Engineering Innovations Volume 13

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Volume 13 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

Full text available online at https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

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Table of Contents

Chapter 1: Energy Engineering

Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

Jihad Rishmany, Chawki Lahoud, Rodrigue Imad, Michel Daaboul

Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

Hitesh Kumawat, Prerna Tundwal, Vikramaditya Dave

Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

Prerna Tundwal, Hitesh Kumawat, Vikramaditya Dave

Chapter 2: Sustainable Approaches in the Development of Urban Drainage Systems

Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility

Fu Ming Chang, Yi Pin Wu

The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

Fu Ming Chang

Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

Tri Khang Lam, Dinh Thang Nguyen, Van Anh Ngo, Hoai Phuong Duy Ninh, Trong Phuoc Huynh

Chapter 3: Water Treatment and Distribution

Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Marie Fe Y. Lacsado, Doris B. Montecastro

Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

Zara Elena Sharon Lah, Nurmin Bolong

Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Mustapha Salisu Muhammad, Mohd Hafiz Dzarfan Othman, Mohd Hafiz Puteh, Nik Ahmad Nizam Nik Malek, Abdul Razis Saidin, Abdulhalim Bin Mohd Yusof, Roziana Kamaludin, Siti Maryam, Samuel Ojo, Chia Ming Liew, A.P. Parvin, Nurul Huda

Chapter 4: Seismic Design of Buildings and Environmental Geology

Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

Adi Tonggiroh, Hendra Pachri, Muhardi Mustafa

Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Andrius Norman Lucana, Rick Milton Delgadillo, Joan Ramon Casas

Keywords Index

Authors Index

Chapter 1

Energy Engineering

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 3-12

doi: 10.4028/p-AO2DRr

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-04-11

Revised:
2024-10-10

Accepted:
2024-10-19

Online:
2025-02-06

Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

RISHMANY Jihad , LAHOUD Chawki , IMAD Rodrigue , DAABOUL Michel

¹University of Balamand, Faculty of Engineering, Mechanical Engineering Department, Tripoli, Lebanon

University of Balamand, Faculty of Engineering, Computer Engineering Department, Tripoli, Lebanon

ajihad.rishmany@balamand.edu.lb, chawki.lahoud@balamand.edu.lb, Rodrigue.imad@balamand.edu.lb, michel.daaboul@balamand.edu.lb

Keywords: Mobilized thermal energy storage (M-TES); Phase change materials (PCM); PCM phases; Economic analysis, Efficiency improvement.

Abstract

The mobilized thermal energy storage system (M-TES) has been investigated for decades, demonstrating its competitiveness compared to conventional heating systems like oil, gas, and biomass boilers. This paper presents a case study where waste heat from a power plant is utilized in M-TES to cover heating, cooling, and water heating needs in a university campus. Erythritol is used as the phase change material (PCM) and Therminol55 as the heat transfer fluid (HTF). The study simulates the charge, self-discharge, and discharge phases of the PCM, revealing that increased HTF flow reduces charging time and enhances efficiency, while increased waste heat potential decreases charging efficiency slightly. Economic evaluation shows that heat costs decrease with larger project scales and more PCM containers. This research highlights M-TES as a sustainable thermal energy storage solution with broad applications in the energy sector.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

In parallel with the exponential growth of the world's population, global energy consumption has dramatically increased. A recent study conducted by British Petroleum projects that the demand for oil will rise by approximately from 2007 to 2035 , while coal and natural gas consumption are expected to increase by more than [1]. This increasing energy demand places pressure on fossil fuel resources and amplifies concerns about greenhouse gas emissions. Within this global context, the building sector emerges as a principal contributor to energy consumption and carbon emissions, accounting for up to of total energy usage in some developed nations and emitting a parallel of total greenhouse gas emissions [ 2,3 ]. Approximately of the energy consumed by various sectors is dissipated as waste heat, remaining largely unused and wasted [4,5].
In response to this energy challenge and the consequential heat waste, the technology of Thermal Energy Storage (TES) has been implemented within a mobile concept known as M-TES. This system stores the waste heat generated by industries such as steel and cement mills, power plants, and sewage sludge incinerators [6-8]. The stored heat is retained within specially designed containers and then transported to end-users to cover space and water heating loads [9,10]. The M-TES is enhanced by the utilization of Phase Change Materials (PCMs), which have a high capacity to absorb or release thermal energy in the form of latent heat, differentiating them from materials used in conventional sensible heat storage systems [11-14].
The adoption of M-TES in providing heat has several benefits, such as a decrease in primary energy consumption, minimization of exergy losses, and a reduction of emissions by up to compared to conventional heating systems that use fossil fuels [15,16]. While M-TES technology has been investigated over the years, the focus of these studies has been on the selection of storage materials, container design, and economic studies. Prominent PCM options in M-TES projects
include organic sugar alcohols like Erythritol and Mannitol, as well as inorganic hydrated salts like Sodium acetate trihydrate and Magnesium chloride hexahydrate [17-25].
Various types of M-TES containers have been designed and tested to optimize performance in both charging and discharging processes, with different configurations such as shell-and-tube, encapsulated, direct-contact, detachable, and sorptive containers [26]. Economic evaluations of the M-TES system have shown that crucial factors governing cost include the transport distance from the waste heat source to the end-user and the heat demand, with findings highlighting that the cost of heating is proportional to the transport distance but inversely proportional to the heat demand [27]. Furthermore, sensitivity analyses show that the pricing of phase change materials affects the overall cost of heating [28].
While M-TES technology has found successful application in developed countries such as Germany, Japan, and Sweden [6,23,29], it holds considerable promise for third-world countries like Lebanon. These countries import oil to cover their energy needs; thus, the implementation of M-TES will help reduce oil consumption to cover heating and cooling demands as well as valorize waste heat, contributing to a sustainable and energy-efficient future. In this paper, the waste heat from a typical power plant is reused and stored using M-TES technology to cover the heating and cooling loads of a university campus. Our case study will investigate the modeling, simulation, and economic and environmental evaluation of the M-TES project. The findings will provide valuable results that can be compared with existing studies on an economic basis.

