from the Elbe estuary across four decades, Estuar Coast Shelf Sci 318 (2025) 109208. .

C.T. Shifa, S.K. Dayananda, X. Yanjie, K.A. Rubeena, S. Bin Muzaffar, A. Nefla, T. Jobiraj, P. Thejass, O.R. Reshi, K.M. Aarif, Long-term anthropogenic stressors cause declines in kingfisher assemblages in wetlands in southwestern India, Ecol Indic 155 (2023) 111062. .

G. Marchessaux, N. Barré, V. Mauclert, K. Lombardini, E.D.H. Durieux, D. Veyssiere, J.J. Filippi, J. Bracconi, A. Aiello, M. Garrido, Salinity tolerance of the invasive blue crab Callinectes sapidus: From global to local, a new tool for implementing management strategy, Science of The Total Environment 954 (2024) 176291. .

F.D. Rosete, Cagayan de Oro leaks cost water district over P700 million in 2022 alone, Rappler (2024).

Department of Environment and Natural Resources (DENR), Water Quality Guidelines and General Effluent Standards of 2016 , (n.d.). (accessed April 13, 2025).

M. Kurihara, H. Takeuchi, SWRO-PRO System in "Mega-ton Water System" for Energy Reduction and Low Environmental Impact, Water (Basel) 10 (2018) 48. .

D.A. Lemley, M. Nunes, J.B. Adams, G.C. Bate, Regional-scale assessment of eutrophic condition indicators in subtropical temporarily closed estuaries, Estuar Coast Shelf Sci 304 (2024) 108850. .

L. Van Niekerk, J.B. Adams, D.G. Allan, S. Taljaard, S.P. Weerts, D. Louw, C. Talanda, P. Van Rooyen, Assessing and planning future estuarine resource use: A scenario-based regionalscale freshwater allocation approach, Science of The Total Environment 657 (2019) 10001013. .

J. Day, R. Lane, M. Moerschbaecher, H.C. Clark, M. Allison, E. Meselhe, A.S. Kolker, R. Hunter, P. Kemp, J.Y. Ko, R. Twilley, J.R. White, R. DeLaune, J. Stephens, C. Chenevert, E.F. Sanchez, D. Sinha, Patterns and mechanisms of wetland change in the Breton sound estuary, Mississippi River delta: A review, Estuar Coast Shelf Sci 313 (2025) 109065. .

L. Reichenberg, F. Hedenus, M. Odenberger, F. Johnsson, The marginal system LCOE of variable renewables - Evaluating high penetration levels of wind and solar in Europe, Energy 152 (2018) 914-924. .

W.Y. Chia, S.R. Chia, K.S. Khoo, K.W. Chew, P.L. Show, Sustainable membrane technology for resource recovery from wastewater: Forward osmosis and pressure retarded osmosis, Journal of Water Process Engineering 39 (2021) 101758. .

M. Llamas-Rivas, A. Pizano-Martínez, C.R. Fuerte-Esquivel, L.R. Merchan-Villalba, J.M. Lozano-García, E.A. Zamora-Cárdenas, V.J. Gutiérrez-Martínez, Pressure Retarded Osmosis Power Units Modelling for Power Flow Analysis of Electric Distribution Networks, Energies (Basel) 14 (2021) 6649. .

O. Alıç, A holistic techno-economic feasibility analysis of residential renewable energy systems: An insight into Turkish case, J Energy Storage 94 (2024) 112433. .

G. Aquila, E. de O.P. Coelho, B.D. Bonatto, E. de O. Pamplona, W.T. Nakamura, Perspective of uncertainty and risk from the CVaR-LCOE approach: An analysis of the case of PV microgeneration in Minas Gerais, Brazil, Energy 226 (2021) 120327. .

D. Roy, S. Zhu, R. Wang, P. Mondal, J. Ling-Chin, A.P. Roskilly, Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models, Appl Energy 361 (2024) 122884. .

M. Đukan, D. Gut, A. Gumber, B. Steffen, Harnessing solar power in the Alps: A study on the financial viability of mountain PV systems, Appl Energy 375 (2024) 124019. .

J.Y. Ozato, G. Aquila, E. de Oliveira Pamplona, L.C.S. Rocha, P. Rotella Junior, Offshore wind power generation: An economic analysis on the Brazilian coast from the stochastic LCOE, Ocean Coast Manag 244 (2023) 106835. .

A. Ioannou, A. Angus, F. Brennan, Stochastic Prediction of Offshore Wind Farm LCOE through an Integrated Cost Model, Energy Procedia 107 (2017) 383-389. .

C.M.M.R.S. Silva, P.N.D. Premadasa, D.P. Chandima, J.P. Karunadasa, P. Wheeler, Optimum sizing and configuration of electrical system for telecommunication base stations with grid power, Li-ion battery bank, diesel generator and solar PV, J Energy Storage 123 (2025) 116704. .

S. Motiwala, S. Kumar, A.K. Sharma, I. Purohit, Enhancing performance of operational utilityscale solar PV projects in India through re-powering: Potential and techno-economic assessment, Energy for Sustainable Development 83 (2024) 101574. .

M. Daoudi, I. Daoudi, A. Idrissi, I. Ihoume, N. Arbaoui, O. Ben lenda, A. Riad, Analyzing life cycle costs and carbon emission reduction of a first offshore wind farm in africa under the climatic conditions of Morocco: Case study, Physics and Chemistry of the Earth, Parts A/B/C 139 (2025) 103937. .

International Renewable Energy Agency (IRENA), Renewable Capacity Statistics 2024, Abu Dhabi, 2024.

X. Hu, A. Elshkaki, L. Shen, The implications of circular economy strategies on the future energy transition technologies and their impacts: Solar PV as a case study, Energy 313 (2024) 133972. .

S. Preet, S.T. Smith, A comprehensive review on the recycling technology of silicon based photovoltaic solar panels: Challenges and future outlook, J Clean Prod 448 (2024) 141661. .

J.C. Garvin, J.L. Simonis, J.L. Taylor, Does size matter? Investigation of the effect of wind turbine size on bird and bat mortality, Biol Conserv 291 (2024) 110474. .

A. Moustakas, P. Georgiakakis, E. Kret, E. Kapsalis, Wind turbine power and land cover effects on cumulative bat deaths, Science of The Total Environment 892 (2023) 164536. .

Y. Zhao, M. Wang, J. Lin, W. Liu, L. Chen, Z. Wang, B. Sun, X. Li, Exploring recycling strategies for retired wind turbine blades: The impact of policy subsidies and technological investments using a game-theoretic approach, J Clean Prod 490 (2025) 144628. .

M.R. Elkadeem, A. Younes, J. Jurasz, A.S. AlZahrani, M.A. Abido, A spatio-temporal decision-making model for solar, wind, and hybrid systems - A case study of Saudi Arabia, Appl Energy 383 (2025) 125277. .

V. Kati, C. Kassara, P. Panagos, L. Tampouratzi, D. Gotsis, O. Tzortzakaki, M. Petridou, M. Psaralexi, L. Sidiropoulos, D. Vasilakis, S. Zakkak, A. Galani, N. Mpoukas, The overlooked threat of land take from wind energy infrastructures: Quantification, drivers and policy gaps, J Environ Manage 348 (2023) 119340. .

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 63-73

doi: 10.4028/p-JoB4sY

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-02-24

Revised:
2025-09-29

Accepted:
2025-10-09

Online:
2026-01-07

Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

Supriyadi , Nita Oni Indriani and Khumaedi

Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Sekaran, Gunungpati, Semarang City, Central Java, Indonesia

supriyadi@mail.unnes.ac.id, nitaoniindriani@gmail.com, medisas@mail.unnes.ac.id

Keywords: GGMplus, FHD, SVD.

Abstract

Semarang is located in the northern part of Java Island where two tectonic plates meet, making it seismically active and causing the formation of faults. Kaligarang river valley is estimated to be an active fault. The research used FHD and SVD derivative methods, and FFD as a gravity continuation method in the Semarang City area to determine the structure of the Kaligarang fault based on GGMplus satellite data. The derivative analysis was obtained from the residual anomaly. CBA contour map is performed to obtain regional and residual anomaly contour map with moving average method. Furthermore, 2D forward modeling was carried out to describe the layers of geological structures by determining the depth using spectrum analysis. The subsurface structure in the study area consists of 2 types of formations in each of the Line 1, 2 and 3 models, namely the Kaligetas Formation which has an average density of . Then in the second layer is the Kalibeng Formation with an average density range of . Meanwhile, the line 4 modeling is dominated by the Kerek Formation with an average density of . The fault structure boundaries identified based on FHD analysis are located around coordinates 9215536.4 UTM Y on Line 1, 9217402.4 UTM Y on Line 2, 9220103.6 UTM Y on Line 3, and 9221429.6 UTM Y on Line 4. Based on the SVD analysis, the type of faults that can be identified along Kaligarang River is a thrust fault.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

Semarang City is one of the most populous cites on the island of Java, located at the meeting point of two tectonic plates The Eurasian Plate and the Indo-Australian Plate. This makes Java Island very seismically active, resulting in many faults. A fault zone is a fracture plane in rock that is subject to displacement. Movement on an active fault will cause earthquakes. Active fault displacements are relatively small or large. Faults can be categorized into several types based on the movement of hanging wall and footwall This makes Java Island very seismically active, resulting in many faults. A fault zone is a fracture plane in rock that is subject to displacement. Movement on an active fault will cause earthquakes. Active fault displacements are relatively small or large. Faults can be categorized into several types based on the movement of hanging wall and footwall [1] that is Thrust fault, Reverse Fault and Strike-Slip Fault Structure.

The geological structure of Semarang city according to the geological map of MagelangSemarang sheet and its surroundings has a stratigraphic sequence from the oldest to the youngest, that is [2]:
a. Alluvium

This layer consists of deposits derived from alluvium originating from lakes, rivers, and beaches during the Holocene. Coastal lithology deposits consist of old silt, clay, and sand with a thickness of up to 50 meters or more.
b. Gajah Mungkur and Keligesik Volcanic Rocks

The rock layer consists of lava and rocks from Kaligesing Volcano in the form of gray-black basaltic lava.
c. Damar Formation

The Damar Formation layer consists of volcanic breccia, tuff sandstone, and conglomerate. The tuff sandstone is fine-coarse grained and brownish yellow in color.
d. Kaligetas Formation

The Kaligetas Formation layers are lava and breccia with fine to coarse tuff inserts and lava, at the bottom in the form of claystone containing sandstone tuff and molluscs.
e. Kalibeng Formation

The layers consist of limestone, marl, and and tuff sandstone.
f. Kerek Formation

The Kerek Formation consists of interbedded claystone, limestone, marl, tuff, sandstone, conglomerate, and volcanic breccia.

The Kaligarang River through the Semarang City area from north to south. The Kaligarang River valley is thought to have been an active fault since the Tertiary to Quaternary periods [3]. The appearance of faults in the Kaligarang river is characterized by the presence of offset river flows and undulating structures along with fault ridges are some of the evidence that tectonic activity is still active in the region. Semarang City is one of the areas that is rarely affected by earthquakes, because it is located far from the epicenter of the earthquake originating from the southern Java subduction. Relatively small earthquakes usually occur because they originate from deep sources. However, this does not mean that Semarang City will be spared from large earthquakes because the source of earthquakes can be caused by active fault movements with shallow epicenters.

Based on these facts, further research was conducted to describe the subsurface geological structure in the area using the gravity method. Gravity method principle works based on the difference gravity field caused by difference in rock density below the earth's surface. The development of technology on the magnitude of the value of gravity on the earth's surface can be measured using satellites. Satellite gravity anomalies are the result of the development of the relative gravity method. The satellite used in this research is GGMplus 2013. GGMplus (Global Gravity Model Plus) 2013 is an earth gravity model generated from GRACE and GOCE satellite data, EGM 2008, and gravity topography effects with resolution not enough more than 200 meter [4].

Research conducted by Indriana [5] on gravity inversion modeling using satellite data inversion has not been able to prove the existence of the Kaligarang fault. Therefore, research was conducted using the First Horizontal Derivative (FHD), Second Vertical Derivative (SVD) and (Fault Fracture Density) FFD methods as advanced gravity methods to determine the structure of the Kaligarang fault along the Kaligarang river. Further analysis is obtained from Complete Bouguer Anomaly (CBA).

Methodology

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

The method used in this research is the gravity method using the 2013 GGMplus Gravity field satellite data. Research area on rivers Kaligarang Semarang City which is carried out at UTM coordinates namely East and North with wide area research and the amount of data obtained was 3969 points.

The initial stage in this research will be a literature study on the regional geology of the area, then data collection is carried out by free access to GGMpuls and DEM data. The data obtained is in the form of Gobs, then a gravity correction is carried out to obtain CBA. The existing GGMplus gravity anomaly data corrected tides and corrections float, so for stage continued that is correct normal gravity, correction air free, bouguer correction and correction terrain.

Stages second for obtained a CBA with determination estimation average density of rocks in the area of use Nettleton method. The nettleton method is based on bouguer correction and terrain correction if applied density in accordance with density surface, then profile anomaly its gravity will become more fine[6].

Stage third that is regional anomalies and residual anomalies are separated use method moving average using MVA matrix 33X33 for get regional anomalies. Digitization process done
and obtained UTM X and UTM Y coordinates, and CBA values. Furthermore, determine depth of every anomaly with an analysis spectrum in one dimensions and use fourier transform with change signal to summation a number of signals[7]. Analysis method spectrum change to in row fourier distance/space domain become number wave or frequency for get k and values. The k and Ln A values are plotted to form a curve analysis spectrum so that can determine depth of each zone with a gradient graph. Analysis data spectrum obtained from results slicing on the CBA anomaly map[8].

Stage fourth is determining structural boundaries fault and types fault use FHD and SVD analysis. The FHD method has also been used to identify fault [9, 10]. Following equality for determine FHD value:

The SVD method is used for known type structure fault [11, 12, 13, 14]. Equality For determine derived SVD value second is as following:

Structure fault on the resulting SVD graph digitization will show results SVD value is zero or approach zero, while the type of fault will be known from maximum and minimum absolute values. Determination type fault as following:

Normal fault

Reserves fault or thrust fault

Strike-Slip fault

Digitization results residual maps and FHD and SVD graphs are used as input for 2D modeling forward modeling. 2D modeling was carried out on 4 profiles CBA incision with trajectory Same digitization on FHD and SVD. Geosoft oasis montaj software used for enter slice data for do forward modeling using GM-SYS tool.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

Determining process density in the area research in Table 1, then served graph as in Figure 1 below this:

Fig. 1. Density Nettleton method.

Fig. 1. Density Nettleton method.

Table 1. Coefficient correlation Bouguer anomaly of profile topography.

Density
Assumption
(g/cm³)
Correlation
Coefficient

1
0.929484

1.2
0.912527

1.4
0.889183

1.6
0.85615

1.8
0.808042

2
0.736122

2.2
0.62698

2.4
0.463687

2.6
0.236309

2.8
-0.0356

3
-0.29878

3.2
-0.50761

3.4
-0.65371

3.6
-0.75093

3.8
-0.81548

4
-0.8592

From Figure 1 it can be seen mark density used as mark average density in the area study of , as assumption resulting density mark small correlation can be seen in Table 1. The density values obtained used in the process of processing gravity data for get CBA value.

Gravity data already obtained then done correct normal gravity, correction air free, bouguer correction and correction terrain. Gravity data has been done correct will produce a map CBA contour in Figure 2. CBA contour map in survey gravity is combined to map regional anomalies in Figure 3 and map residual anomaly in Figure 4 located below surface area study with variation level frequency.

Fig. 2. CBA contour map.

Fig. 2. CBA contour map.

CBA contour map that has been obtained in Figure 2 later made trajectory as many as 6 tracks for represent all areas of the region research, nemely the track until with for done calculation analysis spectrum.

Table 2. Depth Use Spectrum Analysis.

Trajectory | Regional Depth (m) | Residual Depth (m) | k cut off | Lamda | Window Width
AA' | 1055.900 | 152.500 | 0.00145 | 4323.758 | 21.619
BB' | 1195.600 | 175.900 | 0.00077 | 8201.439 | 41.007
CC' | 1134.000 | 217.070 | 0.00067 | 9342.048 | 46.710
DD' | 631.330 | 107.540 | 0.00101 | 6196.704 | 30.984
EE' | 811.470 | 212.360 | 0.00085 | 7433.490 | 37.167
FF' | 758.200 | 53.028 | 0.00132 | 4765.247 | 23.826
Average | 931.083 | 153.066 | 0.00101 | 6710.448 | 33.552

In Table 2 it is found the average depth of regional anomalies with depth 931.083 m and depth residual anomaly with depth 153.066 meter. Separation regional anomalies and residual anomalies using a moving average filter use use MVA matrix 33 X33 with Surfer software. Separation this aims for know pattern anomalies at each depth. Use of moving average filters for get map contour regional anomalies in Figure 3 and map contour residual anomaly in Figure 4.

Fig. 3. Regional anomaly map results separation with MVA.

Fig. 3. Regional anomaly map results separation with MVA.

Fig. 4. Residual anomaly map of results separation with MVA.

Fig. 4. Residual anomaly map of results separation with MVA.

The next process is carried out after SVD and FHD analysis that is modeling forward modeling 2D using geosoft oasis montaj software using the GM-SYS tool. Trial and error process often used in modeling this. The resulting effect on modeling This with do calculation derived mathematics from condition lower surface, so exists changes in model parameters with level possible correlation accepted based on comparison of residual anomalies. Modeling carried out on the trajectory Lines 1, 2, 3, and 4 on the map contour residual anomaly Figure 4. This direction is digitization, namely west-east, because of the Kaligarang fault location which has the north-south direction of Semarang city.

Analysis determination fault in research this uses FHD analysis and SVD analysis. FHD analysis was used for determinining the boundaries of the structure faults in the research area with digitization of the FHD map is presented in Figure 5. Next A purposeful FHD graphic plot is carried out to determine location fault with the observed mark maximum or highest on FHD graphics. Making FHD graphics based trajectory digitization carried out on the residual grid of the traversed area fault in Figure 4. Trajectory digitizing the map FHD contours have direction transverse to traversed area fault throughout river Kaligarang. In the picture we have done trajectory digitization as many as 4 in the residual map, so obtained UTM X, UTM Y coordinates, value gravity anomaly each residual grid, FHD.

Fig. 5. Contour map FHD anomaly.

Fig. 5. Contour map FHD anomaly.

Indication exists that structure fault use SVD analysis can be visible in contrast between anomaly high and anomalous low over short distances in the study area. In Figure 6 it is SVD map that performs digitization as many as 4 tracks for known existence fault in the area that. Correlation in research this analysis generated SVD graph from the digitization process. Structure fault on the SVD graph of digitization showing SVD value is zero or approaching zero. Whereas for determine type fault can is known through analysis SVD charts based on mark absolute maximum and value absolute minimum.

Fig. 6. Contour map SVD anomaly.

Fig. 6. Contour map SVD anomaly.

Modeling carried out on the trajectory Lines 1, 2, 3, and 4 on the map contour residual anomaly. Position digitization in modeling forward modeling is the same with digitization carried out when analysis faults on the map FHD and SVD contours. Digitization was carried out on the residual anomaly grid in 4 passes so that resulting modeling as many as 4 tracks with direction digitization transverse location fault. This direction digitization namely west-east, because of the Kaligarang fault location which has the north-south direction of Semarang city.

Fig. 7. 2D Forward Modeling on Line 1.

Fig. 7. 2D Forward Modeling on Line 1.

Location of structure boundaries fault on the track digitization Line 1 seen in Figure 7 has a distance 1658.31 meters from the point of digitization. Fault type indicated SVD graph indicated as thrust fault. layer on that is Kaligetas formation. Kaligetas formation arranged from the resulting material results in activity volcanic form lava and lava flows, breccia volcanic and sandstone tuff with range average density of sand tuff . Second layers is Kalibeng formation consists of gravel, sand, silt and clay with a range the average density is . Identified faults based on FHD graphics and SVD graphics are available on the track This is located at coordinates between antara 434283.6 UTM X dan 9215536.4 UTM Y. Error obtained on this track is 1.988 .

Fig. 8. 2D Forward Modeling on Line 2.

Fig. 8. 2D Forward Modeling on Line 2.

Structure boundaries faults identified on the graph This seen with a marked FHD anomaly on the FHD graph at a distance of 940.44 meters from point did digitization. Thrust faults are identified faults on the track digitized Line 1 is visible in Figure 8. First layers that is Kaligetas formation. Kaligetas formation arranged from the resulting material activity volcanic form lava and lava flows, breccia volcanic and sandstone tuff with range average density of sand tuff . Lava formation deposits form in the environment. Then on layers second is Kalibeng formation consists of gravel, sand, silt and clay with range the average density is . Fault location on the track this based on FHD and SVD graphs are at coordinates 433905.6 UTM X dan 9217402.4 UTM Y. Error obtained on this track is 3.279 .

Fig. 9. 2D Forward Modeling on Line 3.

Fig. 9. 2D Forward Modeling on Line 3.

The existence of structural boundaries indicated fault with mark maximum FHD anomaly and zero SVD value or approach zero at a distance of 1266.46 meters from point did digitization. Types of faults identified on the track this is thrust fault. First layrs is Kaligetas formation. Kaligetas formation arranged from the resulting material activity volcanic form lava and lava flows, breccia volcanic and sandstone tuff with range average density of sand tuff . Formation this is in part lower there are clay stones that contain molluscs. Then on layers second is Formation Kalibeng consists of gravel, sand, silt and clay with range the average density is . Error obtained on this track is 2.608 . Location identified fault on the track Line 3 shown in Figure 9 is at coordinates between 434387.9 UTM X and 9220103.6 UTM Y.

Fig. 10. 2D Forward Modeling on Line 4.

Fig. 10. 2D Forward Modeling on Line 4.

Identified structural boundaries fault trajectory Line 4 in Figure 10 with the maximum FHD value and the SVD value are close to zero or zero at a distance of 1307.21 meters from point digitization. Types of faults identified on the track this is thrust fault. Layer first is the Kerek formation is formation oldest from location research. The Kerek formation consists of mudstone, marl, sandstone tuff, conglomerate, breccia volcanic, and limestone with range the average density is . Fault location This is at the coordinates between 433676.9 UTM X dan 9221429.6 UTM Y. Error obtained on this track is 4.023, error on the track This is the biggest compared to trajectory others.

Interpretation results obtained showing suitability with study previously carried out by [15] for determine kinematics and dynamics of the Kaligarang fault zone in Semarang and research conducted by [16] which explains studies paleoseismology consequence disaster earthquake earth in the Kaligarang Fault zone in Semarang. Based on research conducted use FHD and SVD methods are proven effective to determine the boundaries of the structure fault and types fault Kaligarang.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

FHD and SVD analysis are methods used to determine the types of subsurface structure in the area reseach, which consists of 2 types formation in each modeling. Line 1, 2 and 3 on the top layers is Formation Kaligetas arranged from the resulting material activity volcano like lava and lava flows, breccia volcanic and sandstone tuff with range average density of . Then on second layers is Formation Kalibeng consists of gravel, sand, silt and clay with a range the average density of . The modeling Line 4 is dominated by the Kerek Formation with an average density of . Types of faults that can occur identified throughout river Kaligarang is Thrust fault or Reserves fault.

References

The original version of this paper is available on https://www.scientific.net/EI.16.63.pdf

Ryanto, T. A., Suntoko, H. & Setiaji, A. B. W. Pendugaan Awal Patahan di Pulau Jawa Menggunakan Anomali Gravitasi dan Riwayat Kegempaan. Eksplorium 40, 43 (2019).

Wardhana, D. D., Harjono, H. & Sudaryanto, S. Struktur Bawah Permukaan Kota Semarang Berdasarkan Data Gayaberat. Jurnal RISET Geologi dan Pertambangan 24, 53 (2014).

Fattah, B. Y. A., Sabri, L. M. & Awaluddin, M. Survei Deformasi Sesar Kaligarang Dengan Metode Survei GNSS Tahun 2019. Departemen Teknik Geodesi Fakultas Teknik Universitas Diponegoro 9, 102-112 (2020).

Agustin, N. & Wibawa, A. Analisis Data Gravitasi Untuk Identifikasi Struktur Bawah Permukaan Daerah Potensi Panas Bumi Cipari. Jambura Geoscience Review 4, 22-32 (2022).

Indriana, R. D., Nurwidyanto, M. I. & Widada, S. Re-modeling kaligarang fault base on satellite gravity data. Journal of Physics: Conference Series 1943, (2021).

Yuliastuti, Y., Suntoko, H. & Budi Susilo, Y. S. Bouguer Density Analysis using Nettleton Method at Banten NPP Site. Jurnal Pengembangan Energi Nuklir 19, 43 (2017).

Putri, M. & Annisa, N. Identifikasi Jenis Sesar Semangko Segmen Sunda di Tenggara Provinsi Lampung dan Barat Laut Provinsi Banten Menggnakan Metode Gravitasi Analisa Derivatif. Indonesia Sosial Sains 2, 936-948 (2021).

Margiono, R., Novitri, A., Pevriadi, A. & Zakariya, H. Analisis Data Gravitasi Untuk Identifikasi Sesar Lokal Penyebab Gempabumi Di Wilayah Barat Daya Sumba Indonesia Gravity Data Analysis To Identify Local Faults As Source of Earthquake in the Southwest Sumba Region Indonesia. Meteorologi dan Geofisika 22, 67-73 (2021).

Maulidah, H. et al. Identikasi Sesar Grindulu Dengan Memanfaatlan Metode Gravitasi. Inovasi Fisika Indonesia (IFI) 11, 20-27 (2022).

Zaenudin, A. & Yulistina, S. Studi Identifikasi Struktur Geologi Bawah Permukaan Untuk Mengetahui Sistem Sesar Berdasarkan Analisis First Horizontal Derivative (FHD), Second Vertical Derivative (SVD), Dan 2,5D Forward Modeling Di Daerah Manokwari Papua Barat. Jurnal Geofisika Eksplorasi 4, 62-76 (2020).

Sarkowi, M., Wibowo, R. C., Sawitri, R. F. & Mulyanto, B. S. Wai Selabung geothermal reservoir analysis based on gravity method. Jurnal Ilmiah Pendidikan Fisika Al-Biruni 10, 211-229 (2021).

Zaenudin, A., Karyanto, K., Kurniasih, A. & Wibowo, R. C. Analisis Struktur Patahan Daerah Suoh Menggunakan Metode Gaya Berat dan Penentuan Kerapatan Patahan. Positron 11, 95 (2021).

Zaenudin, A., Dani, I. & Amalia, N. Delineasi Sub-Cekungan Sorong Berdasarkan Anomali Gaya Berat. Jurnal Geocelebes 4, 14 (2020).

Setiadi, I. & Riyanda, A. R. Delineasi Cekungan Sedimen dan Interpretasi Geologi Bawah Permukaan Cekungan Tanibar Berdasarkan Analisis Data Gaya Berat. Jurnal Geologi dan Sumber Daya Mineral vol. 17 153-169 Preprint at (2016).

Fahrudin, Sadisun, I. A. & H, A. Studi Sesar Aktif, Kinematik, dan Dinamik Zona Sesar Kaligarang (KGFZ). Proceedings JCM Makassar 26-29 (2011).

Fahrudin. Studi Paleoseismologi Akibat Bencana Gempa Bumi Di Zona Sesar Kaligarang (KGZF) Di Semarang. Teknik 25-32 (2011).

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 75-95

doi: 10.4028/p-J80vYP

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-07-22

Revised:
2025-08-13

Accepted:
2025-08-20

Online:
2026-01-07

METEORO: Low-Cost IoT Weather Station

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Universidad Evangélica de El Salvador

douglas.aguilar@uees.edu.sv, maxg.sanchezf@gmail.com, jh3027d@gmail.com,

d*oskydiaz845@gmail.com

Keywords: IoT, meteorology, climate monitoring, ESP32, microcontrollers, weather station, lowcost, website.

Abstract

The development of low-cost, autonomous scientific instrumentation is crucial for enhancing environmental education and research, particularly in settings with limited resources. This paper presents METEORO v2, a low-cost Internet of Things (IoT) weather station designed to address the need for accessible scientific instrumentation in educational settings. The system overcomes common durability and energy autonomy challenges by integrating a robust physical structure with an optimized power management system. Architected around an ESP32 microcontroller utilizing a deep-sleep strategy and a 6W solar panel, the station provides continuous monitoring of precipitation, wind speed, UV radiation, air quality, temperature, humidity, and pressure. Data is transmitted via Wi-Fi to a Firebase cloud database and visualized through a responsive web interface. This work provides a replicable and affordable model for a sensing platform that serves as both an effective pedagogical tool and a reliable scientific instrument.

Douglas Adalberto Aguilar, Max Gabriel Sánchez Flores, Jhoan Daniel Martínez Marroquínand Oscar Eduardo Díaz Gómez

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The METEORO v2 project was conceived as an evolution of an earlier version (METEORO v1) that performed basic climate monitoring using low-cost sensors. The initial system was capable of measuring temperature, humidity, and atmospheric pressure, and served as a functional prototype for environmental data collection. However, it presented several limitations, particularly in terms of energy consumption, environmental durability, and the range of measured variables.

In a context of constant climatic variability, having an autonomous and reasonably accurate monitoring system can support environmental observation efforts within educational settings. This updated version expands the system's capabilities by adding measurements of wind speed, precipitation, UV radiation, and air quality, while also improving energy efficiency and usability for the university community[12].

To address the limitations of the original design, METEORO v2 incorporates a 6W solar panel for improved power management. The ESP32 microcontroller is configured to operate in deep sleep mode between readings, helping to reduce energy consumption. Physical modifications have also been made to the station's structure and sensor enclosures to better withstand weather exposure. The system monitors key climate parameters and includes a communication feature that enables real-time alerts about extreme weather conditions to be sent to students and university staff.

Background

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The evolution of meteorological monitoring has transitioned from manual, mechanical instruments to sophisticated electronic systems that offer high accuracy and automatic operation [1]. This progression has been significantly accelerated by the proliferation of the Internet of Things (IoT), a technological paradigm that enables the creation of low-cost, interconnected sensor networks, bridging the gap between complex environmental phenomena and tangible data [2,3,13]. This has led to the emergence of low-cost weather stations, which leverage affordable microcontrollers like the ESP32 and Raspberry Pi to create functional monitoring systems for educational and research purposes [4, 5, 7].

In the face of increasing climatic variability and extreme weather events, it is essential to rethink how environmental data is gathered and interpreted, especially within educational contexts. Traditional large-scale meteorological networks often lack the spatial precision needed to detect localized microclimates, an aspect of particular relevance in a university setting where specific research and learning activities may depend on site-specific conditions. Commercially available weather stations, while accurate, often lack the flexibility needed to adapt to the specific requirements of an educational or research setting. To address this problem, specialized weather stations have become indispensable tools for gathering high-resolution, in-situ meteorological data [8].

The application of these IoT-based stations is broad, ranging from microclimate monitoring on university campuses to enhancing smart agriculture and providing data for building energy consumption models [16, 19]. Such systems empower students and researchers to collect, analyze, and visualize real-time data on variables like temperature, humidity, and atmospheric pressure through accessible cloud platforms [10,15]. The integration of IoT in educational environments, particularly in STEM fields, provides invaluable hands-on experience, allowing students to engage with engineering practices, data analysis, and problem-solving in a real-world context [9, 14]. Furthermore, the use of renewable energy sources, such as solar panels, to power these remote stations is a critical aspect of modern designs, promoting sustainability and ensuring operational autonomy in diverse locations [24, 26, 28]. While the potential of these technologies is evident, their practical implementation reveals significant challenges that must be addressed to ensure their scientific validity and long-term viability.

Problem

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Many educational institutions, particularly in developing regions, face significant barriers to implementing such solutions due to the prohibitive cost of professional-grade scientific instrumentation [1]. Within this context, providing students with access to autonomous and reasonably accurate environmental monitoring systems can profoundly support observational efforts and project-based learning. This work details an updated version of a weather station designed specifically for the university community, expanding its measurement capabilities while enhancing its energy efficiency and usability as a pedagogical tool.

Despite the educational potential and widespread development of low-cost weather stations, a review of the literature reveals persistent technical challenges that limit their long-term effectiveness and academic reliability. A primary issue is the lack of durability; many do-it-yourself (DIY) designs utilize materials and enclosures that are not sufficiently robust for prolonged outdoor exposure, leading to sensor degradation and compromised data quality [6]. Furthermore, many reported systems struggle with energy autonomy, relying on inadequate solar power configurations that are insufficient for continuous, unattended operation, a critical requirement for remote monitoring tools [7,8]. This creates a necessity for a low-cost system that addresses these fundamental engineering challenges of durability and energy independence in a single, replicable framework.

Theoretical Framework

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The development of automated meteorological stations has evolved considerably with the incorporation of Internet of Things (IoT) technologies and renewable energy systems. These advances have enabled the creation of devices capable of real-time environmental monitoring with minimal human intervention, facilitating predictive analytics and allowing for rapid responses to climatic variations [26]. The combination of low-cost hardware and modern communication networks has made such systems more accessible for academic, governmental, and industrial applications.

The integration of environmental sensors with IoT platforms allows for the precise measurement of variables such as temperature, humidity, atmospheric pressure, and wind speed. Low-power communication protocols, such as LoRaWAN, optimize data transmission efficiency in remote or hard-to-reach areas [27]. This approach ensures that meteorological stations can operate
autonomously while transmitting accurate, timely information to centralized databases for further processing.

Energy autonomy plays a critical role in the operation of meteorological systems, particularly in isolated environments where conventional power supply is unavailable. Solar-powered systems significantly reduce operational costs, lower the environmental impact, and ensure uninterrupted data acquisition [28]. These systems leverage photovoltaic panels and energy storage units to maintain continuous functionality, even under challenging weather conditions.

Data processing in modern weather stations goes beyond simple data logging. Cloud-based storage solutions allow historical data comparison, trend analysis, and the implementation of predictive models [27]. This capability enables decision-makers to anticipate climatic events and adapt agricultural, industrial, or safety measures accordingly. Additionally, secure transmission protocols are essential to protect the integrity and confidentiality of environmental data.

Despite these advances, challenges remain in ensuring sensor calibration accuracy, enhancing hardware durability under extreme conditions, and implementing robust cybersecurity measures [26]. Future developments in meteorological stations should prioritize modular designs, advanced encryption standards for wireless communication, and increased resilience to environmental stress factors, ensuring long-term reliability and scalability of these systems.

Methodological Framework

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

This study employs a multi-phase applied research and development methodology to design, build, and validate the METEORO v2 weather station. The approach is structured to systematically address the core research objectives of creating a low-cost, energy-efficient, and durable monitoring system suitable for an educational environment.

The conceptual design of METEORO v2 was guided by a framework prioritizing solutions to the documented limitations of existing low-cost weather stations. The primary design requirements were derived from a synthesis of challenges identified in the literature, including power management, data accuracy, and long-term reliability [1, 7]. The following architectural principles were established to guide the development process:

Energy-First Design: The central architectural principle was the maximization of energy autonomy. This principle dictated the selection of a microcontroller with ultra-low-power deep-sleep capabilities, such as the ESP32-WROOM, and the design of a software architecture that minimizes active time. The solar power system was sized not merely for average daily operation but to ensure resilience during extended periods of low solar irradiation.

Modularity for Educational Use: The system was designed with a modular philosophy, both in hardware and software. This ensures that individual sensors can be easily replaced, calibrated, or upgraded without requiring a complete system overhaul. This flexibility is crucial in an educational context, as it allows the station to be used as an extensible platform for various student projects and interdisciplinary research [9,14].

Data Integrity and Accessibility: A data-centric approach was adopted, leveraging a scalable, cloud-based database (Firebase) for data aggregation. This choice aligns with modern IoT architectural patterns and ensures high data availability, integrity, and easy access for the university community through a web-based interface, thereby fulfilling a key objective of making scientific data accessible and engaging [10,15].

System Durability and Reliability: The long-term operational reliability of the system was assessed via a continuous 90 -day deployment in an unprotected outdoor environment, exposing it to direct sunlight, precipitation, and wind. System uptime was monitored through the continuous logging of data to the cloud database. After the test period, a qualitative physical inspection of the enclosure, solar radiation shield, and sensor housings was conducted to identify any signs of material degradation, such as water ingress, UV damage, or physical wear.

The selection of a university campus as the primary deployment and validation environment was a deliberate methodological choice. This setting provides an ideal testing ground, offering stable WiFi infrastructure and an engaged user base of students to test the web interface. The design of the
user-facing components was guided by the pedagogical goal of making complex environmental data accessible and understandable. The project's modular design is intended to position METEORO v2 as a platform for future student-led research, aligning with modern approaches to Science, Technology, Engineering, and Mathematics (STEM) education [14].

Proposed Structure

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The development of an affordable Internet of Things (IoT) weather station can be achieved through diverse methodologies, each with its own distinct objectives and capabilities. The aim of developing the station was to build a functional and useful prototype designed for installation in the university environment in which the project was carried out.

In light of these considerations, the team opted for an agile approach, facilitating its soon implementation within the university. This strategy enabled the progression from a general concept to more precise designs, facilitating the formulation of the desired functionality to be integrated into the university context, as well as the physical design of the prototype and the internal hardware.

As illustrated in Figure 1, this approach was fundamental to guiding the project and achieving results similar to the initial proposal, albeit with slight modifications aimed at maximizing the system's efficiency. From the beginning, the goal was to design an outdoor-use structure made of a durable material that would facilitate installing the weather station on poles. This would allow the station to be placed in an elevated position for accurately measuring different environmental variables.

Fig. 1. The project development process begins with the abstraction and conceptualization of the idea. (a) Digital sketching process. (b) Final prototype. (c) PVC structure assembly process.

Fig. 1. The project development process begins with the abstraction and conceptualization of the idea. (a) Digital sketching process. (b) Final prototype. (c) PVC structure assembly process.

Initially, we considered basic aspects such as temperature, humidity, wind, and rain, which we will discuss in greater detail later in this document. From that stage on, it was understood that sufficient space was needed to house the system and the components associated with the environmental variables, such as a solar radiation shield for accurate temperature and humidity readings, an anemometer for wind measurement, and a rain gauge for rain measurement. Additionally, the plans included a control box and a solar panel; all these components are essential for the device to function properly as design.

The system adopted a modular design that distributes different functions to specific areas within the structure, clearly organizing the necessary hardware for each sensor. The original design focused on making it strong enough to handle different types of challenges, while keeping costs low by using affordable materials.

Furthermore, one of the main goals of collecting these environmental variables is to send the data to a remote server using a free cloud database and web hosting service. This implementation enables students and academic staff to access daily weather data through a web application, which can be easily accessed via QR codes placed throughout the university. These codes link directly to the site where current and historical data is visualized. If enabled, users could also receive notifications when dangerous or abnormal environmental conditions are detected, enhancing the educational and preventative potential of the system. Throughout the research, the technical and structural details related to this architecture will be explored in greater depth.

Advantages and Disadvantages

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

While METEORO v2 shares similarities with other low-cost weather stations, such as the use of ESP32 microcontrollers and solar power, its comprehensive sensor suite and energy management system set it apart. For example, the rain gauge's interrupt-based wakeup feature ensured no precipitation events were missed, a functionality not commonly found in comparable systems. Additionally, the station's PVC-based structural design offered superior durability compared to the materials used in [6], which often degrade under prolonged exposure.

The METEORO v2 weather station achieves significant improvements in cost, functionality, and energy efficiency compared to existing low-cost solutions like the 110 USD system by Pourbafrani et al. [1]. While their design measures basic parameters (temperature, humidity, wind speed, and solar irradiance), METEORO v2 expands capabilities with UV radiation, air quality (MQ135), and precipitation sensors-all while maintaining comparable affordability through optimized component selection (e.g., 3D-printed anemometer, PVC enclosure)[23].

Energy management is a key differentiator: METEORO v2's ESP32 deep sleep mode and relaycontrolled sensor activation reduce consumption compared to continuous-operation systems [1]. This addresses the high-power limitations of air quality sensors, a challenge overlooked in the Pourbafrani design [1].

Although METEORO v2 demonstrates strong performance, its exclusive reliance on Wi-Fi communication limits deployment in remote or low-connectivity areas and increases dependence on existing network infrastructure. Transitioning to alternative long-range, low-power communication protocols, such as LoRa, could expand its operational range and reduce energy requirements. Additionally, the absence of over-the-air (OTA) firmware update capability means that physical access is currently required for maintenance or software improvements-an area where remote update functionality would enhance flexibility and reduce service interruptions.

From a hardware perspective, the station does not yet integrate the BME680 sensor, which combines temperature, humidity, pressure, and air quality measurements into a single unit. Incorporating this sensor could simplify the design, lower power consumption, and improve calibration consistency. Likewise, the lack of a wind vane limits the ability to record wind direction, and the precision of the rain gauge and anemometer could be further improved through professional calibration and refined measurement algorithms to ensure more accurate readings.

In terms of data processing capabilities, METEORO v2 currently operates without edge AI algorithms for real-time anomaly detection, which could help optimize bandwidth usage and reduce dependence on cloud processing. Implementing such algorithms, along with predictive alert systems based on historical weather data, would allow the station to provide early warnings for extreme weather events. These enhancements, while not yet present, represent valuable directions for future development that could significantly increase the system's autonomy, adaptability, and contribution to proactive environmental monitoring.

Development

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

A. Microcontroller Selection

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The choice of microcontroller is a critical aspect in the design of IoT weather stations, as it determines the energy efficiency, processing capacity, connectivity, and scalability of the system. In
this project, we opted to use the ESP32-WROOM, a robust, low-cost, high-performance solution widely adopted in similar research projects due to its integrated Wi-Fi and Bluetooth capabilities, dual-core architecture, and compatibility with energy-saving modes such as deep sleep.

The ESP32 is responsible for collecting data from connected environmental sensors, processing it locally, and sending it via HTTP protocols to a cloud database. Its ability to enter a sleep state between scheduled measurements-or be activated by specific interrupts such as those generated by the rain gauge-allows for energy-efficient operation, a crucial aspect in solar-powered systems.

Other studies of low-cost weather stations have analyzed microcontrollers according to their specific applications. Pauzi [4] highlighted the ESP32 for its ability to integrate sensors, control actuators, and provide real-time visualization in agricultural systems. Iglesias Cabello [11] compared the ESP32 with the Arduino Nano 33 IoT, highlighting the power of the former versus the simplicity of the latter in educational contexts. For their part, Sanchez [3] used a Raspberry Pi 3 for data collection and management, noting its high performance, albeit with limitations in cost and energy consumption.

Table 1. Comparison of microcontrollers considered for the research area.

| ESP32-WROOM | Arduino Nano 33 IoT | Raspberry Pi 3
Connectivity | Wi-Fi + Bluetooth | only Wi-Fi | Wi-Fi + Ethernet
GPIO pins | 30–34 | 14–20 | 26+
Energy consumption | Very low (deep sleep) | Low–medium | High
Cost | Low | Medium | High
IoT scalability | High | Medium | High

Based on these criteria, the ESP32-WROOM represents an ideal solution for developing an IoT weather station in educational contexts, as it combines energy efficiency, programming flexibility, broad sensor compatibility, and integrated connectivity, all within a budget that is accessible for academic projects.

B. Selection of environmental variables and sensors

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

For the construction of a functional weather station, multiple key environmental variables were considered that allow for comprehensive monitoring of environmental conditions. These variables are: precipitation, wind speed, temperature, humidity, atmospheric pressure, ultraviolet radiation, and air quality. The selection of sensors was guided by criteria of low energy consumption, adequate accuracy, ease of integration with the ESP32, and commercial availability.

Rain gauge:

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

A rain gauge was developed using a rocker mechanism, a Hall sensor, and a neodymium magnet, with the aim of offering a functional, low-cost solution that can be adapted to low-energy monitoring systems such as the ESP32. (Figure 2).

The sensor operates using a seesaw system, which allows the amount of water collected to be counted using a channeling structure (funnel) with an inlet area of . At one end of the seesaw is a neodymium magnet, while in the central part of the system, attached to the housing, is a Hall sensor, which detects the passage of the magnet each time the seesaw tilts due to the weight of the accumulated water.