Case Study Description

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

In this study, we utilize waste heat from a Lebanese power plant (PP) to cover heating, cooling, and domestic hot water needs in a university campus located in Tripoli. The PP comprises three fired boilers. The largest Lebanese university campus, which consists of 32 buildings covering 500,000 , is the recipient of this waste heat. Previously, the campus used oil boilers for heating and chiller units for cooling. For the Mobile Thermal Energy Storage (M-TES) cycle, it involves heat exchange between the industrial waste heat (IWH) and a heat transfer fluid (HTF) as a first phase. The HTF, in turn, transfers heat to Phase Change Material (PCM) within a container until it is fully charged. The container is then transported to the campus, where the PCM releases heat to the circulating water for space heating, water heating, and absorption chillers for cooling. After full PCM heat discharge, the container returns to the PP to initiate a new cycle. The M-TES cycle is presented in figure 1.

Fig. 1. M-TES cycle [49]

Fig. 1. M-TES cycle [49]

Over the course of a year, PP's exhaust gas temperatures range from to , with an average of , and the PP remains off for around 45 hours annually. The IWH's annual potential and flow characteristics are detailed in Table 1.

Table 1. IWH potential, flow and annual energy wasted. [30]

IWH | Minimum | Maximum | Mean value | Annual energy (MWh)
Potential (MW) | 0.798 | 21.35 | 10.385 | 90,778.75
Flow (kg/s) | 19.11 | 158.54 | 100.72 |

Simultaneously, the university campus load (MW) varies annually. The energy requirements for the considered reference year are summarized in Table 2. Specific operating temperature criteria are adhered to, where the radiator water should exceed for winter heating [31], and chiller water should surpass for summer cooling [32].

Table 2. University annual energy needs.

| Water heating | Space heating | Space cooling | Total
Energy consumed/year (MWh) | 1,273 | 10,833 | 11,794 | 23,900

To achieve effective heat transfer from the industrial waste heat to the PCM and subsequently release it at the university to cover the energy loads, we employ Erythritol as the PCM due to its melting point temperature ( ) falling within the required temperature range, in addition to its high latent heat of and favorable cost-effectiveness [33-37]. The selected HTF is Therminol55, characterized by a high flash point of and a boiling temperature of [38,39]. Key physical properties of Erythritol and Therminol55 are summarized in Table 3.

Table 3. Thermo-physical properties of selected PCM and HTF at different operating temperatures. [33-35,38,39].

Item
T
(°C)
Density
(kg/m³)
Cp
(kJ/kg.
K)
Thermal
Conductivity
(W/m. K)
Latent
Heat
(kJ/kg)
Melting
point
(°C)
Flash
point
(°C)
Boiling
Temperature
(°C)
Viscosity
(kg/m. s)
Dynamic
viscosity
(MPa.s)

Erythritol
(PCM)
20
1480
1.35
0.732
339
118
-
-
0.02895
-

140
1300
2.74
0.326

-
-
0.01602
-

Therminol55
(HTF)
33
865
1.94
0.1273
-
-
177
351
-
25.2

130
797
2.3
0.1156
-
-

-
1.71

Modelling and Simulation

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

IWH is valorized to cover the university energy requirements. Between the waste heat and the load, there is a series of heat exchangers that must be used to transfer the heat from the PP to the university:

Exhaust Heat Recovery Exchanger: The energy of the exhaust gas is transferred to the HTF, where three counter-flow heat exchangers are used with exhaust gases for a mean power of 10.38 MW .

Thermal Storage Exchanger: The energy of the HTF is transferred to the PCM, designed as a 20 -foot ISO shell-and-tube container with encapsulated PCM inside the tubes and HTF flowing in the shell side. This design improves the heat exchange due to a larger exchange area. Staggered aluminum tubes are chosen due to their high conductivity, light weight, and low cost [40,41].

Heat Distribution Exchangers: The energy of the PCM is transferred to circulating water in the campus. Two counter-flow tubular heat exchangers are selected, one for heating and one for cooling.
Once the heat exchangers are designed, simulations are conducted for three phases: charging, selfdischarging, and discharging of the PCM, carried out on Simulink-MATLAB. The simulation tracked the evolution of PCM temperature, oil tank temperature, and oil flow. First, the PCM temperature increases exponentially as it stores sensible heat, gradually reaching its melting temperature. After this, the PCM starts storing latent heat until reaching the liquid state and saturation phase. Following this saturation phase, the PCM resumes absorbing sensible heat until it reaches a temperature of
, marking the completion of the charging process. Oil flow increases with the increase of oil tank temperature to recuperate heat through the Exhaust Heat Recovery Exchanger until reaching the maximal flow setpoint, and the oil tank temperature increases with the increase of oil temperature exiting the container due to the increase of PCM temperature.
In our study, we investigated the impact of oil flow and IWH potential on charging time and efficiency through simulations. By increasing the max oil flow setpoint and varying the IWH potential, we observed changes in these parameters. The results, summarized in Table 4, show that doubling the max oil flow setpoint led to a reduction in charging time and an increase in charging efficiency. Furthermore, adjustments in the IWH potential showed varying effects: maximizing it resulted in a slight decrease in charging time but a substantial drop in efficiency from to . Conversely, minimizing the IWH potential increased efficiency by but at the expense of a increase in charging time. These findings show the importance of oil flow regulation, particularly when adjusting IWH potential, for managing charging efficiency effectively. Therefore, the simulation was conducted by setting the mean potential power at 10.385 MW and a maximum oil flow of , with the initial PCM temperature set at .
During normal system operation, after discharge during the previous cycle, PCM temperature settles between and , depending on the load requirements. When starting a new cycle, the charging phase begins from these temperatures. This initial temperature influences the subsequent charging phase, impacting charging time, efficiency, and container capacity. All results are summarized in Table 5.
After the charging phase, PCM capacity self-discharges and its capacity decreases due to heat conduction through the storage tank and container shells, and by convection with surrounding air. During transportation, forced convection accelerates this process, while during waiting periods before the discharge phase, natural convection takes over.

Table 4. Charge phase simulation results for different oil flows and different IWH potentials.