Each complete oscillation of the rocker represents 3.2 ml of fallen water, which is equivalent to 0.34 liters per square meter , considering the surface area of the funnel. This count of oscillations is stored internally and converted into an estimate of the accumulated precipitation level. To obtain these values, the system multiplies the number of changes in the rocker arm over a given period by and normalizes it per hour. The microcontroller performs these calculations internally, allowing for autonomous and continuous evaluation of precipitation behavior.

A significant advantage of this design is its compatibility with low-power systems. The system is connected to a wakeup pin on the ESP32, which allows any change in the Hall sensor's state (e.g., from 0 to 1 or from 1 to 0 ) to automatically wake up the microcontroller, even if it is in deep sleep
mode. This ensures that no precipitation event goes unnoticed, regardless of the system's energy status.

The system not only detects the onset of rain, but is also able to determine when it has stopped using an algorithm based on the frequency of changes in the rocker. If the frequency of oscillations is high, the system interprets the event as recent and reduces the waiting time before considering the rain to have ended. If the frequency is low, the system waits for a longer interval before closing the event. This logic allows for the precise definition of the start and end of each storm, improving the accuracy of records and optimizing the use of computational and data transmission resources.
(a)

Fig. 2. Process of constructing a 3D-printed rain gauge. (a) 3D model design in Fusion 360 CAD software. (b) 3D printing process with PLA. (c) Assembly of the rain gauge together with the Hall sensor.

Fig. 2. Process of constructing a 3D-printed rain gauge. (a) 3D model design in Fusion 360 CAD software. (b) 3D printing process with PLA. (c) Assembly of the rain gauge together with the Hall sensor.

The rain gauge was designed and manufactured using 3D printing, with a focus on resistance to the outdoor environment and structural precision. Its main physical characteristics are:

Sealed walls to prevent interference from wind or external vibrations.

Upper grid to block the passage of unwanted objects.

Epoxy coating to protect all parts exposed to water and humidity.

These measures seek to minimize false positives caused by external factors unrelated to rain, as well as to extend the device's useful life in adverse weather conditions.

Although the volume calculations by oscillation have been empirically defined by the development team, they will need to be validated against certified instruments in future research[20]. However, preliminary results indicate reliable and repeatable performance, allowing us to affirm that the sensor is suitable for low-budget environmental monitoring tasks[19]. The rain gauge currently under development is capable of:

Detecting the start and end of precipitation events.

Measuring the total amount of rainfall in .

Classify rainfall intensity into established categories.

Calculate the duration of each event.

Estimate the intensity during the event, useful for activating automatic alerts.

Anemometer:

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The anemometer measures wind speed using a structure that combines a static surface and a moving surface, joined by a bearing and a bolt that secures both parts. Its design incorporates a Hall sensor and a neodymium magnet. The blades mounted on the moving surface allow the wind to generate rotation; the magnet, attached to this moving part, passes in front of the Hall sensor, which is fixed to the base of the device and strategically located in the circular path. (Figure 3).

Fig. 3. 3D-printed anemometer. (a) Photograph of rotating mechanism with a bearing and bolt for the anemometer, next to the internal base of the anemometer with a neodymium magnet. (b) Photograph of a Hall sensor used in the project. (c) Final assembly of the anemometer.

Fig. 3. 3D-printed anemometer. (a) Photograph of rotating mechanism with a bearing and bolt for the anemometer, next to the internal base of the anemometer with a neodymium magnet. (b) Photograph of a Hall sensor used in the project. (c) Final assembly of the anemometer.

The Hall sensor detects each revolution of the magnet, generating an electrical pulse that is captured by the microcontroller. In this way, by counting pulses per unit of time, it is possible to calculate the rotation frequency, which serves as the basis for estimating wind speed. For this purpose, physical principles that relate angular velocity to linear velocity are applied, taking the radius of rotation as a reference.

Equation 1. Relationship between angular velocity and tangential velocity.

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

In addition, the pulses are counted at a specific time interval, using the millis() function, which returns the number of milliseconds since the ESP32 board started running the current program. This helps avoid interruptions in the system. Through this rotation frequency and an empirically calibrated constant, an estimated value for wind speed is obtained.

Equation 2. Calculation of anemometer rotation frequency.

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Finally, wind speed can be obtained from the signal frequency, the number of cycles, and their times, using the following equation:

Equation 3. Final estimate of wind speed.
Like the rain gauge, this instrument has been designed and manufactured by the project team using 3D printing with PLA material. All calculations and parameters have been determined through internal testing. For future research, it is recommended that a validation process be carried out by comparison with professionally calibrated and certified anemometers in order to determine the actual accuracy of the prototype.

Solar Radiation Shield (DHT11, MQ135, S12SD and BMP180)

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The solar radiation shield is an essential component integrated into our system, designed to optimize readings from environmental sensors that require direct exposure to outdoor air to take their measurements, while simultaneously protecting them from weather conditions. To ensure this functionality, we have adapted low-cost plastic plates that protect the DHT11, MQ135, S12SD, and BMP180 sensors [24]. This design promotes constant airflow, which is essential for improving data accuracy, while isolating the devices from the direct effects of solar radiation and preventing rainwater that could damage them. The sensors are integrated into a compact structure that simplifies installation and optimizes the use of available space. Specifically, the DHT11 sensor measures temperature and humidity parameters; the MQ135 evaluates air quality; the S12SD records UV radiation; and the BMP180 monitors pressure, altitude, and temperature. The sensors are carefully arranged inside the solar radiation shield: all are located in the center, except for the UV radiation sensor, which is located in the upper section to ensure direct exposure to sunlight, as shown in Figure 4. The shield structure was made using economical materials such as plastic plates, PVC, nuts, and bolts.
Looking ahead to future versions of the system, there are plans to replace the current set of sensors with a single, more efficient module: the BME680, which integrates temperature, humidity, pressure, and air quality measurements into a single device, thereby reducing energy consumption and space requirements. This decision stems mainly from the high energy demand of the MQ135, which posed a significant challenge in the energy optimization of the system, a topic that will be addressed in a later section. The specific accuracy of each sensor corresponds to the manufacturer's specifications; however, the uniqueness of our station lies in the infrastructure used and the methods applied within the microcontroller programming, which allows measurements to be averaged to achieve greater reliability in the data collected.

Fig. 4. DIY Solar radiation shield with plastic plates and PVC. (a) Final assembly of the solar radiation shield. (b) Internal analysis in Fusion 360 of the effect of the solar radiation shield on the sensors. (c) Internal tube of the solar radiation shield that integrates various sensors, showing the
DHT11, BMP180, and MQ135 sensors. (d) The UV radiation sensor is shown at the top of the internal tube. (e) Complete view of the internal tube of the solar radiation shield with the integration of the sensors.

Fig. 4. DIY Solar radiation shield with plastic plates and PVC. (a) Final assembly of the solar radiation shield. (b) Internal analysis in Fusion 360 of the effect of the solar radiation shield on the sensors. (c) Internal tube of the solar radiation shield that integrates various sensors, showing the DHT11, BMP180, and MQ135 sensors. (d) The UV radiation sensor is shown at the top of the internal tube. (e) Complete view of the internal tube of the solar radiation shield with the integration of the sensors.

C. Selecting the power system

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

With the aim of achieving an autonomous, cable-free, and self-sustaining system, a solar power supply system was integrated. This decision not only promotes the use of clean and renewable energy but is also critical for installing the device in remote or hard-to-reach locations where conventional electrical connections are unavailable or represent a high logistical cost.
To ensure continuous operation, a detailed analysis of the energy consumption of each of the system's components was carried out, considering both the values provided by the manufacturers and
the data obtained experimentally. One of the most relevant findings was that the MQ135 sensor, responsible for measuring air quality, is the component with the highest energy consumption in the system. This is because it requires initial and continuous heating to operate properly, with a typical consumption of approximately 150 mA at 5 V , according to its technical data sheet. Although this value may seem low in conventional systems, it represents a major challenge for a solar-powered system, where even small energy savings are decisive [16].

To mitigate this problem, an on/off control was implemented using a 5 V mechanical relay, ruling out alternatives such as NPN, PNP, MOSFET, or Darlington transistors, which, in empirical tests, did not offer the stability necessary to obtain reliable readings. Thanks to this system, the MQ135 sensor is activated only when measurements are required, including a 5 -minute warm-up time, followed by a short sampling window to obtain a representative average. Due to its high power consumption, readings from this sensor are limited to once every 6 hours, thus avoiding compromising the overall energy autonomy of the system.

On the other hand, the low-power sensors integrated into the solar radiation shield (DHT11, S12SD, and BMP180) operate more efficiently and do not require prolonged stabilization periods. These devices take periodic readings every 30 minutes, and their residual consumption has been minimized through the use of an PNP transistor that completely cuts off the power supply when they are not in use. This selective power-on logic allows for greater energy control and contributes significantly to the overall efficiency of the system.
In addition, although the ESP32 microcontroller operates with Wi-Fi connectivity for data transmission, it does not maintain this connection continuously. Instead, a deep sleep function has been implemented, which drastically reduces energy consumption during periods of inactivity. According to the manufacturer, the ESP32 can consume between 160 and 260 mA in active operation with Wi-Fi, but when entering deep sleep mode, consumption can drop to 0.15 mA (with the ULP coprocessor active) and even down to , a critical reduction for keeping the system operational 24 hours a day. The microcontroller is programmed to activate in sync with the local time, allowing the system to wake up at specific times to take periodic measurements. In the case of the rain gauge, an interrupt generated by the Hall sensor is used, set on one of the pins of the esp32 with this signal detection functionality enabled, capable of waking up the microcontroller when it detects a change of state, even if it is in sleep mode. Similarly, the anemometer has this capability; however, its activation is limited to once every 30 minutes in order to prevent the microcontroller from continuously waking up due to the unpredictable nature of the wind. This ensures that, during that time interval, there is an opportunity to record the first gust that occurs before the programmed readings are reset.

Fig. 5. Solar energy system and energy storage circuit.

Fig. 5. Solar energy system and energy storage circuit.

After carefully analyzing the daily consumption of all components, an average energy consumption of approximately 540 mAh per day was estimated. To reliably meet this demand, a redundant power supply system was designed with three 18650 batteries, each with a capacity of 8800 mAh , connected in parallel to maximize system autonomy, especially on cloudy days or days with low solar production. Each battery has its own TP4056 charging module, which manages protection against overcharging and overdischarging. (Figure 5) A 6W solar panel that can generate 9600 mAh in a day with 8 hours of sunshine, and according to our own theoretical analyses, it is estimated that in low
solar production scenarios there is still enough energy to keep the system running for several days. However, the stored energy does not increase much and begins to be limited with more cloudy days.

As mentioned, the charging process is carried out using a 6W solar panel, whose energy is regulated to prevent unwanted current return by means of a Schottky diode. To power the components that require 5 V , a Step-Up module is used to raise the voltage of the batteries ( 3.7 V nominal) to a stable 5 V . The output of this module is connected directly to the microcontroller via a physical switch, which allows the system to be turned on or off completely if necessary.

The energy design of this weather station not only allows it to operate completely autonomously, but also reflects a series of strategic decisions that balance measurement accuracy with strict energy efficiency. This architecture represents a scalable and replicable model for future implementations in rural, agricultural, or hard-to-reach areas, where sustainability and energy independence are key factors for long-term success.
D. Development of the physical structure

The structural design of Meteoro was conceived with a focus on economy, strength, and ease of replication. To this end, -inch PVC pipes were used, an accessible material that offers the necessary rigidity to withstand adverse weather conditions while allowing for a modular configuration[21]. This modularity facilitated the strategic distribution of key components such as the solar radiation shield, anemometer, and rain gauge, each located in an optimized position to ensure readings and efficient operation. Bolts and nuts were used to assemble the joints, as using PVC glue would have prevented future modifications to the prototype.

The junction box-responsible for protecting the electronic components-was redesigned using a locally sourced waterproof enclosure. This modification significantly improves protection against water, dust, and prolonged exposure to the sun, thus extending the useful life of the devices contained within.

At the top of the structure is the solar panel, positioned to maximize solar radiation capture during the day. In addition to its energy function, it acts as a thermal shade, helping to reduce overheating of the junction box, which is critical especially for preserving the health of lithium batteries, which tend to degrade rapidly when exposed to extreme heat.

The METEORO v2 station is designed to adapt to a wide variety of flat or cylindrical poles using plastic or industrial clamps. These are complemented by an optional metal plate to adapt to cylindrical surfaces, providing greater structural strength and stability against wind or vibrations, ensuring reliable anchoring in outdoor environments. The electrical connections between the sensors and junction box were implemented using UTP cables, which allow both data and power to be transmitted through a single modular channel.
E. Electronic control architecture

As previously illustrated, the system is based on an ESP32 microcontroller, which acts as the central processing and communication unit between the various sensors and actuators. Figure 7 shows the connection diagram detailing the control architecture, including voltage distribution, the use of auxiliary electronic components such as transistors and resistors, and the integration of each sensor into the system.

The ESP32 microcontroller is powered by 5 V , which is also required by the MQ135 sensor. The rest of the sensors are powered by the 3.3 V output provided by the ESP32 itself. However, because the MQ135 delivers an analog signal with an output range that can reach 5 V , a voltage divider composed of and resistors is implemented in order to adapt the signal to the logic level of the ESP32 and prevent damage to the analog pin.

To minimize energy consumption, the MQ135 is activated only at reading times by a relay controlled by an NPN transistor. This transistor acts as a switch that energizes the relay in conjunction with the ESP32 control logic. Since the relay is activated by negative logic, its control can be shared between the signal pin and the transistor's power pin. In order to avoid unwanted activations, especially when the microcontroller enters deep sleep mode, a pulldown resistor is incorporated to ensure a stable low state at the base of the transistor, eliminating possible floating signals.

Fig. 6. Control circuit diagram and system sensors.

Fig. 6. Control circuit diagram and system sensors.

fritzing

As for the DHT11 sensor, its communication is digital and requires no additional configuration, operating at 3.3 V . Something similar occurs with the S12SD UV radiation sensor, which, despite delivering an analog signal, is compatible with the voltage levels of the ESP32 as it is powered by 3.3 V . On the other hand, the BMP180, which measures barometric pressure and altitude, uses the I2C communication protocol, connecting via the SDA and SCL lines, which are fully supported by the microcontroller.

The Hall effect sensors used in the rain gauge and anemometer are digital and operate at 3.3 V . These sensors can send signals to the microcontroller both to record events and to wake it up from deep sleep mode using pins configured as wake-up GPIOs, thus optimizing the system's energy use.
Finally, for more efficient energy management, the DHT11, S12SD, and BMP180 sensors are indirectly powered by a PNP transistor. This transistor allows the power supply to these sensors to be cut off when they are not in use. As in the case of the NPN transistor, the correct signal status during low-power mode is guaranteed by a pull-up resistor connected between the transistor base and the 3.3 V voltage, ensuring that there are no unwanted fluctuations during periods of microcontroller inactivity.

F. Microcontroller firmware logic

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

With regard to the microcontroller firmware, METEORO's internal operation is based on a hybrid architecture designed for low energy consumption, in which the ESP32 remains in deep sleep mode most of the time[22]. It is only activated to perform specific measurement or data transmission tasks. When exiting this state, the system automatically selects between three autonomous operating modes: Rain Gauge Status (RG_Status), Anemometer Status (A_Status), and Time Status (T_Status), depending on the event that triggered its activation. These modes are organized and represented in the system control and communication diagram (Figure 7), which outlines both the logical flow and the relationship between physical components.

Fig. 7. Microcontroller program logic flowchart.

Fig. 7. Microcontroller program logic flowchart.

In basic terms, the ESP32 is programmed to wake up mainly in three ways: by timing, by anemometer signal, or by rain gauge signal. By default, it wakes up automatically every 30 minutes, just before the internal clock strikes --:00 and --:30. This synchronization is achieved using the time.h library, which allows the exact moment when the system should be activated to be calculated, ensuring periodic readings. Once awakened for this reason, the system enters the T_Status mode, in which low-power sensor readings from the Solar Radiation Shield, such as the DHT11, S12SD, and BMP180, are taken. This mode also checks whether a measurement should be taken from the MQ135, a sensor that is only activated every 6 hours due to its high power consumption.

The second activation mode occurs via an external interrupt (EXT1) when the anemometer's Hall sensor detects variations in its magnetic field associated with a gust of wind. This interrupt activates A_Status mode, initiating a wind speed measurement process. During this state, the microcontroller measures the revolutions of the anemometer at a fixed time interval, using the millis() function to
avoid interruptions or blockages in execution. Upon completion of the measurement, the data is transmitted and the microcontroller returns to deep sleep until the next scheduled activation. To maintain consistency with the sampling cycles and avoid redundancy, the anemometer can only wake up the system once per interval between timed readings.

The third activation mode is triggered by the EXT0 wake-up pin, linked to the rain gauge's Hall sensor. This sensor detects changes in the status of the tilting mechanism, and if an initial value was recorded when entering deep sleep, the microcontroller can identify a change in status at the time of interruption. This activates RG_Status mode, allowing an immediate response to precipitation events, without waiting for periodic reading cycles. In this state, the microcontroller remains active as long as rain detection continues. Furthermore, if during RG_Status the programmed time for T_Status or A_Status is reached, these processes are executed without conflict, thanks to the non-blocking logic provided by millis(), which allows tasks to be executed in parallel.

The RG_Status termination algorithm is based on the rain gauge's switching frequency: the lower the frequency of changes, the longer the waiting time before the rain status is terminated, and vice versa. This proportional logic improves the accuracy of detecting intermittent rain or prolonged events.

In any of the three scenarios described, once the measurements are complete, the ESP32 briefly activates its WiFi module, connects to the local network, and sends the collected data via HTTP protocol to a cloud database. For this implementation, we chose Google Firebase, a free and accessible service that offers cloud storage (up to 1 GB ) and a developer-friendly interface. Since the data packets generated by the weather station are lightweight, Firebase was an ideal solution for our needs.

Finally, once the data transfer is complete, the system closes the WiFi connection and assesses whether there are any pending tasks. If not, it calculates the time remaining until the next scheduled wake-up (for the --:00 or --:30 marks), adjusting the sleep time based on the duration of the operations performed to ensure energy efficiency and timing accuracy.
G. Communication protocol

As for the communication protocols used by the weather station, it was decided to use only the WiFi wireless technology integrated into the ESP32 microcontroller. This decision allows the system to connect directly to the university campus network as another device within the existing LAN infrastructure, without requiring additional hardware to establish communication with the server. This facilitates its implementation within the institutional environment. From the network, the station makes HTTP requests to transmit the collected data to cloud storage services. To improve connection stability and ensure greater coverage in potentially adverse environments-such as thunderstorms or interference caused by the height of its location-an external antenna was integrated into the microcontroller. This significantly improves data reception and transmission, ensuring continuous and reliable operation.
Although WiFi communication has proven functional in this version, it has been considered that, for future iterations, an ultra-low-power protocol such as LoRa could represent a more efficient alternative. LoRa allows the transmission of small packets over long distances with minimal energy consumption, which is ideal for applications in rural areas or areas with limited network infrastructure [18]. However, this approach would require a redesign that includes two nodes: one transmitter (in the field) and one receiver (connected to the WiFi network) [6], which acts as a bridge between the station and the server.

Additionally, the possibility of implementing OTA (Over-The-Air) functionality via WiFi is being considered, allowing for remote updating of the microcontroller's firmware. This feature is particularly useful, given that the station is installed in an elevated position that is difficult to access physically, which complicates manual updates. However, the implementation of OTA must consider future cybersecurity measures to prevent unauthorized access that could compromise the integrity of the system.
H. Backend Cloud services

As part of METEORO's evolution towards a more robust and scalable version, it was also decided to integrate a cloud-based backend based on Google Firebase [15], which allows real-time data
collection, storage, and querying without the need for additional physical infrastructure. This environment, in addition to being highly compatible with microcontrollers such as the ESP32, provides features such as user authentication, web hosting, and scalable NoSQL databases, making it an ideal solution for educational and research purposes, especially since it offers free and accessible plans.
The ESP32 microcontroller is responsible for collecting environmental data and sending it securely to the cloud via the HTTPS protocol, using an API Key and the Firebase ESP Client library. The information is structured in JSON format and stored in the Firebase Realtime Database, allowing for efficient, low-latency synchronization. The data is organized under a hierarchical node called UsersData, where each user is identified by a unique Firebase UID. Within each UID, readings are grouped by sensor type (e.g., DHT22 or BMP180) and tagged with timestamps, facilitating both realtime visualization and historical analysis. (Figure 8).

Fig. 8. Firebase database graph.

Fig. 8. Firebase database graph.

In this system, Firebase acts as a persistent logger. Although backups must be done manually, the platform allows data to be easily exported in JSON format for future queries or analysis. In addition, alert monitoring is handled from the web interface using real-time listeners that detect critical conditions (such as high temperatures or poor air quality) and trigger visual notifications, reducing the processing load on the ESP32 and maintaining an efficient and decentralized approach.

I. Data visualization method and web interface development

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

METEORO v2 utilizes Firebase Realtime Database's web hosting services to facilitate data visualization and alert mechanisms. By leveraging Firebase's integrated hosting features, the system ensures reliable performance and uninterrupted availability, aligning with the project's goal of maintaining cost-efficiency.
The web interface was developed using standard front-end technologies, including HTML, CSS, and JavaScript, to create an accessible and intuitive user experience. One of the main architectural features of the interface is its real-time data synchronization. Client-side JavaScript code establishes a persistent, real-time listener to the Firebase database. This event-driven approach ensures that upon the arrival of a new measurement from the weather station, the web application automatically fetches the new data and refreshes the displayed values and graphs without requiring a manual page reload. This functionality provides the university community with a live dashboard of campus weather conditions, fulfilling a primary educational goal of the project. (Figure 9).

Fig. 9. (a) Viewing the website on a mobile phone (b) Viewing the website on a tablet (c) Viewing the website on a computer.

Fig. 9. (a) Viewing the website on a mobile phone (b) Viewing the website on a tablet (c) Viewing the website on a computer.

For data visualization, the open-source Chart.js library was implemented18. This powerful library was chosen for its flexibility in rendering dynamic and interactive time-series graphs directly in the browser[17]. It is configured to display data on both daily and weekly scales, allowing users to easily analyze short-term fluctuations and longer-term weather trends. The entire website was built with a responsive design philosophy, using flexible grid layouts and media queries to ensure full functionality and an optimized user experience across a wide range of devices, from desktop monitors to mobile phones. Figure 11 shows the responsive design of the web interface, illustrating its adaptability to different screen sizes and its commitment to broad accessibility for the entire university community.

Implementation

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

J. Initial testing in a domestic environment, detection and correction of bugs

Once the METEORO hardware assembly was completed and a preliminary version of the firmware was developed, the testing phase under real conditions began. The system was installed on a pole outside the home of one of the researchers, where it remained in continuous operation for 90 days. During this period, it was exposed to direct sunlight, high temperatures, humidity, and several thunderstorms, which allowed us to validate both its environmental resistance and the efficiency of solar energy collection.

In this initial testing stage, several technical challenges were identified, the most recurrent being event synchronization and stability in sending data to the server. Since the ESP32 had to establish a WiFi connection each time it woke up from low-power mode, the connection time was sometimes insufficient to successfully complete HTTP requests. This required redesigning the firmware's timing logic and adjusting connection times to ensure a more robust link before each data transmission.

In addition, significant improvements were made to the system's energy management, optimizing deep sleep periods and reducing unnecessary component consumption during inactivity. Key sensors such as the UV radiation sensor and the MQ135 (air quality) were calibrated, and algorithms associated with the anemometer and rain gauge data were refined to improve the accuracy of readings and avoid false positives.

A major challenge was that each firmware update required a physical cable connection, which was inconvenient due to the prototype's elevated location. This prompted consideration of incorporating OTA (Over-The-Air) updates or some secure wireless programming method for future versions.

Despite the initial adjustments and difficulties, the system evolved positively and managed to establish itself as a functional and stable prototype within its test context. This current version represents a solid starting point for future more robust and scalable iterations.
K. Installation in the university environment

Once the prototype was completed, the team engaged in discussions with university authorities to identify the most suitable location for the weather station. The selection process was critical, as the station required specific conditions to ensure optimal functionality. Key considerations included:

Solar Exposure: The chosen site needed uninterrupted sunlight to maximize the efficiency of the 6W solar panel, which is essential for maintaining the system's energy autonomy. A location with minimal shading was prioritized to ensure consistent power generation throughout the day.

Wi-Fi Connectivity: Proximity to a stable Wi-Fi network was another crucial factor, as the station relies on internet connectivity to transmit real-time atmospheric data to the Firebase cloud database. This enables seamless visualization of the data on the web interface for students and staff.

Safety and Accessibility: The installation site had to be secure and easily accessible for maintenance, while also being exposed to environmental elements to ensure accurate sensor readings.
After evaluating these factors, an ideal location was identified on the university campus: a lamppost that met all the technical and logistical requirements. The elevated position not only provided ample solar exposure but also facilitated unobstructed wind and rain measurements. Additionally, the site was within range of the university's Wi-Fi network, ensuring reliable data transmission. (Figure 10).

Fig. 10. (a) Installation of the station at the university (b) Correct adjustment of the station for sunlight (c) Final angle of the station.

Fig. 10. (a) Installation of the station at the university (b) Correct adjustment of the station for sunlight (c) Final angle of the station.

The installation process involved mounting the PVC structure securely to the lamppost using industrial clamps, positioning the solar panel for optimal sun exposure, and verifying the connectivity of all sensors. This strategic placement ensures the station's long-term functionality and its role as a reliable educational tool for the university community.
L. Awareness and promotion in the educational community

To highlight the significance of the METEORO v2 project for the university, a promotional campaign was implemented to encourage engagement with the weather station's data. Key strategies included:

QR Code Posters: Informative posters featuring QR codes linked to the METEORO web interface were strategically placed in high-traffic areas across the campus. This allowed students and staff to easily access real-time and historical weather data from their mobile devices or computers.

Accessibility and Visibility: The placement of posters in central locations-such as corridors, laboratories, and common areas-ensured widespread awareness and facilitated seamless interaction with the system.
Through these efforts, METEORO not only became a functional weather monitoring system but also an educational asset, supporting hands-on learning and interdisciplinary collaboration.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The weather station was designed to address the limitations of its predecessor and other low-cost weather monitoring systems by integrating advanced energy management, durable materials, and a comprehensive suite of sensors. The system's performance was evaluated based on its energy efficiency and durability under real-world conditions. The results demonstrate significant improvements over existing solutions, particularly in terms of energy autonomy while maintaining a low-cost framework suitable for educational use.

One of the primary advancements in METEORO v2 is its optimized energy management system. The integration of a 6W solar panel and three 18650 batteries ( 8800 mAh each) ensured continuous operation, even during periods of low solar irradiation. The ESP32 microcontroller's deep sleep mode reduced energy consumption during inactive periods, a critical feature for long-term autonomy. This aligns with findings from Lefevre et al. [6], who emphasized the importance of energy-efficient designs in low-cost weather stations. However, the MQ135 sensor's high-power consumption (150 mA at 5 V ) posed a challenge, necessitating a relay-based activation system to limit its usage to once every 6 hours. This strategic power management approach contrasts with the simpler but less efficient designs reported in other studies [1], highlighting METEORO v2's innovative balance between functionality and energy conservation.

The system's data pipeline, leveraging Firebase for cloud storage and a responsive web interface, provided real-time access to weather data for the university community. The use of Chart.js for dynamic visualization enabled users to analyze trends in temperature, humidity, and other variables, fulfilling the project's educational goals. This approach mirrors the data-centric frameworks highlighted by Lefevre et al. [6], who advocated for scalable, cloud-based solutions in environmental monitoring.

Conclusion and Future Work

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

The METEORO v2 project represents a significant advancement in low-cost, autonomous weather monitoring systems, particularly optimized for educational environments. The system's most notable achievement is its highly efficient energy management system, which combines a 6W solar panel with three parallel-connected 18650 batteries (each 8800 mAh ) and sophisticated power control logic. The ESP32 microcontroller's deep sleep capability forms the backbone of this efficiency. Strategic decisions like using a mechanical relay to control the high-power MQ135 air quality sensor (limiting its operation to once every 6 hours with a 5 -minute warm-up period) demonstrate thoughtful power optimization. The inclusion of wake-up interrupts from the rain gauge ensures no precipitation events are missed while maintaining energy savings.

Equally important is the station's durable physical construction. The team selected affordable yet robust materials including -inch PVC pipes for the main structure, 3D-printed PLA components for sensor housings, and a waterproof electrical enclosure. These choices provide adequate protection against environmental factors while keeping costs low. The modular design allows for easy maintenance and upgrades. The solar radiation shield, crafted from plastic plates and PVC, effectively protects temperature and humidity sensors while permitting proper airflow. For data accessibility, the system provides responsive web-based visualization through Firebase, allowing users to view current and historical measurements.

Future Improvements:

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Advanced Communication and Component Reduction

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Replace Wi-Fi with LoRa (Long Range) communication for remote or low-connectivity areas, reducing reliance on network infrastructure.

Integrate the BME680 sensor (combining temperature, humidity, pressure, and air quality) to simplify hardware, reduce power consumption, and minimize calibration errors.

Allow the use of OTA or similar technology for remote updating of microcontroller firmware.

Enhanced Sensor Accuracy and Expansion

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Improve the precision of the rain gauge and anemometer through professional calibration and refined measurement algorithms[23].

Add a wind vane (wind direction sensor) to provide comprehensive data on local wind patterns.

AI and Data Processing

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

Implement edge AI algorithms for real-time anomaly detection, optimizing bandwidth and cloud storage usage.

Develop predictive alert systems based on historical trends to anticipate extreme weather events.

References

The original version of this paper is available on https://www.scientific.net/EI.16.75.pdf

M. Pourbafrani, H. Ghadamian, M. Moghadasi, and M. Mardani, Design, Fabrication, and Experimental Study of a Low-cost and Accurate Weather Station Using a Microcontroller System, Journal of Renewable Energy and Environment, 10(4), (2023) 35-43. https://doi.org/10.30501/jree.2023.383796.1551.

Dwi Ramadhani, A. Taqwa, Ade Silvia Handayani, Wahyu Caesarendra, Nyayu Latifah Husni, and C. R. Sitompul, Multisensor monitoring system for detecting changes in weather conditions and air quality in agricultural environments, Journal of Environment and Sustainability Education, 3(2), (2025) 274-286. .

M. A. Navarrete-Sanchez, Re. Olivera-Reyna, Ro. Olivera-Reyna, R. J. Perez-Chimal, and J. U. Munoz-Minjares, IoT-Based Classroom Temperature Monitoring and Missing Data Prediction Using Raspberry Pi and ESP32, Journal of Robotics and Control (JRC), 6(1), (2025) 234-245. .

A. F. Pauzi and M. Z. Hasan, Development of IoT Based Weather Reporting System, IOP Conference Series: Materials Science and Engineering, 917 (2020) 012032. .

J. C. Shovic, Raspberry Pi IoT Projects, Apress, Berkeley, California, 2021. .

A. Lefevre, B. Malet-Damour, H. Boyer, and G. Rivière, Advancing Urban Microclimate Monitoring: The Development of an Environmental Data Measurement Station Using a LowTech Approach, Sustainability, 16(7), (2024) 3093-3093. .

K. Ioannou, D. Karampatzakis, P. Amanatidis, V. Aggelopoulos, and I. Karmiris, Low-Cost Automatic Weather Stations in the Internet of Things, Information, 12(4), (2021) 146. .

None Silvia Ganesan, C. Peng, L. Chen, N. Kong, N. Ng, and M. Reyasudin, IoT-enabled Smart Weather Stations: Innovations, Challenges, and Future Directions, Malaysian Journal of Science and Advanced Technology, (2024) 180-190, .

N. G. Bowen and Kumar Yelamarthi, Applied Classroom Use of a Mobile Multifunctional Weather Station, , (2020). .

Molnár, J., Kiresova, S., Vince, T., Kovac, D., Jacko, P., Beres, M., & Hrabovský, P., Weather Station IoT Educational Model Using Cloud Services, Journal of Universal Computer Science, 26(11), (2020) 1495-1512. .

I. Cabello, Design and Implementation of an IoT Weather Station: Installation at Novia University of Applied Sciences, Finland, , (2025), .

Leeming, F. C., Dwyer, W. O., Porter, B. E., & Cobern, M. K. (1993). Outcome Research in Environmental Education: A Critical Review. The Journal of Environmental Education, 24(4), 8-21. .

Abed, S., Alyahya, N., Altameem, A. (2020). IoT in Education: Its Impacts and Its Future in Saudi Universities and Educational Environments. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. .

A. Arnautov, R. Esin, E. Arnautova (2020) DESIGNING STEM-BASED LEARNING MODULES FOR FRESHMEN ENGINEERING STUDENTS, ICERI2020 Proceedings, pp. 5438-5446.

L. Romero, S. Velarde, y M. Ampuero Rosas, "Smart meeting room scheduling and management system for a university campus using Android tablets with Firebase backend and Headwind MDM," en Proc. 19th LACCEI Int. Multi-Conf. for Engineering, Education, and Technology - Virtual Edition, Bogotá DC, Colombia, 19-23 Jul 2021, paper FP383.

Yugank, H.K., Sharma, R. & Gupta, S.H. An approach to analyse energy consumption of an IoT system. Int. j. inf. tecnol. 14, 2549-2558 (2022). .

Bahadur, T. (2022). Data Visualization: Existing Tools and Techniques. In: Kaur, P., Mallick, P.K., Balas, V.E., Geetha, S. (eds) Advanced Data Mining Tools and Methods for Social Computing. Intelligent Data-Centric Systems, vol 1. Elsevier. .

Muthukrishnan, H., Jeevanantham, A., Sunita, B., Najeerabanu, S., & Yasuvantha, V. (2021). Performance Analysis of Wi-Fi and LoRa Technology and its Implementation in Farm Monitoring System. In: Proceedings of IVC RAISE 2020. IOP Conference Series: Materials Science and Engineering, vol.1055, issue 1, 012051. IOP Publishing, UK. .

Hernández-Torres G and Sánchez-DelaCruz E (2025) Challenges and opportunities of ambient intelligence (AmI) in the 21st century: a historical review, Evolutionary Intelligence, 10.1007/s12065-025-01067-1, 18:4, Online publication date: 1-Aug-2025.

Masson, M. H., Canu, S., Grandvalet, Y., & Lynggaard-Jensen, A. (1999). Software sensor design based on empirical data. In: Measurement, vol.30, issue3, pp.213-221. Elsevier. .

Mazlitdinova, D., Suyunova, M., Khaydarova, G., Saibnazarova, M., & Kodirova, M. (2019). Modular Training System as a Factor of Improving Educational Process. In: International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 1, pp. 31603166. Blue Eyes Intelligence Engineering & Sciences Publication..

L.M. Broell, C. Hanshans, and D. Kimmerle, 'IoT on an ESP32: Optimization Methods Regarding Battery Life and Write Speed to an SD-Card', Edge Computing - Technology, Management and Integration. IntechOpen, Aug. 02, 2023. doi: 10.5772/intechopen. 110752.

G. Jenkins, 'A comparison between two types of widely used weather stations', Weather, vol. 69, no. 4, pp. 105-110, Apr. 2014. doi: 10.1002/wea. 2237.

Balamurali, D., Chakankar, S., Sharma, G. et al. A solar-powered, internet of things (IoT)controlled water irrigation system supported by rainfall forecasts utilizing aerosols: a review. Environ Dev Sustain (2025). .

B. Tian, K. M. Hou, X. Diao, H. Shi, H. Zhou and W. Wang, "Environment-Adaptive Calibration System for Outdoor Low-Cost Electrochemical Gas Sensors," in IEEE Access, vol. 7, pp. 62592-62605, 2019, doi: 10.1109/ACCESS.2019.2916826. keywords: {Gas detectors; Calibration; Monitoring; Air quality; Temperature sensors; Meteorology; Air quality monitoring; environment-adaptive calibration (EAC); IoT cloud platform; low-cost electrochemical gas sensors; non-linear regression}.

H. Abubakar, S. A. Muhammad, M. A. Abubakar, and M. S. Sani, "Design and Development of a Solar Powered IoT-Based Weather Station," International Journal of Sustainable Science and Engineering (IJSSE), vol. 14, no. 1, pp. 251-258, 2024.

D. Franco, M. Costa, L. Rodríguez, and A. López, "IoT-based Smart Meteorological Monitoring System with LoRaWAN Communication," Frontiers in Communication and Networks, vol. 2, 2024, Art. no. 1505375.

R. Yadav, A. Kumar, and S. Sharma, "Low-Cost Solar Powered Weather Station for Remote Monitoring Applications," Indonesian Journal of Electrical Engineering and Computer Science, vol. 2024.I2, no. 009, pp. 1-10, 2024.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 97-105

doi: 10.4028/p-5l14Yq

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-02-05

Revised:
2025-04-22

Accepted:
2025-08-07

Online:
2026-01-07

Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

M. Rasyid Ridla , Haidar Ali , Nandang Mufti , Saparullah , M. Tommy Hasan Abadi , Naufal Fiego Fadhlullah , Erma Surya Yuliana , Muladi , Yoyok Adisetio Laksono

¹Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Malang, JI. Semarang 5 Malang 65145, Indonesia

Centre of Advance Materials for Renewable Energy, Universitas Negeri Malang, JI. Semarang 5 Malang 65145, Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Malang, JI. Semarang 5 Malang 65145, Indonesia

muhammad.rasyid.2203228@students.um.ac.id, haidar.ali.2003228@students.um.ac.id, nandang.mufti.fmipa@um.ac.id, saparullah.1903225@students.um.ac.id,

Corresponding author: nandang.mufti.fmipa@um.ac.id

Keywords: water quality monitoring, system, IoT, water quality parameters, aquaculture.

Abstract

Rising demands for precise and efficient water quality monitoring have accelerated the adoption of IoT technologies, particularly in aquaculture environments. This article discusses the development of Internet of Things (IoT)-based water quality monitoring devices. This device enables real-time monitoring of water quality parameters, including temperature, total dissolved solids (TDS), electrical conductivity (EC), resistivity, and salinity. The device involves the use of several waterquality sensors connected to a microcontroller that serves as the central processing unit. Sensor data is transmitted wirelessly to a cloud platform for analysis. The results are then presented through a user-friendly visualization on a Human-Machine Interface (HMI) device or Android application. The main advantage of this device is its ability to provide real-time data that allows quick response to changes in water conditions. The system was tested on various water samples, demonstrating stable measurements when compared to the Hanna Instrument HI98194 standard device. Further calibration of the TDS sensor is necessary to improve the accuracy of Total Dissolved Solids (TDS), Electrical Conductivity (EC), resistivity, and salinity measurements.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

Poor water quality poses a serious challenge to both public consumption and industries such as aquaculture and manufacturing. Water quality must undergo rigorous testing to assess its suitability for public distribution by drinking water providers [1]. Water quality is a key factor in ensuring the success of aquaculture and fisheries businesses. Key water parameters-such as salinity, pH , temperature, dissolved oxygen, and total dissolved solids-must be carefully monitored to support optimal fish growth and health . Poor water circulation in ponds-caused by leftover feed, metabolic waste, and limited water exchange-can disrupt water quality parameters such as dissolved oxygen levels, which in turn impacts fish growth [4].

The importance of monitoring water quality as a living medium for fish encourages the development of methods for monitoring pond water quality. Traditionally, water quality has been assessed through laboratory analysis, which involves sampling pond water and testing it in a controlled environment. Sensors have been introduced to monitor specific water parameters, although most are limited to measuring only one variable per sensor [5,6]. This approach is both inefficient and time consuming, limiting its practicality in real time aquaculture operations.

Many researchers have developed Internet of Things (IoT)-based systems to monitor water quality in aquaculture. This monitoring system was developed to monitor several parameters such as pH , temperature, salinity, electrical conductivity, dissolved solids, and dissolved oxygen levels[7]. The calibration process for the multi-sensor monitoring system is carried out using a comparison method with standardized devices, including pH sensors calibrated with buffer solutions and temperature sensors equipped with the HEL-711 RTD. The calibration process itself is carried out for 24 hours and then the results are analyzed so that small range differences are obtained. The minor discrepancies observed suggest that the developed system has achieved sufficient accuracy for practical implementation [8].

Sobri et al. (2021) have developed an IoT-based water quality monitoring system for real-time measurements using Total Dissolved Solids (TDS) sensors, pH sensors, and temperature sensors. The device measures parameters including temperature, pH , and salinity. Some of these values, such as salinity, are inferred through mathematical relationships from the TDS sensor readings [9]. Chuzaini (2022) has also developed IoT-based water quality monitoring which is used to measure temperature, pH , and TDS levels in water. Water sample measurements were carried out in rural areas, near the sea, and in industrial areas with high measurement accuracy[10]. The monitoring system that has been created by Adityas et al. (2021) can measure temperature, pH , salinity, and turbidity parameters. However, there are still shortcomings in the form of measurement results that are not clearly presented or easily interpretable by end users because the data on the website is only presented in graphical form, making it quite difficult for lay users to understand the water quality levels of the testing system[11].

Based on the explanation above, many water quality monitoring systems have been developed. However, there are constraints on the number of sensors, the lack of water quality parameters that can be measured, and the user interface design lacks interactivity and visual clarity, making it difficult for non-experts to interpret water quality data. In this research, an IoT-based water quality monitoring tool will be developed for real-time monitoring of water quality in aquaculture businesses. The components of this monitoring system consist of a microcontroller, TDS sensor, temperature sensor, and LCD touchscreen which are connected in a box enclosure. Arduino IDE is used for programming and Blynk used as more communicative user interface to display water quality monitoring results.

Research Method

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

The research begins with the design of both hardware and software components. The hardware includes a microcontroller, temperature sensor, TDS sensor, and a Human Machine Interface (HMI) device. The software is developed using Arduino Uno, MySQL, Node-RED, and Blynk. The system architecture of the proposed water quality monitoring device is illustrated in Figure 1.

Fig. 1. System Architecture for Water Quality Monitoring Device.

Fig. 1. System Architecture for Water Quality Monitoring Device.

Sensor inputs-temperature and TDS- are collected via devices programmed through the Arduino Uno. The sensors provide temperature data and, from the TDS sensor, additional values such as electrical conductivity, resistivity, and salinity are derived. The ESP32 microcontroller transmits the data to a MySQL database. Node-RED retrieves the data from the database and forwards it to Blynk for
visualization. Blynk functions as a data visualization platform, displaying sensor readings on Android-based devices. In addition to Android, sensor data is also visualized on a dedicated HMI device. Figure 2 shows an explanation of the workflow of the water quality monitoring device system based on the following flowchart.

Fig. 2. Flowchart of IoT-based Water Quality Monitoring Device System.

Fig. 2. Flowchart of IoT-based Water Quality Monitoring Device System.

The system operation starts with the Microcontroller Unit (MCU), ESP32, establishing an internet connection. Once connected, the sensors begin collecting analog data, which is then processed by the MCU. This data can be displayed directly on the system device using HMI, or via Android using Blynk. The MCU manages data flow by sending the processed information to a MySQL database. Node-RED retrieves the data and transmits it to Blynk, which serves as the user interface platform. The system provides readings for temperature, TDS, electrical conductivity (EC), salinity, and resistivity based on sensor input.
Subsequently, the water quality monitoring device is tested using various water samples to evaluate its performance. Testing was conducted to evaluate the performance of key components, including the ESP32 microcontroller, DS18B20 temperature sensor, TDS sensor, and the integrated system as a whole. The water samples used consisted of three samples. The following water samples were used in the evaluation:
a) Sample A: Commercial mineral water (Brand A).
b) Sample B: Lake water collected from the campus of Malang State University.

The samples were analyzed using the developed monitoring system and compared with results from the Hanna Instrument HI98194, a standardized measurement device. The data obtained were compared to determine the difference in measurements and to find the relative deviation obtained using the following equation [12]:

Where represents the values measured by the system's sensor, and represents the values obtained from the standard measuring instrument.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

The system was developed using two sensor probes: a temperature sensor and a TDS sensor. The TDS sensor probe was used to measure several parameters, including TDS, electrical conductivity (EC), resistivity, and salinity. The two probes can be seen in Figure 3. The sensor probes are connected to an ESP32 microcontroller, selected for its built-in Wi-Fi capabilities.