IWH | Maximum oil flow (kg/s) | IWH energy (MWh) | Recovered energy by oil (MWh) | Recovery efficiency (%) | Storage efficiency (%) | Charging Efficiency (%) | Charging time (s) | Saturation time (s)
10.38 | 100 | 35.11 | 7.942 | 22.58 | 88.79 | 20.05 | 12,193 | 5,582
10.38 | 201.4 | 24.9 | 7.95 | 31.79 | 88.78 | 28.22 | 8,664 | 3,122
21.354 | 100 | 71.097 | 7.945 | 11.18 | 88.8 | 9.92 | 11,985 | 5,378
0.798 | 100 | 8.45 | 7.94 | 94.04 | 88.8 | 83.5 | 38,110 | 27,050

Table 5. Charge simulation results for a mean IWH potential, a maximum oil flow of and different PCM initial temperatures.

PCM initial Temperature (°C) | Maximum oil flow (kg/s) | IWH energy (MWh) | Recovered energy by oil (MWh) | Recovery efficiency (%) | Storage efficiency (%) | Charging efficiency (%) | Charging time (s) | Saturation time (s)
80 | 100 | 34.296 | 7.402 | 21.58 | 85.8 | 18.52 | 11,889 | 5,371
95 | 100 | 34.057 | 7.252 | 21.29 | 84.89 | 18.08 | 11,806 | 5,303

The container shell is constructed from steel [42], while we choose fiber-reinforced plastic (FRP) for the storage tank shell. To insulate the storage tank, it was surrounded by 4 cm of rockwool. Physical properties of these materials are summarized in table 6 .
The simulation results illustrated in fig.2, demonstrate a nearly linear decline in PCM temperature from to over around 2.5 hours. This simulation was repeated for different rockwool thicknesses and with a duration of 1.5 and 2.5 hours, as summarized in table 7 . The results show that 4 cm of rockwool is a good choice since it improves significantly the insulation as the energy losses decreases from 0.017 Mwh to 0.0109 Mwh while by using 5 cm thickness it decreases to 0.009 MWh and PCM final temperatures are very close. Additionally, it is shown that each one-hour delay causes the loss of of PCM capacity as shown in table 7 ( loss for 1.5 hours of self-discharge compared to loss for 2.5 hours of self-discharge for a rockwool thickness of 40 mm ).

Table 6. Physical properties of carbon steel, FRP and rockwool. [43,44]

Material | Thermal conductivity (W/m. K) | Thickness (mm) | Density (kg/m³)
Carbon Steel | 45 | 4 | 7500
Fiber Reinforced Plastic | 0.57 | 4 | 1550
Rockwool | 0.043 | 40 | 60

Table 7. Self-discharge simulation results for different rockwool thicknesses.

Self-Discharge duration = 1.5 hours | Self-Discharge duration = 2.5 hours
Rockwool thickness (mm) | Energy lost (MWh) | Percentage | PCM final temperature (°C) | Energy lost (MWh) | Percentage | PCM final temperature (°C)
25 | 0.017 | 0.23% | 129.64 | 0.026 | 0.37% | 129.44
40 | 0.0109 | 0.15% | 129.76 | 0.018 | 0.26% | 129.59
50 | 0.009 | 0.13% | 129.79 | 0.015 | 0.21% | 129.67

After 2.5 hours of PCM self-discharge, the discharge phase begins from a PCM Temperature of . For each load, PCM discharge power is calculated resulting in a power of 7 MW . The simulation in fig. 2 shows that the PCM temperature decreases until it reaches . At this point, the PCM undergoes a phase change, reaching a solid state after 2392 seconds. Following solidification, the PCM releases sensible heat until it reaches , marking the end of the discharge phase. With an efficiency of .

Fig. 2. PCM cycle: charge, self-discharge, and discharge phases.

Fig. 2. PCM cycle: charge, self-discharge, and discharge phases.

To ensure a continuous operation strategy, we require 9 containers for winter (with 3 containers discharging simultaneously) and 12 containers for summer (with 4 containers discharging simultaneously) to cover the university daily loads requirements. The total number of cycles per year is calculated by dividing the total discharged energy by the energy discharged per container, resulting in 5,196 cycles:
Additionally, by considering an additional useful energy of year for space heating/cooling and water heating using fuel oil potential in the Power Plant, the M-TES system improved the Power Plant efficiency by , as shown in Table 8.

Table 8. PP efficiency calculation with and without M-TES.

PP | Fuel oil potential (MWh) | Useful energy/ year | PP efficiency
Without M-TES | 2,443,239.15 [28] | 738,130 [28] | 30.21%
With M-TES |  | 738,130+23,900 | 31.19%

Economic Analysis

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

In most studies involving M-TES, the system typically focuses on covering space and water heating needs. To align our results with other research, two economic evaluations are conducted for different scenarios:
Case 1: M-TES solely provides space and water heating (requiring 9 containers).
Case 2: M-TES serving both space heating/cooling and water heating (requiring 12 containers). For the economic assessment, the project cost is first calculated, then the operation cost, and savings. Table 9 outlines the project cost for both cases. Currently, the system relies on an oil boiler for space and water heating, along with electricity for the space cooling system. Implementing M-TES will lead to savings in oil and electricity consumption, as detailed in Table 10.
Operation costs vary based on the total number of cycles per year. In Case 1, there are 2,638 cycles annually, while Case 2 involves 5,196 cycles. The cost per cycle is estimated at 57.5 USD , based on feedback from a local transport company and the distance separating the PP from the university. These operation costs are summarized in Table 11.

Table 9. Initial cost breakdown for cases 1 and 2.