Fig. 3. Sensor probes used on the system.

Fig. 3. Sensor probes used on the system.

As illustrated in the flowchart, the sensors read water quality parameters and generate analog signals, which are then processed by the ESP32. The measured water quality data can be displayed directly through the Human Machine Interface (HMI) in the form of a touchscreen display. Integrating HMI into this device enables a more interactive interface, enhanced by touchscreen capabilities, which surpasses the functionality of conventional LCDs [13]. The HMI screen is integrated into the system and housed in an enclosure box, connected to the sensors and the MCU (Microcontroller Unit). Figure 4 displays the interface used to show the measured water quality parameters, namely temperature, TDS, EC, resistivity, and salinity.

Fig. 4. Interface display on HMI's device.

Fig. 4. Interface display on HMI's device.

Blynk is used as the interface to display the water quality measurement results. This software is commonly used in IoT applications and is accessible via Android or web platforms. First, the data processed by the MCU is transmitted to a MySQL database. Next, Node-RED, which functions as a data handler, retrieves the uploaded data from the database and is tasked with sending the data to Blynk. By utilizing Blynk technology, data processing becomes significantly more efficient compared to using a microSD card for data storage, as data can be stored in the cloud and accessed via the internet at anytime, thereby reducing production costs [14]. In Figure 5, the Blynk interface displays the results of water quality parameter measurements accessed via Android.

Fig. 5. Blynk interface on android.

Fig. 5. Blynk interface on android.

The system was tested by collecting water quality data from two samples and comparing the results using the Hanna Instrument HI98194. Comparisons are made to obtain relative deviations. Where then the relative deviation is known to be the accuracy of the system that has been created. Data were recorded over a 50 second period, with measurements taken every 5 seconds. The comparative results for each water quality parameter are presented in the tables below.

Table 1. Temperature measurement results on the system and HI98194.

Temperature Measurement (°C)
Sample A | Sample B
System | HI98194 | Difference | Relative Deviation (%) | System | HI98194 | Difference | Relative Deviation (%)
27.8 | 27.83 | -0.03 | 0.11 | 27.6 | 27.86 | -0.26 | 0.93
27.8 | 27.9 | -0.1 | 0.36 | 27.6 | 27.92 | -0.32 | 1.15
27.8 | 27.92 | -0.12 | 0.43 | 27.7 | 27.94 | -0.24 | 0.86
27.8 | 27.92 | -0.12 | 0.43 | 27.7 | 27.96 | -0.26 | 0.93
27.8 | 27.92 | -0.12 | 0.43 | 27.7 | 27.94 | -0.24 | 0.86
27.8 | 27.92 | -0.12 | 0.43 | 27.7 | 27.95 | -0.25 | 0.89
27.8 | 27.91 | -0.11 | 0.39 | 27.7 | 27.95 | -0.25 | 0.89
27.8 | 27.91 | -0.11 | 0.39 | 27.7 | 27.96 | -0.26 | 0.93
27.8 | 27.89 | -0.09 | 0.32 | 27.7 | 27.96 | -0.26 | 0.93
27.8 | 27.88 | -0.08 | 0.29 | 27.7 | 27.96 | -0.26 | 0.93

Table 1 shows that temperature measurements recorded by the system were relatively stable, whereas on the HI98194 device, the temperature readings are more varied. These variations may be attributed to the sensitivity and resolution differences between the devices. The highest deviation value in sample A measured by the system was , while the lowest was . In sample B , the highest deviation is , while the lowest deviation is . This shows that the performance of the temperature sensor in the system being created is appropriate because it has a relatively small shift. The treatment given to each sample was the same, where the room temperature, sample water's volume, and the measurement time were constant. An observation of note during temperature measurements was that the system produced more consistent readings than the measurements produced by the HI98194, which fluctuated even within a small range. The recorded temperature measurements showed low deviation. However, compared to the device developed by Chuzaini (2022), higher measurement accuracy was achieved due to its ability to detect temperature up to two decimal places, enabling more precise and specific readings [10,15].

Table 2. TDS measurement results on the system and HI98194.

TDS Measurement (ppm)
Sample A | Sample B
System | HI98194 | Difference | Relative Deviation (%) | System | HI98194 | Difference | Relative Deviation (%)
180 | 185 | -5 | 2.70 | 315 | 329 | -14 | 4.26
180 | 185 | -5 | 2.70 | 315 | 328 | -13 | 3.96
179 | 182 | -3 | 1.65 | 314 | 328 | -14 | 4.27
179 | 181 | -2 | 1.10 | 313 | 328 | -15 | 4.57
178 | 181 | -3 | 1.66 | 313 | 329 | -16 | 4.86
177 | 180 | -3 | 1.67 | 314 | 329 | -15 | 4.56
179 | 180 | -1 | 0.56 | 315 | 330 | -15 | 4.55
180 | 179 | 1 | 0.56 | 316 | 329 | -13 | 3.95
176 | 178 | -2 | 1.12 | 315 | 330 | -15 | 4.55
176 | 178 | -2 | 1.12 | 315 | 330 | -15 | 4.55

In Table 2, the readings taken by the TDS sensor regarding TDS levels in water have varying values. In sample A , the highest deviation was and the lowest deviation was . Meanwhile, in sample B, the highest deviation value was higher than sample A, namely , and the lowest was . The difference in measurements that occur in sample A and sample B is relatively the same. To eliminate errors in differences in measurements made by the system with the HI98194 standard measuring instrument, a recalibration process is required in the programming of the TDS level readings on the system that has been created. Based on the results above, adding a different value can be a solution to adjust the measurements made by the system on HI98194 so that systematic errors can be reduced and measurements obtained that are close to the results of
standardized measuring instruments. However, the interesting thing is that the measurements made by the system tend to be more stable in the measured TDS levels compared to the HI98194 which experienced fluctuations, especially in the measurement of sample A .

Table 3. EC measurement results on the system and HI98194.

EC Measurement (μS/cm)
Sample A | Sample B
System | HI98194 | Difference | Relative Deviation (%) | System | HI98194 | Difference | Relative Deviation (%)
360 | 375 | -15 | 4.00 | 630 | 658 | -28 | 4.26
360 | 370 | -10 | 2.70 | 630 | 656 | -26 | 3.96
358 | 364 | -6 | 1.65 | 628 | 656 | -28 | 4.27
358 | 363 | -5 | 1.38 | 626 | 657 | -31 | 4.72
356 | 361 | -5 | 1.39 | 626 | 658 | -32 | 4.86
354 | 360 | -6 | 1.67 | 628 | 657 | -29 | 4.41
358 | 359 | -1 | 0.28 | 630 | 659 | -29 | 4.40
360 | 358 | 2 | 0.56 | 632 | 658 | -26 | 3.95
352 | 357 | -5 | 1.40 | 630 | 660 | -30 | 4.55
352 | 357 | -5 | 1.40 | 630 | 660 | -30 | 4.55

Table 4. Resistivity measurement results on the system and HI98194.

Resistivity Measurement (kΩ.cm)
Sample A | Sample B
System | HI98194 | Difference | Relative Deviation (%) | System | HI98194 | Difference | Relative Deviation (%)
0.16 | 0.18 | -0.02 | 11.11 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67
0.16 | 0.17 | -0.01 | 5.88 | 1.4 | 1.5 | -0.1 | 6.67

Table 5. Salinity measurement results on the system and HI98194.

Salinity Measurement (psu)
Sample A | Sample B
System | HI98194 | Difference | Relative Deviation (%) | System | HI98194 | Difference | Relative Deviation (%)
0.16 | 0.18 | -0.02 | 11.11 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38
0.16 | 0.17 | -0.01 | 5.88 | 0.29 | 0.32 | -0.03 | 9.38

In Table 3, the EC measurements carried out by the system have quite a large difference. This can be seen in the deviation that occurred in the measurements of sample A and sample B which had the highest deviations of and respectively. This quite large deviation occurred because the EC level measurement was carried out using the same sensor probe, namely the TDS sensor. The EC
value itself is obtained using the relationship equation between TDS and EC. Thus, a lack of appropriate calibration can result in quite large differences between measurements and measurement results with standardized measuring instruments. The stability of the system in EC measurements also looks more stable, especially in measuring sample A against the HI98194 standard measuring instrument. This also occurs in the resistivity and salinity measurements shown in Table 4 and Table 5. The resistivity and salinity values are also taken based on the equation of the relationship between TDS, EC, resistivity and salinity. However, even though there is a large deviation, the difference in measurements made using the system that has been created when compared with standard measuring instruments has a relatively small difference, so it is considered to have met the measurement tolerance. The deviation value of the resistivity itself in sample A and sample B has a maximum value of and respectively. Meanwhile, for salinity values, the system's highest deviation from the standardized instrument measurement results in sample A was and .

Based on the results of the measurements that have been carried out, further recalibration is required for TDS, EC, resistivity and salinity measurements in order to obtain more accurate results that are close to the measurement results of standardized measuring instruments. So, later the application of the water quality monitoring system that has been created can be easily used with high measurement validity.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

A water quality monitoring system was successfully developed and implemented, integrating sensor data with real time display capabilities. The system displays water quality measurements through both an Android application (using Blynk) and a Human Machine Interface (HMI) device. System testing of the temperature sensor demonstrated reliable and stable measurements, comparable to standard instruments. However, recalibration of the TDS sensor is required to enhance measurement accuracy and consistency to match standardized measuring instruments. Such recalibration would increase the system's potential for practical application in aquaculture environments.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

The authors would like to especially thank the LPPM (Institute for Research and Community Service) of the State University of Malang which has assisted in providing research funding with contract number 5.4.571/UN32.20.1/LT/2023.

References

The original version of this paper is available on https://www.scientific.net/EI.16.97.pdf

Y. Rohmawati and K. Kustomo, "Analisis Kualitas Air pada Reservoir PDAM Kota Semarang Menggunakan Uji Parameter Fisika, Kimia, dan Mikrobiologi, serta Dikombinasikan dengan Analisis Kemometri," Walisongo Journal of Chemistry, vol. 3, no. 2, p. 100, 2020, doi: 10.21580/wjc.v3i2.6603.

Supono, Manajemen Kualitas Air untuk Budidaya Udang. 2018.

I. F. Hastari, R. Kurnia, and M. M. Kamal, "Analisis kesesuaian budidaya KJA Ikan Kerapu menggunakan SIG Di Perairan Ringgung Lampung," Jurnal Ilmu dan Teknologi Kelautan Tropis, vol. 9, no. 1, pp. 151-159, 2017.

C. P. Paruntu, "Budidaya Ikan Kerapu (Epinephelus tauvina Forsskal, 1775) dan Ikan Beronang (Siganus canaliculatus Park, 1797) dalam Karamba Jaring Apung dengan Sistim Polikultur," e-Journal BUDIDAYA PERAIRAN, vol. 3, no. 1, pp. 1-10, 2019, doi: 10.35800/bdp.3.1.2015.6924.

M. E. Eshmat and N. Abdul, "Analisis Kondisi Kualitas Air Pada Budidaya Ikan Kerapu Tikus (Cromileptes altivelis) DI SITUBONDO ANALYSIS," vol. 5, no. 1, pp. 1-4, 2013.

G. Valentino, A. A. Damai, and H. Yulianto, "ANALISIS KESESUAIAN PERAIRAN UNTUK BUDIDAYA IKAN KERAPU MACAN (Epinephelus fuscoguttatu) DI PERAIRAN PULAU TEGAL KECAMATAN TELUK PANDAN KABUPATEN PESAWARAN," eJurnal Rekayasa dan Teknologi Budidaya Perairan, vol. 6, no. 2, p. 705, 2018, doi: 10.23960/jrtbp.v6i2.p705-712.

T. Abinaya, J. Ishwarya, and M. Maheswari, "A Novel Methodology for Monitoring and Controlling of Water Quality in Aquaculture using Internet of Things (IoT)," 2019 International Conference on Computer Communication and Informatics, ICCCI 2019, pp. 14, 2019, doi: 10.1109/ICCCI.2019.8821988.

J. Y. Lin, H. L. Tsai, and W. H. Lyu, "An integrated wireless multi-sensor system for monitoring the water quality of aquaculture," Sensors, vol. 21, no. 24, 2021, doi: .

S. Sobri, P. Prayitno, B. Basino, and N. Nurhayat, "Automatic Water Quality Monitoring System With Real- Time Data Type Based on Internet of Things ( IOT ) for Vannamei Shrimp Farming," Urecol Journal. Part E: Engineering, vol. 1, no. 2, pp. 52-63, 2021.

F. Chuzaini, D. Wedi, S. Mata, A. Grogolan, D. Ngunut, and S. Tirta, "IoT Monitoring Kualitas Air dengan Menggunakan Sensor Suhu , pH , dan Total Dissolved Solids ( TDS )," Jurnal Inovasi Fisika Indonesia, vol. 11, no. 3, pp. 46-56, 2022.

Y. Adityas, M. Ahmad, M. Khamim, K. Sofi, and S. R. Riady, "Water Quality Monitoring System with Parameter of pH , Temperature, Turbidity, and Salinity Based on Internet of Things," JISA(Jurnal Informatika dan Sains), vol. 4, no. 2, pp. 138-143, 2021, doi: 10.31326/jisa.v4i2.965.

G. A. Pauzi, M. A. Syafira, A. Surtono, and A. Supriyanto, "Aplikasi IoT Sistem Monitoring Kualitas Air Tambak Udang Menggunakan Aplikasi Blynk Berbasis Arduino Uno,"JURNAL Teori dan Aplikasi Fisika, vol. 05, no. 02, pp. 1-8, 2017.

Karan R. Chandalwar, Nikhita A. Barde, Shirisha S. Pureddi, Tejasvi Uike, Naveen Yadgiri, and Prof. Mahesh Dumbere, "Water Quality Monitoring System Based on IoT," International Journal of Advanced Research in Science, Communication and Technology, pp. 744-751, Nov. 2024, doi: 10.48175/IJARSCT-22300.

R. Arisandi, A. Purno Wahyu W, A. Purno, and W. W. Jumanji -, "DESIGN OF A PROTOTYPE OF WATER QUALITY PHYSICS MEASURING EQUIPMENT BASED ON MICROCONTROLLER."

T. Asyhar and K. Sekarsari, "RANCANG BANGUN MONITORING TOTAL DISSOLVED SOLIDS PADA AIR TANAH BERBASIS IoT," EPIC Journal of Electrical Power Instrumentation and Control, vol. 4, no. 2, p. 149, Dec. 2021, doi: 10.32493/epic.v4i2.1491.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 107-114

doi: 10.4028/p-2GUuyA

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-04-21

Revised:
2025-08-06

Accepted:
2025-08-09

Online:
2026-01-07

IoT Security: Solutions to Mitigate Attacks

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

Andrea Gisselle Menjivar

Florida International University, Miami, Florida

amenj003@fiu.edu

Keywords: IoT, Security, Mitigation.

Abstract

The Internet of Things (IoT) is one of the most transformative technologies in recent years, offering unprecedented connectivity and functionality across a wide range of industries. However, like all internet-connected systems, IoT devices are vulnerable to significant security threats. These vulnerabilities pose serious risks to both residential and industrial environments, as attackers can exploit them to compromise privacy, disrupt services, or cause physical harm. Traditional cybersecurity approaches are often impractical for IoT devices due to their resource constraints and unique architectures. This paper begins by defining IoT devices and illustrating the scale of the security challenges they face. Then it explores the three fundamental pillars of IoT security, confidentiality, integrity, and availability, highlighting their importance and how current techniques aim to uphold them. Finally, the article reviews various mitigation strategies proposed in the literature to provide a clearer picture of the evolving threat landscape and the measures needed to protect user trust and ensure the safe adoption of IoT technologies.

I. Introduction

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

Over the years, the transformative technology known as the Internet of Things (IoT) has significantly changed how we interact with the world around us. IoT is a constantly evolving technology driven by the increasing need for people to live in a more interconnected world. It enables the connection of realworld objects to digital platforms, allowing users to monitor and control them remotely [1]. A simple way to understand IoT is to consider it as any device capable of collecting data and transmitting it to the cloud through an internet connection. This allows users to access data from anywhere. IoTbased systems automate, integrate, and manage processes through sensors and communication technologies [2]. This revolutionary technology is advancing telecommunications and enhancing quality of life globally through its wide range of applications. In manufacturing, it enables increased efficiency and predictive maintenance; in transportation, it improves logistics, real-time tracking, and safety measures; in the energy sector, it optimizes resource use, monitors equipment performance, and improves overall operational efficiency; in retail, it streamlines inventory management, personalizes customer experiences, and provides real-time analytics. IoT is also crucial in the development of smart cities, healthcare systems, and supply chains, among others. By playing a key role across multiple industries, IoT has substantial potential for driving global economic growth. Today, there are approximately 26.66 billion IoT devices in use worldwide, and the global economic impact of IoT is projected to range between USD 2.7 trillion and 6.2 trillion by 2025 [3] [4].

II. Security of Iot Devices

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

As the Internet of Things technology has evolved over the past few years, the risks, and threats to which these types of devices are exposed have not been left behind, and likewise, cyber attacks have increased in both quantity and sophistication. Every day malicious actors develop more ways and employ increasingly complex techniques and tactics to compromise the security measures and to exploit vulnerabilities on Internet of Things devices. Cybersecurity and privacy have become one of the main issues for IoT devices and monitoring applications, and it's mainly because these devices contain plenty of information that attackers can use for their own benefit.

Fig. 1. CIA Triad

Fig. 1. CIA Triad

The statistic from these previous works provides us with a quantitative perspective to understand the scope and magnitude of the risks in security of IoT devices: According to [5], Over 85% of global organizations are projected to utilize IoT devices in various capacities, with around of these businesses lacking confidence in the security of their IoT devices. Furthermore, approximately of IoT devices are vulnerable to diverse attacks when connected to the internet, and a quarter of enterprise attacks are expected to result from compromised IoT devices. These statistics not only illustrate the magnitude of the security concern problems on IoT devices, but it also provides a solid foundation for further investigations and analysis.
To guarantee better security on IoT devices, it is imperative to always take into consideration the three key pillars of security, these pillars are known as the CIA, which are confidentiality, integrity, and availability as shown in Figure 1. Author [6] defines this pillars as follows: confidentiality as the protection of information against unauthorized access to ensure that only individuals or systems authorized have access to the information. Integrity refers to the authenticity of the data, meaning ensuring the integrity of data during communication or verifying the authenticity of information generated from the source. Finally, availability implies that authorized users should be able to access the data whenever they request it. Maintaining a focus on these three important pillars will help us to strengthen security and minimize the risk of vulnerabilities that are always present in this IoT devices.In order for the IoT system to ensure all of these three pillars it should be accompanied with different security methods or models. Security models should work as a whole to ensure these three pillars are always present on the IoT systems so that they can be reliable and secure. This three pillars represent an important role in safeguarding these interconnected devices they become indispensable to preserve the trustworthiness of IoT devices and applications. Since data is always being transferred between the device and the cloud it should always be protected, always available and ensure that no unauthorized users can have access to it.

III. Methodology

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

The main goal of this literature survey aims to investigate on the current mitigation strategies that are being employed in addressing the IoT devices attacks. This work's purpose is to gather insights into different approaches, tools and research carried out by different authors to secure the IoT systems. This work represents the derived findings by thoroughly exploring different IoT security- specific papers extending from 2019-2024, inclusively. The aim was to extract and summarize relevant, common, and impactful IoT security mitigation methods that are being used and inspire researchers for their future

Fig. 2. Deception using Decoys

Fig. 2. Deception using Decoys

investigations on this field. This work categorizes the different methods and tactics that can be used to make the IoT systems and devices more reliable and enhance their security. The investigations cited on this work were selected research in which the author's contributions defined, analyzed, emulated, or simulated the mitigation method or model proposed to mitigate and detect different IoT security attacks.

IV. Using Deception for IoT Security

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

Deception, as the word says it, is to trick or mislead someone by creating false impressions, information or also to manipulate perceptions. In the past few years the cyberspace has adopted this term as cyber deception and author [7] describes it as intentional actions taken to deceive and/or confuse attackers, influencing them to either take or refrain from certain actions that support computer security defenses. Basically, how cyber deception function is a two-step action that includes the defender crafting information that is used to mislead the adversary and the adversary making wrong actions because of the deception.
D. Weissmen in his research proposes to use deception by the creation of duplicates of IoT devices using decoys. as a way to mitigate security risks by confusing the attack target. By having multiple of impostor devices exceeding the number of authentic operational working devices, the likelihood of attackers infecting and gaining control over the targeted IoT endpoint will be reduced [8]. This can be achieved using elements like software instances in virtual machines and it will also facilitate an easier management for network and security administrators. Figure 2 shows a graphic on how this model works by having different decoys for deception making it harder for the attacker to get to the targeted IoT device.
A framework for dynamic defense in SDN IoT-Edge Networks was suggested in [9] for cyber deception. The mechanism in this research produced a satisfactory outcome by supplying a protective measure to UDP, TCP SYN and LAND DDoS attacks by avoiding the compromise of IoT devices through temporary network addresses, without causing additional burdens and ensuring consistent system performance through proactive security measures. This framework is deceptive as it presents a misleading representation of the true network address, this approach acts defensively by preventing communication with the misrepresented hosts. Simultaneously, it consistently filters out unidentifiable traffic, clears flows, and alters IP mappings.
Another research on using deception for mitigating attacks on IoT devices was taken by [10]. In his work, a HoneyComb was proposed as a dark net-centric preemptive cybersecurity defense approach to mislead IoT devices compromised by malware and gather IoT forensic evidence of malware. They achieved this cyber defense method by utilizing IoT scans obtained from the dark net. They established an extensive dark net network as a substantial honeypot by constructing a honeycomb on the premises, enabling widespread interaction with malware-infected IoT devices. As a result, a broad perspective is obtained, and it can provide an extensive and robust examination of the cybersecurity
stance when compared to the standard IoT honeypots which are much more restricted. A record of 1,432,518 interactions and 37,323 malware compromised IoT devices was obtained linked to over 40 types of IoT malware. As a result, a large vantage point of dark net IP addresses, one cloud-based IoT honeypot, and a cluster of virtualized IoT systems helped achieved deception.

V. Using REST APIs for IoT Security

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

As mentioned in this article, the development of alternative IoT monitoring cloud based applications is introducing new and evolving cyber risks to the protection and confidentiality. of the devices and the user's information. To the many risks that these devices are exposed to there is a need to implement IoT systems with robust security measures to prevent attackers from infiltrating the network through the IoT devices and to make sure data is secure while traveling from the device to the cloud. [11] research provides details on how the use of REST (Representational State Transfer) APIs enable the secure exposure of connected devices to both cloud-based applications and users.
This research proposes a model in which middleware is predominantly employed to make device data accessible through REST, concealing the intricacies and serving as a user interface for interacting with sensor data. This model can be used to prevent man-in-the-middle attacks because the API can undergo authentication with the server, and conversely, the server can authenticate with the API. Utilizing REST for machine-to-machine connectivity, IoT APIs enable the secure exposure of connected device data to users, that is why REST APIs are being used widely in the development of IoT networks. The data transfer in REST APIs is typically accomplished by JSON or XML over HTTP protocol, serving as an effective model for heterogeneous systems [12]. A middle ware platform can manage diverse models, acting as the intermediary between smart devices and cloud-based applications. Through the incorporation of a middle ware platform with abstract hardware, it can furnish APIs for communication, data processing, computation, privacy, and security.
As shown in Figure 3 this model shows the integration of API Models and REST APIs with a middleware between the communication of the device and the cloud application. Gateways facilitate the secure exchange of all information through the REST API. The middleware is responsible for managing device registration, identification, and database operations. It ensures data privacy and security by exposing stored data only after authentication and authorization through the REST API. This model proposed, while it doesn't guarantee a non-vulnerable IoT network, it for sure can help in the mitigation on man in the middle IoT attacks.

VI. Enhancing IoT security through the use of Blockchain.

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

Blockchain technology, also referred to as a distributed shared ledger, is an immutable database of records secured by cryptography. This type of technology enables the transfer and storage of digital assets without requiring third-party oversight. There is no requirement for a central authority because devices engage in direct asset exchange with each other, a process known as peer-to-peer. Thanks to these features blockchain can be a promising mitigation solution to manage many security attacks and issues on IoT devices inter networking and communication, Blockchain is a technology that can be well used to enhance the overall security features on IoT technology [13].
In a research carried out by [14] authors incorporated a blockchain in overall Internet of Things system to improve the security of the system indifferent parameters. One of the parameters was access control, with the incorporation of blockchain. Internet of Things devices are signed digitally preventing unauthorized entry. Secure storage was achieved due to the information on every transaction being kept in the blockchain, so the data's integrity can consistently be confirmed. Distributing IoT device

Fig. 3. An IoT system utilizes REST API and Middleware

Fig. 3. An IoT system utilizes REST API and Middleware

firmware information via the blockchain platform ensures firmware authenticity, verifying the authenticity prior to booting and upgrades. This functionality aids in combating malware and counterfeiting. In the aspect of communication and secure status, each device undergoes authentication through a digital signature and utilizes a secure API for data communication, ensuring the overall system's security and integrity. The secure state using blockchain ensures that the smart device will stay secure state even if it fails or if it suffers from any kind of attack.
Leveraging blockchain technology in IoT systems greatly improves the overall security, transparency, and trustworthiness of the system. Additionally, it enhances reliability by incorporating features such as fault tolerance. Although it's known that introducing blockchain is not a simple or straightforward solution it certainly opens up space for more research and investigation on how to implement to mitigate several security attacks on IoT devices.

VII. Using Machine Learning in IoT Security

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

With the continuous advancement of IoT technology, an increasing amount of malware is being spread online and the proliferation of botnets has risen over the years. Malicious software initially establishes a botnet, spreading the bot across a network, and the botnet is utilized in situations involving a large number of infected computers. Numerous studies aim to discover methods for safeguarding the IoT environment from botnet attacks. Nevertheless, significant gaps persist in the development of an efficient detection mechanism [15].
A proposed model for mitigating this botnet attacks was carried out in [16] incorporates Machine Learning in Internet of things technology with cybersecurity. In this model, cybersecurity involved creating botnets and conducting diverse malware analyses by configuring attacker and client machines, and analyzing packets using Wireshark packet capturing tools. At the end, the machine learning models developed predict the analyzed packets. The study proposed certain contemporary machine learning approaches to recognize botnets and malicious traffic behavior. The author used decision tree, linear SVC, random forest, logistic regression, gradient-boosted decision tree and ensemble. Following the development of the model, the outcomes indicated that both the ensemble model and the gradientboosted decision tree model surpassed the performance of other classification models. This method can be utilized to identify various botnet attacks and other types of disruptive network activities. Another finding from this study indicated that a single dataset can serve as the foundation for training a majority of machine learning-based botnet detection models. Researchers could further extend this approach in future work by testing it with novel types of botnets and
evaluating their performance and execution time. Utilizing diverse benchmark botnet datasets, a dynamic framework could be developed to predict future botnet behavior.

VIII. Other Threats and Solutions on IoT Security

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

IoT security encounters many risks and threats, and can be classified threats in different layers. Based on layers author [15] present the following threats and possible solution to prevent and mitigate different cybersecurity attacks and vulnerabilities. For the sensing layer on IoT systems, which is the lower layer in that architecture, security on hardware is the priority. For authentication on hardware, digital signatures can be applied alongside cryptographic hash algorithms that can aid in mitigating potential side-channel attacks. For data privacy on the sensing layer applying a lightweight cryptographic algorithm can prevent unauthorized access to the sensor data.
For the network layer, which has many different embedded wireless devices, communication and monitoring can accomplished at this layer, to achieve this, it must establish a strong authentication process employing a point-to-point encryption algorithm to thwart unauthorized access to the nodes. For security on routing IoT Systems mplementing multiple routing paths is crucial to guarantee the security of routing and the ability to detect errors. This layer, typically, is a vulnerable target for eavesdropping, unauthorized access, and Denial of Service (DoS) attacks. Attackers analyze eavesdropping and network congestion to exploit network privacy and confidentiality. The elevated likelihood of such attacks primarily stems from the distant access mechanism used for IoT and information exchange. To safeguard against any unauthorized access, the key exchange technique should be implemented with a high level of security [6].
For the middleware layer, which is in the middle of the network layer and the application layer, implementing an improved lightweight cryptography algorithm can guarantee confidentiality in both data exchange and storage. Another reliable solution that can be used is using decentralized blockchain for reliable and secure data protection instead of using centralized cloud services.
For the application layer, which is the one for the user, it should be the most secured layer, it should provide the user with multiple authentication services by providing the required hash value data for authentication. A firewall can also be used to enhance security. These are some of the possible solutions that can be researched on or test reliability to make IoT systems more secure every day.
Another method used for and mitigation of DDoS attacks on Internet of things Network was proposed by [18]. The method proposed consists of a unusual statistical pattern based algorithm for detection technique which is called ODIT, Online Discrepancy Test. This algorithm effectively addresses the attack while causing minimal disruption to the regular service and performance of the device, it scales well to large systems and It doesn't depend on assumed baselines and attack patterns; instead, it achieves a rapid and precise detection and mitigation approach for covert DDoS attacks. Authors also introduced a innovative intrusion detection ads well as a mitigation framework which employed as an online, scalable, and nonparametric algorithm for detecting anomalies, this method can be further investigated to de applied to IoT systems as a technique for mitigation DDoS attacks.
A groundbreaking security mechanism called LEDEM for MDA sent by wireless IoT to the IoT server was proposed and tested by [19]. This model consisted of a machine learning model with semi supervision for detection of attacks and two varied approaches to mitigate IoT-related risks. This method was tested and proven to impede denial of service attacks to the endpoint users even in the event of a wireless IoT attack on the IoT server. Combining new technologies such as machine learning algorithms with the internet of things can make a powerful tool for future research. This method can be studied and delved deeper by probing into different machine learning models to enhance the accuracy of attack detection. In addition to denial-of-service attacks, various security breaches in IoT can pose issues for users. Integrating technologies can result in a comprehensive security solution that addresses all breaches and potential future attacks.
The last mitigation method reviewed for this survey was a anomaly mitigation framework for Internet of Things systems with the use of fog computing proposed in [20]. They proposed a hybrid framework using the fog computing paradigm employed to address the insufficient resources in IoT networks.

This computational paradigm alleviates the computational overhead and related operational demands involved in mitigating anomalies from resource-constrained IoT devices. They tested this model by dividing the framework into two modules, one model was based on signature and the other model was an anomaly based IDS. Both modules used a database containing blacklisted IP addresses and employed an extreme gradient booster classifier. That enabled detection modules to capitalize on their capabilities. The results were that the signature-based model ensured system's ability to accurately detect known attacks through their source, coupled with the anomaly-based detection's acceptable accuracy in identifying zero-day attacks, ensures safety and improves reliability in the IoT network, as proposed by the authors.

IV. Conclusions

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

There are many different attacks that can contribute to disruption of any of the three pillars necessary to ensure the IoT devices integrity and security. Some of these attacks include botnets, unauthorized access, denial of service, man in the middle, eavesdropping, and physical attacks. As the Internet of things technology evolves so does the attacks, so it is important to be aware of and investigate the different methods and techniques that can be used to approach this problem. This paper presented some of the actual methods discovered, implemented, proposed, and simulated by many authors that contributed to making the IoT systems more secure and reliable. Although all these methods mentioned in this work can help reduce the risk of attacks, IoT devices are never a hundred percent secure and there are always attackers trying to exploit any minimum vulnerability they can find. Through the developing of this work, it is expected to other authors and research to get motivated to continue investigating on this topic and apply or discover new methods for future implementations.

References

The original version of this paper is available on https://www.scientific.net/EI.16.107.pdf

A.G. Menjivar, "Mobile App Development for Wireless Monitoring and Configuration of Sensors using ESP8266," in 2022 IEEE Central America and Panama Student Conference (CONESCAPAN), San Salvador, El Salvador: IEEE, Oct. 2022, pp. 1-6. doi: 10.1109/CONESCAPAN56456.2022.9959569.

"Development of a Pure Sine Wave Current Inverter with IoT Monitoring | IEEE Conference Publication | IEEE Xplore." Accessed: Jan. 23, 2024. [Online]. Available:

M. H. Alsharif, A. Jahid, A. H. Kelechi, and R. Kannadasan, "Green IoT: A Review and Future Research Directions," Symmetry, vol. 15, no. 3, Art. no. 3, Mar. 2023, doi: 10.3390/sym15030757

L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, "IoT Privacy and Security: Challenges and Solutions," Appl. Sci., vol. 10, no. 12, Art. no. 12, Jan. 2020, doi: 10.3390/app10124102.

W. Iqbal, H. Abbas, M. Daneshmand, B. Rauf, and Y. A. Bangash, "An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via SoftwareDefined Security," IEEE Internet Things J., vol. 7, no. 10, pp. 10250-10276, Oct. 2020, doi: 10.1109/JIOT.2020.2997651.

T. Varshney, N. Sharma, I. Kaushik, and B. Bhushan, "Architectural Model of Security Threats theirCountermeasures in IoT," in 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India: IEEE, Oct. 2019, pp. 424-429. doi: 10.1109/ICCCIS48478.2019.8974544

C. Wang and Z. Lu, "Cyber Deception: Overview and the Road Ahead," IEEE Secur. Priv., vol. 16, no. 2, pp. 80-85, Mar. 2018, doi: 10.1109/MSP.2018.1870866.

D. Weissman, "IoT Security Using Deception - Measuring Improved Risk Posture," in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), Jun. 2020, pp. 1-2. doi: 10.1109/WFIoT48130.2020.9221223.

H. Galadima, A. Seeam, and V. Ramsurrun, "Cyber Deception against DDoS attack using Moving Target Defence Framework in SDN IOT-EDGE Networks," in 2022 3rd International Conference on Next Generation Computing Applications (NextComp), Oct. 2022, pp. 1-6. doi: 10.1109/NextComp55567.2022.9932172.

M. S. Pour, J. Khoury, and E. Bou-Harb, "HoneyComb: A Darknet-Centric Proactive Deception Technique for Curating IoT Malware Forensic Artifacts," in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Apr. 2022, pp. 1-9. doi: 10.1109/NOMS54207.2022.9789827.

"Securing IoT Devices and SecurelyConnecting the Dots Using REST API and Middleware.pdf."

"Design of Restaurant Billing System (E Bill Resto) by Applying Synchronization of Data Billing in Branch Companies to Main Companies Based on Rest API | IEEE Conference Publication | IEEE Xplore."

K. Košťál, P. Helebrandt, M. Belluš, M. Ries, and I. Kotuliak, "Management and Monitoring of IoT Devices Using Blockchain," Sensors, vol. 19, no. 4, Art. no. 4, Jan. 2019, doi: 10.3390/s19040856.

"IoT Security Enhancement Using Blockchain | IEEE Conference Publication | IEEE Xplore." Accessed: Jan. 28, 2024. [Online]. Available:

R. Patel, Cyber Security in Domain of IoT: A Review Threats and Security. 2020. doi: 10.13140/RG.2.2.20037.47841.

] Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto, and K. Sakurai, "Machine Learning-Based IoTBotnet Attack Detection with Sequential Architecture," Sensors, vol. 20, no. 16, Art. no. 16, Jan. 2020, doi: 10.3390/s20164372.

"IoT Botnet Creation and Detection using Machine Learning | IEEE Conference Publication | IEEE Xplore." Accessed: Jan. 28, 2024. [Online]. Available:

"Timely Detection and Mitigation of Stealthy DDoS Attacks Via IoT Networks | IEEE Journals Magazine|IEEE Xplore." Accessed: Jan. 29, 2024. [Online]. Available:

"Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture | IEEE Journals Magazine | IEEE Xplore." Accessed: Jan. 29, 2024. [Online]. Available:

M. A. Lawal, R. A. Shaikh, and S. R. Hassan, "An Anomaly Mitigation Framework for IoT Using Fog Computing," Electronics, vol. 9, no. 10, Art. no. 10, Oct. 2020, doi: 10.3390/electronics9101565.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 115-125

doi: 10.4028/p-Uhz5HA

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-06-09

Revised:
2025-07-07

Accepted:
2025-07-15

Online:
2026-01-07

Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

Callum Scott-Russell , Simon Barrans and Paul Hudson

¹Queensgate, Huddersfield, HD1 3DH, United Kingdom

Queensgate, Huddersfield, HD1 3DH, United Kingdom

Ryefield Way, Silsden, Keighley BD20 0EF, United Kingdom

Keywords: democratisation, automation, simulation

Abstract

Given the growing importance of simulation in engineering and its increasing adoption by SMEs, it's crucial to find ways for these smaller enterprises to use simulation tools efficiently, despite having fewer experts than larger organizations. After reviewing literature on how knowledge-based engineering can involve non-expert users and examining simulation workflows. A system has been proposed that will allow non users to conduct certain FEA analysis. This system enables non-expert users to adjust parameters within templates created by a simulation expert. It was found that the system could produce results that were very similar to the results of the expert users initial analysis.

Background

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

Finite element analysis (FEA) is a simulation tool used by engineers since the 1960s. In that time, it has rapidly grown to be a powerful tool to aid engineering understanding of how a product will respond to expected conditions. Its use is increasingly seen in smaller companies looking to improve production or reduce costs. These smaller companies, however, do not have the resources or knowledge base to properly exploit the use of FEA, commonly relying on design engineers to do the analysis or employing a single analyst to perform all analyses. As such, there is an increasing demand for a cost-effective way to improve the efficiency of analysis tasks, with a popular method being the democratization of knowledge. This sees people sharing expertise to expand a team's capabilities by distributing the workload whilst maintaining relevant knowledge levels, as can be seen in [1] . Another method for achieving this is using a quality management system (QMS), which acts as a framework to guide other team members in performing analyses and hence reduces the number of errors.

1 Literature Review

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

1.1 Knowledge-based engineering. Knowledge-based engineering (KBE) continues to grow in both academia and industry. This growth is, for the most part, driven by the increasing use of AI. However, KBE can also be used to investigate tools that help democratise organisational knowledge [2]. Reference [3] looks at many established methods for using KBE; however, these are not always used within the industry due to time and cost penalties.
1.2 Knowledge management. Companies cannot always rely on having the same capabilities throughout their operations when centering around personal knowledge. This is even more true for SMEs, where knowledge is concentrated on a more individual level, resulting in a greater risk of knowledge loss [4]. Therefore, disseminating knowledge within an organisation can reduce this risk.

Reference [5] shows that with knowledge management, there are four distinct drivers: motivational, technological, people interaction and organisational. This has led to using tools and software within organisations that act as a central repository for knowledge [6](.This allows people in different departments to access and seamlessly alter documents. While it can store knowledge, it also needs to store the context in which that knowledge is used, as this context is mainly taught within
the company [7]. This can cause issues as time passes and knowledge is lost, leading to gaps previously understood within the organization.
1.3 QMS. QMSs provide a documented procedure to follow when conducting tasks. This ensures that results are easily repeatable by streamlining tasks done frequently. This can give some benefits, as outlined in [8]. However, if not implemented to the company's needs, it can cause disadvantages such as increased costs arising from the software and specialists using the selected software, as outlined in [9].
1.4 Design and simulation communication. A significant obstacle toward the democratisation of knowledge, is the need for design and simulation engineers to be able to communicate necessary information. Refernce [10] looked at potential barriers to communication. Twenty barriers were identified. They can be split into five categories: people, data, tools, process, and product. Their sixteen recommendations were laid out in [11], which focuses on ensuring clear communication, such as "definition of clear and specific objectives" or "Interaction and sharing questions, ideas, and values".

Reference [12] Went on to survey a limited number of companies to obtain their perceptions of the barriers and recommendations, with the main barrier being the "inefficient use of CAD-integrated FEM systems". The primary recommendation they all felt was used to great advantage was "utilising knowledge from experienced designers."

These studies provide some insight into industrial sentiment but do not provide detailed information on implementing their proposals. They highlight a preference for relying on experienced engineers for their knowledge but make no mention of issues that arise when that knowledge base leaves or how that knowledge can be captured
1.5 Democratisation of knowledge. When it comes to democratising the use of software in the company, a popular approach is to use designer-oriented simulation. These tools allow those with fewer specialist skills within the simulation space to run a simulation and obtain meaningful results [13]
1.6 Workflows. Several workflow flows have been developed for use within simulation environments. [14] has a detailed workflow that uses the MATLAB system workflow tool. These tools aid analysts with creating finite element models by automating the more repetitive and timeconsuming tasks.

Workflows can take many different forms and can be used by people with varying levels of expertise, which is why how the workflow communicates its function to both new and existing users is important, as highlighted by [15].
1.7 Automation. When it comes to automation, it is important that different individuals can use the same inputs and acquire the same outputs. This can done through a documentation strategy. Reference [16] looked at information that should be recorded to ensure the repeatability of simulations.
1.8 Best practices. Refernce [17] has looked at using software to democratise simulation knowledge and highlighted three basic informational requirements for the analysis: geometry, loads, and material data. These reports also state that the software should not be able to perform the analysis without these three inputs.

The paper does not investigate scenarios in which the workflow already has some of these factors locked into the simulation, i.e., the model already has its load and materials loaded, and the user only needs to change the geometry. This situation could significantly reduce the skills and understanding needed to answer simple one-dimensional changes.

2 Analysis Autoamtion

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

2.1 Analysis qualtiy management. Before an analysis is democratised, it must move through a QMS document to ensure the simulation setup has a sound foundation. This QMS document records all the decisions made about the simulation setup, the justification for these decisions, and any verification and validation done on the model. When the simulation has been approved by the analyst and the responsible designer, it can then be democratised

The initial step is to determine the subject's purpose, and the information required from the analysis. This information helps guide the creation of simulation by justifying the boundary conditions and methods of result extraction.

The load cases that the simulation will use should be agreed. If possible, the load cases should result in balanced loads, as it will simplify the constraints applied. This also ensures that the model is loaded as the designer intended.

The geometry used in the simulation must be agreed upon. It should also be discussed what geometry dimensions should be parametric to allow for modification in later studies.

The analyst should now be able to identify any remaining constraints to ensure that the model is sufficiently constrained.

Once the designer has signed off on the simulation, the analyst can create it and perform any necessary verification and validation to ensure that the analysis's results are reliable. The simulation can then become a template for later use

Within this work, Simcenter (a simulation solution produced by Siemens) [18] has been used to implement simulation democratization. Simcenter has many tools that are useful for automating the simulation process by providing alternative methods to alter the simulation with existing tools. These include:

Running simulations from batch files. This allows for external inputs to be used in the simulation without the need to open the graphical user interface (GUI).

Equation driven parameters with values being derived from external spreadsheets.

Tools for synchronous modelling allowing geometric features of imported parts in a nonnative file type to be altered. Hence simple parameter driven changes to the geometry can be made without needing to go back to SolidWorks

Direct import of SolidWorks files. This keeps the names applied to faces and bodies but removes the feature trees.
2.2 Access levels. A system where the user can have different access levels would allow nonexpert users to modify controlled parameters and safely run simulations based on their limited knowledge. The outputs can then be tailored to the levels of knowledge to make this information actionable.

The structure of the system developed in this work can be broken down into five access levels, as shown in Fig 1. These access levels are determined by the user's role within the organisation.

Level one is aimed at people outside the engineering team who might be interested in the capabilities of the product e.g. sales get an inquiry that falls outside the initial load case. This level has the lowest level of access for the app, restricted to only being able to select existing geometry and enter loads for the simulation. This level of access allows for simulation runs that do not need much user input and give results that display elements and nodes on a pass/fail measure.

Level two of the system is now focused on members of the engineering team who are not experts in FEA simulation. This level of access gives greater freedom, allowing the user to change geometry details, types of material, and loads. This level is intended to provide a deeper understanding of the effect of making alterations to existing geometry and provides feedback on how these changes can be utilized in further product development. The results given to the engineers from this have increased granularity to allow for a deeper assessment of design changes.

Level three is focused on members of the design team who are trained to be able to create and run simple linear and thermal analyses and is the level where they can use more complex geometry changes that would be difficult to detail with spreadsheet changes.