Description | Unit price [50] | Quantity Case 1 | Quantity Case 2 | Cost of case 1 (USD) | Cost of case 2 (USD)
Container | 2,500 USD/container | 9 | 12 | 22,500 | 30,000
Erythritol | 3.5 USD/kg | 9 *30,048 kg | 12*30,048 kg | 946,512 | 1,262,016
Aluminum tubes | 2 USD/kg | 9*1,080*3.2m*2.5kg/m | 12*1,080*3.2m*2.5kg/m | 155,520 | 207,360
Therminol 55 | 3 USD/kg | 20,000 kg | 20,000 kg | 60,000 | 60,000
PRF | 1,510 USD/ton | 9*137.7 kg | 12*137.7 kg | 1,865.907 | 2,487.89
Rockwool | 1 USD/m² | 9*67.23 m² | 12*67.23 m² | 605.07 | 806.76
HX & Pumps | 1.33 USD/kWh | 14,100 kWh | 14,100 kWh | 18,753 | 18,753
Sum |  |  |  | 1.206 million | 1.58 million
Shipping |  |  |  | 15% | 15%
VAT |  |  |  | 11% | 11%
Total cost |  |  |  | 1.52 million USD | 1.99 million USD

Table 10. Annual savings for cases 1 and 2.

Description | Useful Energy (MWh/year) | System efficiency or COP | Energy saved (MWh/year) | Unit price (USD/kWh) | Total savings (USD/year)
Water heating | 1,273 | 0.8 | 1,591.25 | 0.05 | 79,562
Space heating | 10,833 | 0.8 | 13,541.25 | 0.05 | 677,062
Space cooling | 11,794 | 3.8 | 3,104 | 0.18 | 558,720
Total (Water and Space heating) | 12,106 |  | 15,132.5 |  | 756,624
Total (Water and Space heating/cooling) | 23,900 |  | 18,502.5 |  | 1,315,344

Table 11. Operation cost for cases 1 and 2.

Case | Number of PCM cycles/years | Unit price (USD/cycle) | Operation cost (USD/year)
Space & water heating only | 2,638 | 57.5 | 151,685
Space heating, cooling and water heating | 5,196 | 57.5 | 298,770

The economic study was conducted by assuming that the lifespan of the container and PCM are 15 years and the cost of PCM is to compare results with Li et al. [25]. In this paper, the campus area is approximately and the distance between the waste heat source and the campus is around 23 km . The results of the economic study for both cases are summarized in table 12.

Previous studies such as Li et al. [27] have shown that as the area being served increases from 500 to , the cost of heating decreases significantly, from to 0.04 USD/kWh, for the same distance of 23 km . Moreover, the payback period for replacing an oil system with M-TES reduces from 10.5 years when only two containers are used to meet the heat demand to approximately 2 years in our case.

Table 12. Economic evaluation results for cases 1 and 2.

Case | Payback period | Cost of heating/cooling (USD/kWh)
Space & water heating only | 2 y 8m | 0.018
Space heating, cooling and water heating | 1y 10m | 0.009

In our study, despite dealing with a larger area and higher initial costs due to more containers being employed, we achieved promising results. The COH for Case 1 dropped to 1.8 cents , which is encouraging for large-scale projects. Furthermore, when M-TES is utilized to fulfill both heating and cooling needs, the cost further reduces to 0.9 cent due to the increased useful energy covered. Additionally, the payback period decreased by 10 months when M-TES is applied to both heating and cooling demands. These findings align closely with those reported by Guo et al. [24]. A comparison of our results with COH of different heating systems is summarized in Table 13.

Table 13. Economic results comparison with different heating systems.

Energy resources | M-TES case 1 | M-TES case 2 | Electricity | Pellet | Bio-oil | Biogas | Oil
Energy price (USD/kWh) | 0.018 | 0.009 | 0.1-0.12 [45] | 0.013-0.04 [46] | 0.04-0.07 [47] | 0.07-0.1[48] | 0.09-0.12 [45,48]

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

The M-TES system studied and simulated on large scale project and with high-capacity containers under the Mediterranean climate conditions and heat/cooling loads was evaluated, and the several key conclusions can be emerged:

The M-TES system increased the efficiency of PP by by using only of the PP's waste heat.

Energy costs were reduced significantly, by compared to previous studies conducted to cover smaller areas energy needs. The cost also decreased when using M-TES for both space heating and cooling. This leads to the conclusion that increasing the number of cycles decreases the energy cost.

From an economical perspective, the M-TES system has a PBP of 2 years and 8 months if only used for heating and 1 year and 10 months if used for both space heating and cooling.

As shown in Table 13, the Cost of Heating (COH) using M-TES for large-scale projects is the lowest compared to other heating systems, making it a highly cost-effective solution.

The outcomes of this research align perfectly with SDG 7 (Affordable and Clean Energy), & SDG 11 (Sustainable Cities and Communities).
The primary challenge associated with implementing M-TES on a large scale will be ensuring a continuous energy supply. Issues such as traffic delays, unscheduled shutdowns of Power Plants, and
the maintenance needs of mechanical systems could disrupt the process of charging and transporting the containers. The impact of these challenges, along with others, needs more studies. By conducting such a study, we can optimize the weight of the containers, leading to more efficient operation. This optimization process also extends to improving the operation strategy and determining the ideal number of containers needed for optimal functionality. Furthermore, a Computational Fluid Dynamics (CFD) study can represent the behavior of the phase change state of the PCM, allowing for comparison with other configurations utilizing different Heat HTF and PCM indirect Heat Exchangers.
While there remain numerous challenges for further research and study on the M-TES system, our results demonstrate its promising potential as a technology capable of replacing conventional heating systems.

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The original version of this paper is available on https://www.scientific.net/EI.13.3.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 13-23

doi: 10.4028/p-DHpNP1

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-02-27

Accepted:
2024-03-07

Online:
2025-02-06

Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

Hitesh Kumawat , Prerna Tundwal , Vikramaditya Dave

Scholar, Electrical Engineering Department, College of Technology and Engineering MPUAT, Udaipur, 313001, India

Associate Professor, Electrical Engineering Department, College of Technology and Engineering MPUAT, Udaipur, 313001, India

a hiteshkumawat64@gmail.com, sainiprera.97@gmail.com, vdaditya1000@gmail.com

Keywords: PV, Power Quality, WECS, standalone hybrid system, Battery, ANN.