Level four provides full access to FEA tools for specialists who can use the software to conduct complex, nonlinear analysis.

3 Implementation

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

3.1 Analysis definition and control. In Sim Centre, templates are best set up using the selection recipe feature. This relates simulation objects to CAD features that have certain user-defined attributes. This ensures that the boundary conditions are applied consistently as the analyst intends
and allows for quick modifications to the applied surfaces without the need to leave the CAD environment later.

An app has been developed called the AE analysis app with a graphical user interface (GUI)
This GUI allows the user to create the inputs for the simulation external to the simulation software. This GUI can be access restricted, reducing the risks of the simulation outputting unusable results.

The AE analysis app removes the users' interactions with the comparatively complex simulation software GUI and replaces it with several simple forms.

The GUI login page allows the AE analysis app to identify the tools the users should be able to use. Levels one and two are directed straight to pages that allow them to create and submit inputs for a simulation.

Fig. 1. Levels and tool access

Fig. 1. Levels and tool access

Levels one and two see different input screens as shown in fig 2 which shows the inputs that a level one user will see and be able to manipulate and fig 3 which shows the inputs that a level two user or higher will be able to use. For level one, the dimensions and materials options are hidden, allowing only loads to be changed. This ensures that the user cannot accidentally change the simulation definition.

The loads are edited using an Excel template that contains the calculations required to generate load values that are consistent across the simulations.

The geometry is edited with another Excel template file specifically for the dimensions. This allows consistency for geometry to be modified in pre-defined directions.

The materials selection GUI differs depending on the selected product type. This screen allows the user to view what materials are used and what bodies have these materials applied by default as seen in fig 4. The user can then either change the material of an existing group, create a new group or move the desired bodies into this new group.

Fig. 2. Level one landing screen

Fig. 2. Level one landing screen

Fig. 2. Level one landing screen

Fig. 3. Level two landing screen

Fig. 4. Material application

Fig. 4. Material application

The user would then click the OK button, which walks the user through the final steps of the input process seen in fig 5 shows an example of how a user would input values for the loads applied and fig 6 shows an example of how the user would alter the dimensions of the geometry used. This involves filling in the yellow boxes in the Excel worksheets for the load and geometry input. Green boxes are calculated values that cannot be edited. Once this step is completed, the software takes the important user inputs and the selected files and sends them as an email to the inbox of the individual who has access to the analysis software.

Levels 3 and 4, have two options: an option to move to the same page or since these levels will have direct access to the FEA software, a page that has tools that can be used to help in creating the analysis or converting the analysis to a template. These tools can range from getting the results needed for comparison with real-world tests to giving the features of geometry meaningful names that can then be made to apply boundary conditions consistently when the geometry is changed.
3.2 Analysis submission. When levels one through 4 submit the information, the request is placed in a queue, and when the queue reaches the request, it downloads the user's inputs for the geometry, loads and dimensions and takes the material information as lines of text. In the case of geometry and loads, it then opens a master version of the analysis Excel sheet, which is determined by the product type and geometry file, pastes in the user's value, and saves these new values in the file that the simulation then reads the values from. A batch file uses these inputs to run the desired simulation and produce pre-agreed outputs.

Fig. 4. Material application

Fig. 5. Load Excel table

10 thickness

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

Fig. 6. Dimension Excel table

The outputs are currently in the form of image files and comma separated variable (CSV) files. The images are of locations of interest in the simulation results, agreed upon between the analyst and the designers when the simulation is initially created. The CSV contains relevant results from the elements and nodes on the face within the area of interest. The graphical results are accompanied by a legend and colour scheme that is determined based on the user's level of access. At level one, the user receives results that are split into two colours: a passing colour and a failing colour, normally blue and red. The fail colours are used when the value is above a specified criteria. At levels 2 and up the results are shown on a more granular scale where red is again used to display elements that fail the criteria

The CSV is converted to an Excel sheet and condition formatting is applied based on the outputs agreed upon during the simulation's initial setup. Level one users receive confirmations through email if any of the elements fail, whilst level 2 users and higher will get a copy of the Excel sheet. The Excel sheet contains three pages. Page one states if any elements have failed. Page 2 lists all relevant elements and nodes on the face's surface. Page 3 contains details of elements that have failed the desired criteria. These results are then emailed back to the user and automatically saved to the user's PC.

4 Example Application

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

An example of a lever arm used in a linear actuator is presented. This lever arm has a pin that slides in a channel to control its position.

This analysis allows the user to adjust the load applied to the lever arm at its maximum position in this channel as well as information about the bolting pattern and the thickness of the lever arm.
4.1 Single component (initial creation). fig 7 shows an analysis of the lever arm is initially created by an expert user with the use of the FEA software. This setup is then used as the basis for the template.

Fig. 7. Lever arm analysis setup

Fig. 7. Lever arm analysis setup

The hydraulic force is provided by the customer as a direct input from the product specification.
The hydraulic reaction force is then equal and opposite to this force acting on the hub side of the lever arm

hydraulic force
hydraulic reaction force
The hydraulic force and its reaction force generates a moment that is reacted by the bolts that attach the lever arm the Hub. The value of this force is calculated using equ 2-3

torque
moment
B number of bolts pairs
bolt distance
moment reaction force
now that the forces are balanced a three-point constraint system is created to make the constraint of the solution Fig 8 shows the results of the simulation before it was converted into a template that the AE analysis app could use. The image is taken directly from the FEA software. The results here show areas where the yield stress is exceeded in red and lower levels of von Mises stress in blue.
4.2 Single component (app). The results produced via the AE analysis app, with the same model parameters as used previously are shown in fig 9. These results are taken from the output png provided by the program. These show a slight difference to the results in fig 8 . It was found that this was due
to the full template using a value extracted directly from an Excel sheet whilst the analyst reads the value from the Excel sheet and manually types it in. As such, the AE Analysis app uses more precise data; it can be concluded that the app template's setup is correct.

Fig 10 shows the results output for a level two or higher user showing the same maximum and minimum stresses with the maximum stresses in the same region as in fig 8 which indicates that the max and minimum for both of the app runs needed to produce these two images produced the same results.

Fig. 8. PNG result analysis setup

Fig. 8. PNG result analysis setup

Fig. 9. PNG result template use

Fig. 9. PNG result template use

Fig. 10. PNG result template use

Fig. 10. PNG result template use

Now that the images have been compared which shows a slight difference in the maximum and minimum values we can check the nodal values.the nodes for this comparison are picked at random. Fig 11 shows the results extracted by the expert user, and the Excel sheet for the level one user. Fig 12 shows the results that are automatically extracted by the app. The comparison of these randomly selected nodes shows a good correlation between the expert user and app runs to 4 significant figures. this should provide confidence that the results that the app generated will be as reliable as the results that are produced by an expert user.

element ID | Node ID | Stress(MPA)
423 | 5537 | 275.2643E+0
1100 | 5394 | 278.9623E+0
1127 | 5448 | 275.4910E+0
7077 | 5365 | 275.1805E+0
7362 | 5656 | 275.8981E+0
7720 | 5475 | 276.1589E+0
8322 | 5297 | 275.4562E+0
8501 | 5468 | 276.1013E+0
8701 | 5562 | 276.2300E+0
9667 | 4568 | 276.0746E+0

Fig. 11. Random Selection of results in the FEA model

element ID | Node ID | Stress(MPA)
423 | 5537 | 275.2643E+0
1100 | 5394 | 278.9623E+0
1127 | 5448 | 275.4910E+0
7077 | 5365 | 275.1805E+0
7362 | 5656 | 275.8981E+0
7720 | 5475 | 276.1589E+0
8322 | 5297 | 275.4562E+0
8501 | 5468 | 276.1013E+0
8701 | 5562 | 276.2300E+0
9667 | 4568 | 276.0746E+0

Fig. 12. Same results points as figure 11 but from the app excel file

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

The AE Analysis app drastically reduces the user's required skill set by removing them from both the creation of the simulation and restricting their interactions with software to simple GUIs or common workplace programs such as Excel.

The AE Analysis app allows for quick modifications to an existing analysis to see how these changes affect the results.

The AE analysis app allows controlling user inputs based on a user's knowledge without affecting the result.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

This work was supported by Innovate UK under KTP Prog. No. KTP13362.

References

The original version of this paper is available on https://www.scientific.net/EI.16.115.pdf

Throp, E. (2024, 11-12 June 2024). Simulation Deployment the MDLZ journey NAFEMS UK Coneference 2024, Staffordshire.

Kügler, P., Dworschak, F., Schleich, B., & Wartzack, S. (2023). The evolution of knowledgebased engineering from a design research perspective: Literature review 2012-2021. Advanced engineering informatics, 55, 101892.

Baikerikar, P., & Turner, C. J. (2021). Comparison of FEA simulations and experimental results for as-built additively manufactured dogbone specimens. The International Journal of Advanced Manufacturing Technology, 115(9), 2839-2851.

Sippola, R. (2023). Knowledge management in an SME: knowledge management as a tool to alleviate vulnerabilities to knowledge losses in the case of a small high-tech enterprise.

Thomas, A. (2024). Digitally transforming the organization through knowledge management: a socio-technical system (STS) perspective. European Journal of Innovation Management, 27(9), 437-460.

Cocca, P., Schiuma, G., Viscardi, M., & Floreani, F. (2022). Knowledge management system requirements to support Engineering-To-Order manufacturing of SMEs. Knowledge management research & practice, 20(6), 814-827. . 2021.1939174

Olalekan, E., Awolaja, A. M., & Otulana, O. K. (2023). Knowledge Retention Strategy and Performance of Small and Medium Enterprises in Ado-Ekiti Metropolis. International Journal of Business, Management and Economics, 4(3), 203-211.

Rusjan, B., & Ali, M. (2010). Capitalising on ISO 9001 benefits for strategic results. The International journal of quality & reliability management, 27(7), 756-778.

Zivaljevic, A., Zakic, K., & Bevanda, V. (2022). What would QMS implementation really bring to a company?- Theoretical review on benefits and disadvantages researched in practice. Journal of organizational change management, 35(6), 805-845.

Schweigert-Recksiek, S., & Lindemann, U. (2018, 2018). Improvement opportunities for the collaboration of design and simulation departments - An interview study.

Schweigert-Recksiek, S., & Lindemann, U. (2020). CHOOSING THE RIGHT MEASURES TO IMPROVE COLLABORATION BETWEEN DESIGN AND SIMULATION DEPARTMENTS. Proceedings of the Design Society: DESIGN Conference, 1, 727-736.

Schweigert-Recksiek, S., Hagenow, N., & Lindemann, U. (2021, 2021). Evaluation of improvement opportunities for the collaboration of design and simulation - An industrial multimethod study. Cambridge.

Tickel, B. (2025, 19-22 May). Democratization, Upfront Simulation and the Role of the Simulation Expert NAFEMS World Congress 2025, Salzburg, Austria.

Spencer, N.A. (2023). Finite Element Analysis System Workflow Tools.

Uhrmacher, A. M., Frazier, P., Hähnle, R., Klügl, F., Lorig, F., Ludäscher, B., Nenzi, L., RuizMartin, C., Rumpe, B., & Szabo, C. (2024). Context, Composition, Automation, and Communication: The C2AC Roadmap for Modeling and Simulation. ACM Transactions on Modeling and Computer Simulation, 34(4), 1-51.

Zhu, Z., Chen, M., Qian, Z., Li, H., Wu, K., Ma, Z., Wen, Y., Yue, S., & Lü, G. (2023). Documentation strategy for facilitating the reproducibility of geo-simulation experiments. Environmental modelling & software : with environment data news, 163, 105687.

Hudson, S., Reed, Z., & Singh, R. (2023, 15-18 May 2023). Implementing Democratized Structural Simulation NAFEMS World Congress 2023, Tampa, Florida, USA.

Simcenter 3D. (n.d.). Siemens Digital Industries Software. .

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-16/978-3-0364-3030-0

Engineering Innovations, ISSN: 2813-1002, Vol. 16, pp 127-138

doi: 10.4028/p-M6fTQm

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-04-21

Revised:
2025-05-13

Accepted:
2025-05-20

Online:
2026-01-07

Game-Theoretical Viewpoint to Optimal Design

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

Vladimir Kobelev

University of Siegen; Faculty IV: School of Science and Technology. D-57076 Siegen

vladimir.kobelev@uni-siegen.de ORCID: https://orcid.org/0000-0002-2653-6853

*Correspondence: vladimir.kobelev@uni-siegen.de

Keywords: optimal design with uncertainties; continuous game theory; games on unit square; quadratic games unit square; game formulation for a potential asteroid impact

Abstract

The main goal of this study is to improve the game-theoretical approach for making the best structural design choices in situations where we are not sure what the external loads will be. The game formulation is used to improve games with different control functions. The games on a unit square need at least two levels because of the basic need for optimization. In a stratified game, the levels are called the substratum and superstratum levels. At the basic level, the way to improve something is like the payoff function, with "ordinal players" trying to make the respective payoff functions as small or as big as possible. The strategies used by these "ordinal players" are limited by their resources, and the value of the substratum game on the unit square is reflected in these strategies. At the upper level of the game, its value depends on the design parameters. The "designer," or the main player, controls these design parameters. If there are multiple players with different goals, the game decides how to handle that. On the other hand, if there is only one cardinal player, finding the maximum value of the superstratum game is the same as a regular optimization. The following text will give you all the information you need. The text presents the exact solutions for two different types of quadratic game formulations: the matrix game and the game on the unit square.

1. Principles of Interdependent Decision Making

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

Game theory is a field of mathematics that examines mathematical models of decision-making in situations where there are two or more parties involved in a competitive endeavor to exert influence over the progression of the conflict in a manner that aligns with their individual objectives. The way we think about making the best decisions using math is called operations research. This means that game theory is a type of math that is used in operations research. Mathematical game theory is an important part of operations research. The subject has been used in many different areas, such as economics, management, industry, agriculture, the military, construction, trade, transportation, communications, and more.
Game theory is a way of thinking about situations where people are both competing and working together. It helps us understand what happens when people make decisions that are based on reason. This theory is based on the principles of mainstream decision theory and economics. According to these principles, people behave rationally when choosing actions that will give them the best results, given the limits they face. Game theory is the study of how people make decisions when the result depends on what other people do. A key point to know about these situations is that each person in charge has only some control over the final results. Game theory is the scientific study of how people make decisions when they are playing a game. The goal of game theory is to understand how people will interact with each other in a way that leads to a specific result. But in many cases, one of the people involved can't be considered a conscious individual with preferences and goals. Therefore, it's not fair for the other players to assume that this person will act logically.
The way operations research tasks are grouped depends on how much information the person making decisions has about the situation being studied. The most basic information about the situation is either deterministic or stochastic. In the deterministic case, the conditions under which decisions are made are fully known. On the other hand, the stochastic case requires a thorough understanding of the possible conditions and their associated probability distributions. In these cases, the goal is to find
the highest or lowest possible value of a function (or its mathematical expectation) while considering the given limits. In game theory, uncertainty can come from many different sources. However, it is commonly understood that these events are often the result of deliberate actions by the other parties involved, who are motivated by their own objectives. In this regard, game theory is often seen as the theoretical framework that includes mathematical models of making the best decisions in situations where there is conflict. Therefore, it can be suggested that game theory models can explain many different things. These include things like economic, legal, and class conflicts; human interaction with nature; and biological struggles for survival. In the study of game theory, all of these models are called "games."
Game theory was utilized in the formulation of optimal solutions [1], [2], [3]. The work [4] is devoted to game control, observation and search problems for dynamical systems. The systems we're looking at are explained using equations or formulas that involve finite differences. We assume a conflict situation, which is a common element in game theory. The system is influenced by two different forces. One of these forces is controlled by one party, and the other force is not controlled by the controlling party. This other force is either associated with the opponent or with factors that are not known or controllable ("nature"). The idea of "playing against nature" was explained in detail in the standard book [5]. Problems are posed and solved about how to control the system in the presence of various kinds of adversary influences, uncertain factors, incompleteness, and imperfection of incoming information about the process. The best possible result is known as "optimality of control." This is understood in the minimax sense. That means it's understood from the point of view of the controlling party. They want to get the best result possible. They want this result without the other party's influences.
In order to ensure compliance, the role of the payoff function can be explained in terms of the smallest eigenvalue of the inverse operator (in the continuous case) or the response matrix (in the discrete case) [6]. This is a key part of the game, and it's called the upper game value. In a certain type of convex game, this specific value is equal to the game's value. The best strategies for the "designer" and "nature" are set [5]. The same payoff functional, which is a way of measuring success, is used for both players. This game is different from the classic matrix game. The "designer" changes the payoff matrix coefficients, while "nature" adjusts the left and right vectors of the payoff function. The left vector is the same as the transposed right vector.
The main goal of this study is to use a game-theoretical approach to make the design of structures better when it is not clear what the external conditions will be. [7]. The basic idea can be explained simply as follows:
In the context of classical structural optimization, the "cardinal" players are assumed to perform the role of "control functions." The "cardinal" players also change the rules of the game. In structural optimization, the "cardinal" players are responsible for determining the coefficients of the governing equations. Their job is to set the rules of engagement, which are the conditions under which some players fight each other. The "cardinal players" have predetermined actions because their decisions are made during the design phase. In the stratified game approach, the "cardinal" players are defined as the "super-stratum."
Also, the other participants act according to the governing equations. In the context of the stratified game approach, the "ordinal" players are the "substratum." The "ordinal" players are allowed to make decisions within their assigned level. However, they are not allowed to change the governing equations. The "original players" can change their actions because they make decisions during the exploration phase. The "ordinal" participants represent external forces. In the business world, external factors are often referred to as "nature." Because of this, the games are called "games against nature."
The "ordinal" participants try to counteract the effects of nature to mitigate potential risks or to achieve the most favorable outcome. People in these roles are now called "operators." The relationship between the two "ordinal" players, nature and operators, is studied using game theory. This is a field of study that uses common principles to analyze strategic interactions. The payout matrix is important for Player I and the profit that Player I makes if he uses his strategy when Nature is in a certain state. It may be better to think of Nature's strategies as "states" instead of strategies. The main
idea of this study is that we don't know what causes nature to choose one state over another. Statistical decision theory is a way of analyzing situations where we have to make a decision. In these situations, probabilities (whether they are objective or subjective) are important factors. At the ordinal level, the focus will be on the analysis of zero-sum games in both matrix and unit-square formulations. The definitions in question were visible within the work [8]. The technique could be extended to the bimatrix and bi-integral formulations.
The payoff matrix is modified by the player who is acting in accordance with the "cardinal" strategy. The change is made to help the player who wins. Once the "substratum" game has started, you can't change the payout matrix. The "cardinal" players have the same names as those used in classical structural optimization. The "cardinal" players' freedom is described as "control functions" or "design parameters." If there's only one "cardinal" player, there won't be any conflicts of interest. If there are several "cardinal" players with different interests, there might be a conflict. The conflict mentioned earlier leads to the "superstratum" game on the upper level. This is not an unusual situation in the engineering field. For example, the system designer's goal is to make the system work as well as possible. The production designer wants to use the system in the best and cheapest way. The operating ecology manager's job is to make sure that the system emits as little as possible while it is being used. The production ecology manager knows that it is important to reduce emissions during manufacturing. The customer's main goal is to reduce the system's overall cost and operational expenses. However, these goals are not always the same, which can lead to problems among the "main players."
This way of thinking can be used for other problems that involve making decisions at different levels. To illustrate, lawmakers can be considered the "super-stratum" of a social game. The actions of the superstratum players directly impact the game's governing equations, including those related to taxation and ecological laws. Meanwhile, the players in the "substratum" follow the laws that define their goals and the way they go about achieving them.

2. Antagonistic Matrix Stratified Games

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

2.1 Common matrix games

This section contains basic information about the theory of finite matrix games. Researchers have studied how to find the best mix of strategies in a group of mixed strategies, the properties of optimal mixed strategies, and methods for solving matrix games. To mathematically describe a game, you need to list all the players and their strategies. This description also includes the exact amounts of money that each player will win after the other players choose their strategies. Consequently, the game is transformed into a formally defined procedure, which lends itself to unique mathematical analysis.
Games can be grouped in different ways. First, non-coalition games in which each coalition (a set of players acting together) consists of only one player. The cooperative theory of non-coalition games is a way of thinking about how players can work together temporarily to form groups and share any winnings they win or make decisions together. Second, coalitional games are games where players are divided into teams according to the rules of the game. Coalition members can share information and make decisions together. In the present study, the focus is exclusively on pure conflict situations. The antagonistic games will be displayed for the sake of simplicity.
Game theory is a field of study that starts with a simple model called a "matrix game." In this model, two players compete, and each player has a limited set of strategies. When one player wins, the other player loses:

where and are nonempty sets, and the function is called an antagonistic stratified game in normal form. The elements and are called the strategies of ordinal players 1 and 2, respectively, in the game . The elements of the Cartesian product (i.e., the pair of strategies where and ) are situations, and the function is the win function of player 1 . The payoff of player 2 in situation ( ) is assumed to be equal to
. Therefore, the function is also called the win function of game itself, and game is called a zero-sum game.
In the standard game theory [9] [10], the elements of the matrix are defined as fixed values. Both players know these values (i.e., "ordinal players"). The main difference between the standard game formulation and the actual formulation is that the matrix elements depend on the control of the "cardinal" player. There are two levels in the game. The "cardinal" player acts on the "upper level." The "ordinal" players perform on the "lower level."
Accordingly, using the accepted terminology to define game , it is necessary to define the sets of strategies of ordinal players 1 and 2 and also the winning function , defined on the set of all situations .