Abstract

The ability of hybrid renewable energy systems (HRES) to combine the advantages of several renewable energy sources has attracted a lot of attention. The intermittent nature of renewable energy sources, such wind and solar power, can make it difficult to keep the grid's power quality constant. Advanced intelligent control strategies are presented in this work with the goal of improving power quality in HRES. In order to reduce power quality difficulties, the research suggests a multidimensional approach that combines the capabilities of advanced control algorithms, intelligent decision-making, and predictive analytics. The main goal is to solve typical issues in HRES, such as harmonic distortions, voltage fluctuations and frequency variations. Proactive system management is made possible by the control approach, which forecasts renewable energy generation trends using machine learning techniques. In addition, real-time monitoring and control systems are included to enable prompt responses to modifications in the power generating mix. The HRES guarantees smooth integration and interaction by utilizing sophisticated hardware and software components to achieve these control mechanisms. This paper presents the results of an extensive simulation research that shows how well the suggested intelligent control solutions mitigate problems with power quality. The results show that the grid's frequency regulation, harmonic distortions, and voltage stability have all significantly improved.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

The integration of various renewable resources has resulted from the progression toward sustainable energy sources, making hybrid renewable energy systems (HRES) a key component of modern power generation. However, the unstable and irregular nature of renewable energy sources makes it difficult to consistently provide high-quality power. To overcome this challenge, advanced control schemes that preserve optimal power quality while quickly adjusting to HRES changes must be developed [1]. For renewable energy systems to be widely adopted, the provision of consistent and dependable power quality is essential. Important aspects of power quality that directly affect the effectiveness and dependability of energy transmission are voltage stability, frequency regulation, and harmonics elimination. But because renewable energy is dynamic, traditional control strategies frequently cannot keep up, which leads to poor power quality and unstable grids [2]. Using cutting edge technologies like artificial intelligence, machine learning, and sophisticated control algorithms, this research aims to investigate and suggest enhanced intelligent control solutions tailored for HRES. By managing energy flow dynamically, reducing disruptions, and enabling the smooth integration of various renewable sources, these strategies aim to improve power quality within HRES [3]. The use of intelligent control techniques aims to address technical challenges while simultaneously meeting the growing need for flexible and durable energy systems. This research aims to improve the performance of HRES by utilizing intelligent control, providing the way for improved sustainability, efficiency, and dependability in the use of renewable energy sources [4]. The application of various intelligent control strategies to improve power quality inside HRES will be thoroughly examined in this study. By means of simulations and practical case studies, our
objective is to demonstrate the effectiveness and potential of these approaches in ensuring stable and superior power distribution, accelerating the shift towards a more sustainable and reliable energy environment [5].

2. Description of Proposed Hybrid System

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

The new control method for features that improve power quality is created with the proposed standalone hybrid microgrid system. If the battery's charging current is excessive, the supercapacitor will enter the charging mode. In addition, the battery will be discharged initially. If the battery's discharging current is excessive, the supercapacitor will enter the discharging mode in order to meet the load's power requirement. The three phase fully controlled inverter bridge's gate pulse is produced by an FLC-based controller. The AC and DC busses are connected via the fuzzy-based regulated inverter. To produce three phase AC voltage of the necessary amplitude, frequency, and low harmonic content, the LC filter circuit is used. Fig. 1 displays the block diagram of the newly designed novel control technique for a standalone hybrid wind solar PV microgrid system [6].
To keep the load voltage constant, a dynamic voltage restorer (DVR) is used at the load side. The improvement of microgrid power quality is the main objective of the suggested control approach. The voltage source inverter with FLC based capability can effectively minimize harmonics and prevent them from entering the load/grid side [7].

Fig. 1 Block Diagram of the Newly Designed Novel Control Technique for a Standalone Hybrid Wind Solar PV Microgrid System [8]

Fig. 1. Block Diagram of the Newly Designed Novel Control Technique for a Standalone Hybrid Wind Solar PV Microgrid System [8]

In order to reduce voltage sag/swell, imbalance, and enhance system power quality, the DVR is linked at the PCC. With an injecting transformer, a voltage source inverter provides the appropriate voltage injection to make up for the difference between supply and load side feeders F1 and F2, respectively. The undesirable harmonic components are suppressed using the LC filter circuit. The connected load receives three-phase electricity from the AC bus.

3. Control of System Components

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

Fig. 2 displays the block diagram for the suggested ANN-based control technique for hybrid storage management.

Fig. 2 Block Diagram for the Suggested ANN-Based Control Technique [9]

Fig. 2. Block Diagram for the Suggested ANN-Based Control Technique [9]

The following control schemes are suggested for the microgrid in order to efficiently manage the hybrid energy storage system.
(i) To increase the suggested microgrid's efficiency, an MPPT control method is created to capture the most electricity possible from the hybrid solar and wind system [10].
(ii) The excess power is stored in HESS in the following ways when the hybrid system's power generation exceeds the load requirement. When the battery's charging current is low, bi-directional converter 1 uses the excess power produced by RESs to charge the battery. Part of the excess power produced will charge the supercapacitor via directional converter 2 when the charging current exceeds the predetermined threshold value. Both of the converters will function in buck mode under these circumstances .
(iii) The HESS supplies load power when the hybrid system's power generation is less than the demand for the load. The battery supplies the shortfall in power in the following ways. When the battery's discharge current is low, the battery uses the bi-directional converter 1 to transfer the power difference to the DC bus in order to meet the load's high power requirement. Part of the shortfall power will be supplied by the supercapacitor to the DC bus via bi-directional converter 2 when the battery's discharge current exceeds the predetermined threshold value. Both of the converters will run in boost mode under these circumstances [10,11].

4. Simulation Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

The setup shows how different components are displayed in the MATLAB/Simulink program, together with Simulink blocks and the necessary interface circuits for improved hybrid microgrid power generation.
To provide electricity to an off-grid community, a hybrid PV-wind system coupled with a hybrid energy storage system (HESS) is the suggested microgrid. MG consists of the following: a bidirectional (DC-DC) converter connects the PV system; an ANN-based controller controls the HESS system; and WECS is connected via a boost converter using the optimal torque (OT) based MPPT technique. These converters are connected to the 640 V common dc link. The AC loads are powered by the fuzzy based VSC regulated inverter. In order to reduce imbalance, improve system power quality, and minimize voltage sag/swell, the DVR is linked at the PCC. Through the use of an injecting transformer and a voltage source inverter, the difference between supply and load side feeders F1 and F2, respectively, is compensated for. The circuit of the LC filter is used to suppress the undesirable harmonic components. The battery gets charged initially when there is enough wind and solar power to create more energy than the load requires. In the event that the battery's discharging current is excessive, the supercapacitor will enter the discharging mode in order to meet
the load's power requirement. In the event that the battery's charging current is excessive, the supercapacitor will enter the charging mode in addition to being initially drained [12].

Fig. 3 MATLAB/Simulink Implementation of Dynamic Model for Hybrid Solar PV Wind Microgrid [12]

Fig. 3. MATLAB/Simulink Implementation of Dynamic Model for Hybrid Solar PV Wind Microgrid [12]

This system displays many Simulink experiments created aimed at the potential uses of a model of an effective control strategy for hybrid generation systems in stand-alone power generation mode. Studies comparing ANN-based DVRs to traditional dynamic voltage restorers are presented. Where in the dynamic performance of an artificial neural network (ANN)-based DVR is examined under several fault scenarios, such as symmetrical, asymmetrical, and sag and swell situations [13].

Fig. 4 V-I and P-V Characteristics of Single Module at and Irradiances

Fig. 4. V-I and P-V Characteristics of Single Module at and Irradiances

Figure 4 above shows the I-V and P-V characteristics for a single PV module at various irradiation levels. At a module temperature of 298 K ( ), the characteristics are provided for various irradiance levels, namely at , and .

Fig. 5 V-I and P-V Characteristics of PV Array at 1000, 800, 600 and Irradiances ( ) [13]

Fig. 5. V-I and P-V Characteristics of PV Array at 1000, 800, 600 and Irradiances ( ) [13]

The I-V and P-V characteristics of a PV array system with 65 strings connected in parallel and 14 modules connected in series are displayed in Fig. 5. The PV array's peak output power at 1000 and is 62.9 kW .
At a temperature of , characteristics are presented for various irradiance levels of , and .

Case 1. Simulation Response During Asymmetrical Fault at Constant Load With Varying Wind Speed and Varying Irradiance simulation results for Line-to-line fault-

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

The stair-case profile of irradiance is utilized over the whole simulation period, as fig. 6 illustrates. The irradiance varies over time from to , and , respectively, over the intervals to to to , and to . Throughout the simulation, the load and temperature remained constant [14,15].

Fig. 6 Waveform of Irradiance and Temp Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 6. Waveform of Irradiance and Temp Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 7 Waveform of Photovoltaic Voltage and Photovoltaic Current Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 7. Waveform of Photovoltaic Voltage and Photovoltaic Current Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

As shown in fig 7, PV array current varies from 28A to 42A to 56A to 70A during time durations of to to to , and to , respectively. PV array voltages vary from 404.6 V to 606.8 V to 809 V to 1011 V [14].
As shown in fig. 8, the supplied wind speed fluctuates during the simulation. The PMSG rotor speed fluctuates in accordance with the variations in the input wind speed, which ranges from 12 to to to . Specifically, it changes from 1930 rpm to 1120 rpm to 1311 rpm to 1517 rpm throughout the course of to to to , and to , respectively [14,15].

Fig. 8 Waveform of Input Wind Speed and Rotor Speed Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 8. Waveform of Input Wind Speed and Rotor Speed Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

As shown in fig 9, The PMSG Electromagnetic & Mechanical Torque changes from to to to within time durations of: to to , to , and to , respectively. The PMSG output power varies from 40800 W to 16380 W to 20520 W to 26670 W [14].

Fig. 9 Waveform of PMSG Output Power and PMSG Te and Tm Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 9. Waveform of PMSG Output Power and PMSG Te and Tm Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 10 Waveform of PMSG ph-to-ph Voltage Vph Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 10. Waveform of PMSG ph-to-ph Voltage Vph Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 11 Waveform of PMSG ph Current Iph Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 11. Waveform of PMSG ph Current Iph Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

As shown in fig. 10 & fig. 11 PMSG phase current and phase-to-phase voltage fluctuate in response to changes in the input wind speed. Additionally, we can see that there are certain distortions in the PMSG phase voltage and phase current waveforms when a line-to-line fault occurs in the system between time and seconds [14].
In Fig. 12 the output power of the PMSG (in KW) is displayed compared to changing wind speeds.

Fig. 12 Waveform of PMSG Output Power and Wind Speed Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 12. Waveform of PMSG Output Power and Wind Speed Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

According to fig. 13 Phase-to-phase voltage on the generator side remains constant until time seconds, at which point a line-to-line fault causes a slight fall in voltage. The system subsequently stabilizes at seconds, at which point the voltage returns to its initial constant level [14].

Fig. 13 Waveform of ph-to-ph Voltages of Generation Side Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 13. Waveform of ph-to-ph Voltages of Generation Side Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

According to fig. 14 Phase-to-phase current on the generating side remains constant until seconds, at which point it increases as a result of a line-to-line fault. The system then stabilizes at seconds, at which point the current returns to its initial value [14].

Fig. 14 Waveform of ph Current of Generation Side Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 14. Waveform of ph Current of Generation Side Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Because the network's ANN-tuned DVR compensates for distortions in the generation side voltage to load side voltage, the phase-to-phase voltage of the load remains constant throughout the simulation period, even when the system experiences a line-to-line fault (fig. 15). This is also visible in the zoomed-in window for the phase voltage of the load in fig. 16 [14].

Fig. 15 Waveform of ph-to-ph Voltage of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 15. Waveform of ph-to-ph Voltage of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 16 Waveform of ph-to-ph Voltage of Load L-to-L Fault Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 16. Waveform of ph-to-ph Voltage of Load L-to-L Fault Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Figure 17 shows that the load's phase current remains constant at 92 A . However, there is a minor distortion from time to , which is clearly visible in the zoomed-in window shown in Figure 18 [14].