2.2 Stratified matrix games

The stratified game is interpreted as follows. Ordinal players simultaneously and independently choose strategies , as explained in [8]. In the substratum game, ordinal player 1 then receives a payoff equal to , and ordinal player 2 receives . The elements are called the strategies of cardinal players in the stratified game .
In a stratified game, the elements are referred to as the strategies of cardinal players. The superstratum game pertains to the strategies for cardinal players that ensure the maximal and minimal values of the substratum payoff function. When there is only one cardinal player, the superstratum game becomes an optimization problem. On the other hand, when there are two or more cardinal players, their interests may be in opposition. This is known as an antagonistic game. The total payoff for both players on the lower level is zero. The goal function on the "upper level" will be the payoff on the "lower level" game of the oppositely acting ordinal players, e.g., the "nature" and "operator". The "designer" makes this goal function better by thinking about it.
In the matrix game, ordinal player 1 is assumed to have only strategies. The set of strategies available to the first ordinal player, , must be ordered. That is, a one-to-one correspondence must be established between and . The same process must be repeated for the second ordinal player, with and .
The substratum matrix game is defined by the matrix :

The game is realized as follows. Player 1 selects a row, , and player 2 (simultaneously with player 1 and independently of him) chooses a column, . Ordinal player 1 then receives a payoff, , and ordinal player 2 receives . If the payoff is a negative number, the original player has actually lost money.
We denote the substratum game with win matrix by and refer to it as an game, in accordance with the dimensions of matrix with the fixed values of strategies for cardinal players .

2.3 Equilibrium situations and minimax principle

The idea of optimality in the lower layer is derived from the construction of an equilibrium situation, known as the equilibrium principle. If there's a balanced situation, the minimax is the same as the maximin. According to the definition of the equilibrium situation, each player can communicate their optimal (maximin) strategy to the opponent, and neither player can obtain an additional benefit from it. In the antagonistic game, the situation is called an equilibrium situation or a saddle point. At the saddle point, a part of the matrix is the lowest value in its row and the highest value in its column. Any two optimal strategies are a balanced situation, and the reward in it is equal to the value of the
game. Games where both players have a limited number of strategies are called substratum matrix games.
It makes sense that a situation in the game is optimal if changing from it is not good for any of the players. When we have a situation like this , we call it an equilibrium. The idea that something should be built in a certain way to create an equilibrium situation is called the equilibrium principle.
Now, we will look at how the equilibrium principle and the minimax and maximax principles work together in an antagonistic game. For the substratum game to be in a balanced state, it is necessary and sufficient that there exist a minimax and a maximin:

and the equality is satisfied:

Equation 0 shows how the equilibrium principle and the minimax and maximin principles in an antagonistic game are connected. Games that have a balanced state are called well-defined games. Therefore, this theorem creates a way to define a "lower level" game. It can be rewritten like this: If there's a balanced situation, then minimax equals maximin. According to the definition of a balanced situation, each "ordinal" player on the "lower level" can communicate their optimal (maximin) strategy to the opponent. This means that neither player can gain an advantage.

2.4 Mixed solutions

Now, let's say that in the game, there's no balanced situation in the "lower level" game, called "game . In this situation, the maximin and minimax strategies aren't the best. Also, it's not good for the players to follow them, because they can win more money. However, telling the other player about your strategy might lead to even bigger losses than if you had chosen the maximin or minimax strategy. In this case, it makes sense for "ordinal" players to act randomly. This provides the greatest secrecy in the strategy choice. The result of the choice cannot be known to the opponent because the random mechanism is not known to the player until it is realized. The random variable whose values are the "ordinal" player's strategies is called their mixed strategy. With that definition in mind, we'll call the first set of strategies "pure." A random variable is defined by its distribution. We will identify a mixed strategy with a probability distribution on the set of pure strategies. Therefore, the range of a mixed strategy includes pure strategies that are chosen with positive probabilities. The pair of mixed strategies of the "ordinal" players in a matrix game is ( ).. Every matrix game has a mixed strategy equilibrium situation [11]. In simple terms, if a solution exists for mixed strategies, "ordinal" players can always remove the uncertainty of their strategy choice. They can do this by choosing strategies randomly from the set of pure strategies. This happens simultaneously and independently on the "lower level" game.
A random variable is defined by its distribution. We will identify a mixed strategy with a probability distribution on the set of pure strategies of ordinal players. Therefore, the strategy of player 1 in the substratum game is a m -dimensional vector, and it is constrained by the following equation:

where the norm in the real vector space is defined as [12]:

In the same way, player 2's mixed strategy, , is a n-dimensional vector:

The positive natural number p is what decides what kind of game it is. Remember, the numbers and can't be the same.

2.5 Linear matrix games with the probability distributions of the strategies of players

If , the values and are the probabilities of choosing pure strategies and , respectively, when ordinal players use mixed strategies and . We will use and to represent the sets of mixed strategies of the first and second players, respectively. It's clear that the set of mixed strategies for each player is a compact set in the corresponding finite-dimensional Euclidean space (a closed, bounded set). A mixed set is a type of strategy that adds to the options available to the player. An arbitrary matrix game is well-defined within the class of mixed random strategies. The von Neumann theorem of matrix games states that, when , every matrix game has a balanced situation within the context of mixed strategies.
Setting is normal for the application of game theory in economics, political science, and the social sciences [9] [13].
This shows that the mixed strategy for ordinal players is just a probability distribution over their pure strategies. Any event must have a positive probability, and the total probability of all events must be one. Therefore, any mixed strategy must meet the following conditions:

The proof of the theorem is constructive. This means that it can be used to solve the problem. The proof reduces the solution of the matrix game for to a linear programming problem. Therefore, any method used to solve linear programming problems can be used to solve matrix games. The simplex method is the most common way to solve these problems.
2.6 Quadratic matrix games with the restrictions on the vector norms of the strategies of players If , the values and are the Euclidian coordinates of vector strategies of the ordinal players. The Euclidean length of a vector in the real vector spaces and are given by their Euclidean norms:

As the examples show, this is a common situation in engineering and physical applications. In these situations, the actions available to the players are limited. The strategy vectors are limited to a value of one:

The payoff function of the game matrix on the lower substratum level is expressed as follows:

The Lagrange multiplier combines the payoff function 0 with the constraints 11 ), taking into account the nonnegative multipliers :

The payoff function 0 with the conditions 0 has an equilibrium state , which can be calculated using the following equations:

The solution to equation 0 is as follows:

Equations 0 on the left side contain two auxiliary matrices:

The matrix is an square Hermitian matrix. The matrix is an square Hermitian matrix. A real symmetric matrix is Hermitian. A Hermitian matrix is always self-adjoint. It follows from (Theorem 2.8, Section 2.4 in [14]), that both matrices and have the same nonzero
eigenvalues, counting multiplicity. The matrices and are positive-semidefinite (Theorem 7.3, Section 7.1 in [14]). The number of zero eigenvalues of and is at least . Let be the matrix with the smallest dimensions of and . In other words,

The matrix is positive-semidefinite. The eigenvalues of matrix are:

If , then the matrices are equal and have the same set of eigenvalues. If is positive definite, the number of its zero eigenvalues is exactly . The eigenvalues of the positive definite matrix are:

From Equation (12), it follows that . Finally,

Therefore, every matrix quadratic game has a balanced situation when it comes to mixed strategies. The proof of the theorem is constructive. That means it reduces the solution of the matrix game with to a quadratic programming problem. Therefore, any way of solving linear programming problems can be used to solve matrix games.
The solution to the substratum matrix game with constrained actions (Equations 6, 8, and 10) is found. The goal of the Superstratum task is to find the best solution for the "designer" by looking for the extreme value of (14) by changing the design variables.
If players have the same resources, as shown in equation (8), the total payoff will be zero. In the event that player possesses a greater quantity of resources than player , the possibilities available to them are subject to different constraints: .
In this case, the "designer," who shares similar interests with the "operator," tries to reduce the maximum possible damage. The principal possible damage is shown by the major eigenvalue with a negative sign. The goal is to make the largest eigenvalue, ., as small as possible.
If player has fewer resources than player , the following inequality holds: . The designer will guarantee a specific outcome, regardless of the first player's actions, which are designated as "nature." [5]. The equilibrium point corresponds to the minimal positive eigenvalue . In this case, the goal of the "designer" is to make the minimal eigenvalue as large as possible, .

3. Antagonistic Stratified Games on Unit Square

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

3.1 Formulations of the stratified games on unit square

This section contains basic information about the theory of finite antagonistic games on a unit square. [10], [15]. This is a two-person game, also known as a zero-sum game, where the set of pure strategies for two opponent players is a interval . The two players are called "nature" and "operator." Any two-person zero-sum game that has a set of strategies for each player that is a continuum can be reduced to a game on a unit square. Games on the unit square are defined by pay-off functions that are defined on the unit square and functionally depend on the as a parameter or a control function of the cardinal player ("designer").

3.2 Solutions of the substratum games on unit square

Players can use mixed strategies, which are functions that take arguments on the unit interval. The payoff of player "nature" when players "nature" and "operator" apply mixed strategies and , respectively, is, by definition [16]:

If is continuous in both arguments for all possible values of , then

that is, for such a game 0 the minimax principle is valid and there exist a value for the game, denoted by , and optimal strategies for both players. The value pf the game depends functionally upon . If is a scalar or vector parameter, the value of game is a function upon these parameters. Theorems about the existence of values in games (minimax theorems) have been proven under less strict assumptions about the payoff functions. The general minimax theorems imply that there is a value for games on a unit square with limited payoff functions. There are theorems that prove the value of a game in certain types of pay-off functions that are not continuous. However, not all games on the unit square have values.
The structure of the set for games with a unique solution has been determined for games on a unit square with continuous pay-off functions. It is important to note that the set of continuous functions in two variables for which the corresponding game on the unit square has a unique solution, for which the optimal strategies of both players are continuous, and whose supports are nowhere-dense perfect closed sets of Lebesgue measure zero contains an everywhere-dense subset.
It has been determined that there are no general methods for solving games on the unit square. However, for certain classes of games on the unit square, it is possible to find an analytical solution (e.g., for games of timing, games with pay-off functions depending only on the difference of the strategies of the two players, and having optimal equalizing strategies). Alternatively, it is possible to prove the existence of optimal strategies with finite support for such games, thereby reducing the problem of finding a solution for a game on the unit square to the solution of a matrix game. Approximate methods can be used to solve games with continuous payoff functions.
The strategy of ordinal player 1 in the substratum game is an integrable function , which is constrained by the following equation:

where the norm is defined as:

Similarly, the mixed strategy y of player 2 is an integrable function:

The positive natural number in Eq. 0,0,0 determines the class of the game.

3.3 Solutions of the linear substratum games on unit square

The cited literature gives an overview of the methods used to evaluate game values. The usual setting shows that the mixed strategy for ordinal players is just a probability distribution over their pure strategies. Any event must have a positive probability, and the total probability of all events must be one. Therefore, any strategy must meet the following conditions:

3.4 Solutions of the quadratic substratum games on unit square

If , there are Euclidean norms:

This is a common situation in engineering and physical applications. In these situations, the actions available to the players are limited. The modules of strategy vectors are less or equal than two positive limit values:

The Lagrangian combines the pay-off function 0 with the constraints 0,0 , taken with the non-negative multipliers :

For the pay-off function 0 with the conditions 0 , the equilibrium state satisfies the equations:

The resolution of the Eq. 0 reads:

If the kernel is symmetric, the transposed kernels match:

The Equations 0,0 have the same eigenvalues. The kernel is positive-semidefinite. The eigenvalues of the integral equation 0 with the kernel are:

From Eqs. follows, that .
Finally, the values of Lagrange multipliers for each equilibrium point 0are equal to:

As a result, every matrix game has equilibrium situations within the context of mixed strategies. Every equilibrium situation has a game value 0 .

4. Game Formulation for a Potential Asteroid Impact

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

The following section will present the example of the stratified game in structural optimization. 65 million years ago, an asteroid triggered climate change. In 1908, an asteroid explosion in Siberia uprooted millions of trees. In Bavaria, an impact left behind a huge crater. Such disasters should be prevented in the future. The Earth is under constant bombardment from space: countless pieces of rock tumble through space and collide with everything in their path. Fortunately, major catastrophes are rare. But just how surprising the impact of a celestial body can be was demonstrated in 2013,
when the European Space Agency (ESA) joined forces with NASA on a double mission to investigate whether and how a dangerous asteroid could be deflected in an emergency. NASA's DART (Double Asteroid Redirection Test) probe was launched on November 24, 2021 to find out the effects of a deliberate impact with an asteroid. NASA wants to have the DART space probe hit it in the fall of 2022 and change its orbit. The experts call it a "cosmic collision". The United Nations also wants to be prepared for an imminent collision with an asteroid. The organization is therefore setting up an international network to research defensive measures and create a functioning early warning system for the whole world. The international commission in charge of this is called the Space Mission Planning and Advisory Group. This Group develops a technical response strategy for a potential asteroid impact. Europe's space agency ESA is also trying to calculate the trajectory of potentially dangerous asteroids as early as possible. NEOCC coordination center for near-Earth objects collects information on discovered asteroids. Under the name NeoShield-2 [17], Europe also researched asteroid defense options for several years from 2012 under the leadership of the German Aerospace Center (DLR). The HERA mission is a direct result of this research, which recommended an experiment with such a "cosmic collision". As outlined above, the motivation for the game formulation is provided by the cited international activities.
The probability of a "cosmic collision" is high, but the likelihood of it occurring is extremely low. However, such a case is not fully susceptible to prediction. The design task, when faced with conditions of aleatoric uncertainty, can be expressed as a stratified game. Namely, the "ordinal player" („nature”) operates on "substratum" and can apply an arbitrary admissible external action. The "cardinal player" ("designer") operates on the "superstratum" of the game and attempts to select the shape of the element . The control variables and are usually called strategies of the cardinal and ordinal player, respectively. The aim of the "designer" is to minimize the pay-off function , while "nature" tries to maximize it. Thus, the game is a zero-sum game because the sum of the payoffs to every player are the same for every single set of strategies. In these games, one player gains if and only if another player loses. Consequently, the interests of "designer" and "nature" are in conflict. This game is called antagonistic. It is important, that the design is characterized by a control set (a, q), which represent the actions of both players.
The pay-off function is the damage for the most unfavorable action of "nature". The "nature" tries to upsurge the pay-off function, varying the components of vector . Contrarily, the "designer" weakens the pay-off function through the initial setting of the vector . The set of players is . The pure strategy sets are .Obviously, there is a conflict of the interests, which leads to the game formulation in normal form:

It is evident that, within the framework of Euclidean geometry, the absolute value of a vector is equivalent to the square root of the sum of the squared components of that vector. The components of vector are restricted with with . The game formulation for a potential asteroid impact turn into the matrix quadratic game. In the continuous formulation, the integral damage in restricted quadratically with the integral constraint. The vector components will be restricted with the integral constraints 000 for . The game's formulation, which initially addressed the potential impact of asteroids, was subsequently converted into a unit square game problem. It is evident that the "lower level" game could be extended. Specifically, the opposing 'ordinal' player, otherwise referred to as the 'pilot Pirks' [18], has the option to participate on the 'lower' level against the 'nature'. It is possible to formulate the corresponding game as the differential game.

5. Discussion

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

problem with the structure of a game is a problem that arises from external circumstances that are beyond the control of the parties involved in the game on a unit square. The scalar criterion works like the payoff function, or damage. The first players, called the "operator" and "nature," try to make the payoff as small or as big as possible on a unit square. They have to follow the rules of limited
resources. The solution to the substratum game is expressed as a value of the game. The game's worth depends on how it's designed. The most important player, called the "designer," is in charge of the design rules. The goal of the game is to find the highest and lowest possible values. This is like a problem in design, where the best solution is to find the most optimal way to do things. If there are several important players with different goals, a superstratum game is created. This game is designed to cater to the superstratum players. The presentation will explain the best design problems for games that use a unit square. The suggested game plans can be used to design continuously controlled systems with uncertain loading in the best way.

Funding: This research received no external funding.
Institutional Review Board Statement: The study did not require ethical approval. Not applicable for studies not involving humans or animals.

Informed Consent Statement: Not applicable for studies not involving humans.
Data Availability Statement: The numerical results presented in this document can be replicated using the methodology and formulations described here. The analytical expressions used in the examples are available upon request to the authors.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

No acknowledgments.
Disclosure Statement: On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

The original version of this paper is available on https://www.scientific.net/EI.16.127.pdf

N.V. Banichuk, „On the game theory approach to problems of optimization of elastic bodies," Journal of Applied Mathematics and Mechanics, pp. Volume 37, Issue 6, p. 10421052, https://doi.org/10.1016/0021-8928(73)90069-5, 1973.

C. Thore, E. Holmberg und A. Klarbring, "A general framework for robust topology optimization under load-uncertainty including stress constraints.," Computer Methods in Applied Mechanics and Engineering, Volume 319, , p. 1, 2017.

C.-J. Thore, H. Alm Grundström und A. Klarbring, "Game formulations for structural optimization under uncertainty.," Int J Numer Methods Eng., p. 121: 165-185. , 2020.

F. Chernousko und A. Melikyan, "Some Differential Games with Incomplete Information," in Optimization Techniques IFIP Technical Conference. Lecture Notes in Computer Science. , Berlin, Heidelberg, Springer, 1975.

J. Szép und F. Forgó, "Games against nature," in Introduction to the Theory of Games. Mathematics and Its Applications, vol 17., Dordrecht. , Springer, 1985, pp. 230-236.

V. Kobelev, „On a game approach to optimal structural design," Struct Multidisc Optimization, , pp. 194-199, 1993.

I. Elishakoff und M. Ohsaki, Optimization and Anti-optimization of Structures Under Uncertainty, London, ISBN 9781848164772: Imperial College Press,, 2010.

J. Szép und F. Forgó, "The n-person game," in Introduction to the Theory of Games. Mathematics and Its Applications, vol 17. , Dordrecht. , Springer, 1985, pp. 18-21.

S. Tadelis, Game Theory. An Introduction., Princeton and Oxford: Princeton University Press, 2013.

S. Karlin, Mathematical methods and theory in games, programming and economics, AddisonWesley, 1959.

J. von Neumann, "Zur Theorie der Gesellschaftsspiele," Math. Ann., V. 100, , p. 295-320, 1928.

D. Werner, „Normierte Räume,“ in Funktionalanalysis. Springer-Lehrbuch., Berlin, Heidelberg, Springer Spektrum, , 2018.

J. von Neumann und O. Morgenstern, Theory of Games and Economic Behavior, Princeton: Princeton University Press. , 2004.

F. Zhang, Matrix Theory, Basic Results and Techniques, New York: Springer, , 2011.

R. Luce und H. Raiffa, Games and decisions. Introduction and critical survey, Hoboken: Wiley , 1957.

J. Szép und F. Forgó, „Games played over the unit square,“ in Introduction to the Theory of Games. Mathematics and Its Ap-plications, vol 17, Dordrech, thttps://doi.org/10.1007/978-94-009-5193-8_16, Springer, 1985, pp. 196-199.

European Commission, "Science and Technology for Near-Earth Object Impact Prevention, CORDIS - EU research results, European Union, Brussel, 2022.

S. Lem, Pilot Pirx, Frankfurt am Main: Suhrkamp-Verlag, 2003.

V. Kobelev, "On the Game-Based Approach to Optimal Design," Eng, 5(4), , pp. 3212-3238., 2024.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

Keywords Index

A

Aquaculture Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Automation Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

B

Binder Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Blue Power Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Braced Corrugated Shear Panel Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Briquette Quality Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Briquetting Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

C

Climate Monitoring METEORO: Low-Cost IoT Weather Station

D

Democratisation Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

E

Earthquake-resistant Structures Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Energy Dissipation Capacity Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Esp32 METEORO: Low-Cost IoT Weather Station

F

Fhd Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

Finite Element Modeling (fem) Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

G

Ggmplus Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

I

Iot METEORO: Low-Cost IoT Weather Station; Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications; IoT Security: Solutions to Mitigate Attacks

L

Lateral Load Resistance Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Lcoe Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Lignin Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Lowcost METEORO: Low-Cost IoT Weather Station

M

Meteorology METEORO: Low-Cost IoT Weather Station

Microcontrollers METEORO: Low-Cost IoT Weather Station

Mitigation IoT Security: Solutions to Mitigate Attacks

O

Optimal Design With Uncertainties; Continuous Game Theory; Games On Unit Square; Quadratic Games Unit Square; Game Formulation For A Potential Asteroid Impact Game-Theoretical Viewpoint to Optimal Design

P

Pressure Retarded Osmosis Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Process Simulation Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

S

Salinity Gradient Power Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Security IoT Security: Solutions to Mitigate Attacks

Seismic Performance Analysis Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Shear Strength Enhancement Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Simulation Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

Steel Structural Systems Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Structural Resilience Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Structural Stability Under Dynamic Loads Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

Sugarcane Bagasse Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Svd Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

System Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

T

Techno-economic Analysis Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

W

Water Quality Monitoring Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Water Quality Parameters Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Weather Station METEORO: Low-Cost IoT Weather Station

Website METEORO: Low-Cost IoT Weather Station

Authors Index

A

Abadi, M. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Aguilar, D. METEORO: Low-Cost IoT Weather Station

Albia, R. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Ali, H. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

B

Barrans, S. Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

C

Cabaraban, G. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Cabatingan, L. Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Calotes, L. Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

D

Dumalagan, J. Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

E

Edayadiyil, J. Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

F

Fadhlullah, N. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Flores, M. METEORO: Low-Cost IoT Weather Station

G

Gómez, O. METEORO: Low-Cost IoT Weather Station

H

Hudson, P. Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

I

Iligan, I. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Indriani, N. Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

Isla-Cabaraban, M. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

K

Khumaedi, K. Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

Kobelev, V. Game-Theoretical Viewpoint to Optimal Design

Kurian, J. Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

L

Laksono, Y. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

M

Maderal, C. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

Marroquín, J. METEORO: Low-Cost IoT Weather Station

Menjivar, A. IoT Security: Solutions to Mitigate Attacks

Merecido, J. Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Mufti, N. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Muladi, M. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

N

Navarro, J. Energetic and Economic Viability of Full-Scale Pressure Retarded Osmosis for Blue Power Generation in the Philippines

P

Philip, J. Seismic Resilience of Braced Corrugated Steel Shear Panels: Finite Element Investigation and Parametric Analysis

R

Ridla, M. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

S

Saparullah, S. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications

Scott-Russell, C. Develop a Tool for Democratising Knowledge within Small and Medium Enterprises in Simulations

Supriyadi, S. Identification of Kaligarang Semarang Fault Based on GGMplus Gravity Data Using First Horizontal Derivative (FHD) and Second Vertical Derivative (SVD) Analysis

T

Tabañag, I. Utilization of Lignin from Waste Degumming Liquor as Fuel Additive and Binder in Sugarcane Bagasse Briquettes

Y

Yuliana, E. Water Quality Monitoring Device: IoT-Based System for Aquaculture Applications