Fig. 17 Waveform of ph Current of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 17. Waveform of ph Current of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 18 Waveform of ph Current of Load L-to-L Fault Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 18. Waveform of ph Current of Load L-to-L Fault Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

As shown in fig. 19 throughout the simulation, the load's RMS voltage and RMS current stay constant and only slightly alter during a line-to-line fault [15].

Fig. 19 Waveform of ph-to-ph RMS Voltage of Load and RMS ph Current of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 19. Waveform of ph-to-ph RMS Voltage of Load and RMS ph Current of Load Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 20 Waveform of ph-to-ph Voltage of Generation Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 20. Waveform of ph-to-ph Voltage of Generation Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 20 Waveform of ph-to-ph Voltage of Generation Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 21 Waveform of ph-to-ph Voltage of Load Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Figure 20 clearly illustrates the distortions caused by a line-to-line fault, which resulted in a decrease in the phase-to-phase voltage of the generation side. However, Figure 21 illustrates how an asymmetrical fault does not affect the phase-to-phase voltage on the load side because the system uses an ANN-tuned DVR to isolate the load when a line-to-line fault occurs [14].

Fig. 22 Waveform of Injected ph-to-ph Voltage in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 22. Waveform of Injected ph-to-ph Voltage in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Figure 22 makes it evident that, in the event of a malfunction, an ANN-tuned DVR will inject phase voltages into the load while isolating it from the generator side, ensuring a continuous and distortion-free voltage supply to the load [14].
Figures 23 and 24 display the phase current of the load side and the phase current of the generator side for each unit system [14].

Fig. 23 Waveform of ph Current of Generation Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 23. Waveform of ph Current of Generation Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 24 Waveform of ph Current of Load Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

Fig. 24. Waveform of ph Current of Load Side in PU Where Constant Load, Variable Irradiance, Variable Wind Speed [14,15,16]

5. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

In conclusion, a critical approach to resolving power quality issues in hybrid renewable energy systems is the incorporation of sophisticated intelligent control mechanisms. This study highlights a notable improvement in power quality indicators, including harmonic mitigation, frequency stability, and voltage regulation, by combining renewable energy sources, such as solar and wind power, with creative control algorithms. Intelligent controls' adaptable nature allows for dynamic response to variations, guaranteeing maximum performance in a range of scenarios. In addition to paving the way for a dependable and stable power supply, these solutions' effective execution and proven effectiveness also support the more general goals of sustainability and effective use of renewable resources. Further advancements and real-world implementations of these strategies hold immense promise in revolutionizing the future of energy systems, fostering a sustainable, resilient, and high-quality power infrastructure.

References

The original version of this paper is available on https://www.scientific.net/EI.13.13.pdf

A. Alexander, M. Thathan, Modelling and analysis of modular multilevel converter for solar photovoltaic applications to improve power quality. IET renewable power Generation. 2015 Jan;9(1):78-88.

S. Arezki, M. Boudour, Improvement of power quality for hybrid PV-FC power supply system. In2014 16th International Power Electronics and Motion Control Conference and Exposition 2014 Sep 21 (pp. 725-730). IEEE.

PG. Arul, VK. Ramachandaramurthy, RK. Rajkumar, Control strategies for a hybrid renewable energy system: A review. Renewable and sustainable energy reviews. 2015 Feb 1;42:597-608.

A. Chauhan, RP. Saini, A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control. Renewable and Sustainable Energy Reviews. 2014 Oct 1;38:99-120.

F. Chishti, S. Murshid, B. Singh, LMMN-based adaptive control for power quality improvement of grid intertie wind-PV system. IEEE Transactions on Industrial Informatics. 2019 Feb 3;15(9):4900-12.

W. Jing, C. Hung Lai, SH. Wong, ML. Wong, Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview. IET Renewable Power Generation. 2017 Mar;11(4):461-9.

K. Zeb, SU. Islam, I. Khan, W. Uddin, M. Ishfaq, TD. Busarello, SM. Muyeen, I. Ahmad, HJ. Kim, Faults and Fault Ride Through strategies for grid-connected photovoltaic system: A comprehensive review. Renewable and Sustainable Energy Reviews. 2022 Apr 1;158:112125.

R. Sedaghati, MR. Shakarami, A novel control strategy and power management of hybrid PV/FC/SC/battery renewable power system-based grid-connected microgrid. Sustainable Cities and Society. 2019 Jan 1;44:830-43.

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H. Kumawat, R. Jangid, Using AI Techniques to Improve the Power Quality of Standalone Hybrid Renewable Energy Systems. InCrafting a Sustainable Future Through Education and Sustainable Development 2023 (pp. 219-250). IGI Global.

H. Kumawat, P. Tundwal, V. Dave, Intelligent Control Strategies for Enhancing Power Quality in Hybrid Renewable Energy System for Agricultural Applications. In2023 IEEE World Conference on Applied Intelligence and Computing (AIC) 2023 Jul 29 (pp. 26-32). IEEE.

H. Kumawat, P. Tundwal and V. Dave, "Artificial Intelligence-Enabled Fault Detection and Diagnosis for Improved Power Quality in Hybrid Renewable Energy Systems," 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE), GHAZIABAD, India, 2023, pp. 480-485, IEEE.

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-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 25-34

doi: 10.4028/p-rt3dFI

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-02-27

Accepted:
2024-03-07

Online:
2025-02-06

Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Prerna Tundwal , Hitesh Kumawat , Vikramaditya Dave

Scholar at Electrical Engineering Department, College of Technology and Engineering, Udaipur, India

PhD Scholar at Electrical Engineering Department, College of Technology and Engineering, Udaipur, India

Associate Professor at Electrical Engineering Department, College of Technology and Engineering, Udaipur, India

sainiprerna.97@gmail.com, hiteshkumawat64@gmail.com, vdaditya1000@gmail.com

Keywords: 100 kWp , design optimisation, educational institute, Grid- interfaced rooftop solar plant, operational effectiveness, technology selection.

Abstract

Grid-interfaced rooftop solar plant deployment marks a big step towards the production of sustainable and renewable energy. This study examines a number of grid-interfaced rooftop solar plant installation issues, with a particular emphasis on design optimisation, technology selection, and operational effectiveness. The numerous grid-connected solar photovoltaic (PV) system components are the main emphasis of this study, along with their operation, installation plan, and design considerations. There is a case study on a 100 kWp grid-connected solar photovoltaic (PV) system in the Department of Electrical Engineering in the College of Technology and Engineering, MPUAT, Udaipur.. The actual output of installed system has been recorded and emission of is also avoided. The comparison is done between the actual output and simulation based model. The solar system is very beneficial in educational institute to avoid bills and also helpful in research area.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

The promotion of renewable energy resources is helpful due to change in climate, global warning and sudden increase in pollution. Government of India also promote the renewable energy resources by various scheme such as, on the purchase of solar module government will give the subsidy[19]. On the government institute, government office and other government building if solar system installed then fund will be allotted by Ministry of New and Renewable Energy[2]. The project of solar power plant are constructed on larger scale in India. Before installation we can verify the efficiency of the installed component because that is costly. Currently the world is facing pollution problem by using conventional power plant such as fossil fuel in the other hand solar energy almost free of cost and clear form of energy[20].
Renewable energy technology adoption has surged because of the need to combat climate change and reduce greenhouse gas emissions. Grid-interfaced rooftop solar plants are one of these technologies that has grown in popularity because of its accessibility and potential for wide-scale adoption[21]. These systems transform solar energy into electrical energy, enabling owners of residential and commercial properties to produce sustainable energy while minimising their reliance on traditional power sources. In order to maximise the advantages of solar energy, this study focuses on the installation optimisation of grid-interfaced rooftop solar plants[1]. The optimisation process includes a number of steps, such as site evaluation, system design, component selection, safety precautions, and financial considerations[22].
In solar photovoltaic(PV) system the most important work is to identify the need and design system accordingly after that installation part is came which will be satisfactorily done because each system have these life line and duration of these work. If any disturbance occur due to installation then
supply will be interrupted. Due to interruption in supply the user will be dissatisfied and do not believe in reliable operation of the PV system[3].

2. Grid Interconnection and Regulations

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

The process of connecting a rooftop solar system to the current electrical grid is known as grid connectivity. It makes it possible for power to move back and forth between the grid and the solar installation. Grid-tie inverters are essential components of this process because they transform the direct current (DC) generated by solar panels into alternating current (AC) that is compatible with the grid. The regulatory framework controlling grid connections is crucial in order to make it possible for solar electricity to be integrated safely and effectively. Safety requirements are crucial, and following them guarantees that the installation doesn't put the public or utility personnel at risk. These criteria are frequently detailed in the National Electrical Code (NEC) and local regulations [4].
Grid interconnection has certain standards set forth by utility companies, including technical requirements and application processes. For a seamless connection to the grid to be established, adherence to certain utility criteria is essential. Grid interconnection is also impacted by net metering regulations[5]. The financial viability of solar systems is impacted by these rules, which specify how extra electricity produced by the solar system is applied to the owner's utility account. Interconnection agreements are legal contracts outlining the terms and circumstances of grid access between property owners and utility corporations. These contracts include duties, payment schedules, and conflict resolution procedures[6].

Fig. 1 Grid Connected Solar Photovoltaic System

Fig. 1. Grid Connected Solar Photovoltaic System

A key component of rooftop solar installations is grid connections. It allows electricity to flow from solar panels to the grid, which has positive effects on the environment and the economy. A successful and compliant grid-interfaced solar system is ensured by adherence to safety standards, utility requirements, net metering policies, and interconnection agreements[7].

3. Site Assessment and Design

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3.1 Location and Orientation

Performing a thorough site assessment is the first stage in optimising a rooftop solar installation. In order to generate energy, the position of the solar panels is vital. The majority of the day's sunshine typically shines onto rooftops with a south or north orientation, respectively, in the Northern Hemisphere and the Southern Hemisphere[23]. According to the coordinate the latitude 24.60 N ,
longitude 73.73 E, and altitude 423m is situated at Electrical Department of college of technology and engineering. Energy production can be greatly impacted by proper alignment and tilt angles. The PV field orientation is fixed plane with 25 tilt angle[8].

Fig. 2. Solar paths at College of technology and engineering, Udaipur

Fig. 2. Solar paths at College of technology and engineering, Udaipur

3.2 Shading Analysis

To find potential obstacles that could lower the effectiveness of solar panels, shading research is essential. When designing the solar array, it is important to take into account adjacent structures, trees, and other objects that can cast shadows on it. This assessment is aided by tools like sun path calculators and shade analysis software[8].

3.3 System Sizing

To best serve the property's energy requirements, the solar system should be optimised for size. Both oversizing and undersizing may result in excessive energy production or insufficient power production. For the right system size to be chosen, extensive information on energy use and past weather trends should be examined[9].

3.4 Designing with Software

A 100 kW solar power plant's design and output estimation are possible with the aid of software version 7.3.2. The system is created to provide the necessary energy based on the aforementioned criteria. The figure below. shows the solar module, inverter, and array designs as a system[9].

4. Component Selection

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Four hundred PV panels, each producing hundreds of watts, are needed when developing PV facilities. The right number, size, and type of PV modules and inverters must be selected by the designer of the PV plant throughout the design phases. In addition, components for installing PV plants that will increase energy output and improve lifespan maintenance are required. The design of a 100 kW PV plant necessitates some understanding of the system and its parts. As a result, the designer will need to be more knowledgeable about solar data, component specifications, solar PV efficiency, and design optimisation[9].

Table 1 Commercial Solar PV Electrical Data Specification

Type
Poly Crystalline

PV Module Make
REIL Solar Make

No's of Module
400

Maximum Power (Pmax)
320 Wp

Maximum Power Voltage (Vmp)
38.1 V

Maximum Power Current (Imp)
8.40 A

Open Circuit Voltage
46 V

Short Circuit Current
8.90 A

Maximum System Voltage
1000 V

Inverter Make
DELTA

Number of Inverter
2

Inverter Model
Inverter 1 RPI M50
Inverter 2 RPI M50

4.1 Selection of Solar Panel

Efficiency, durability, and warranty considerations should be made when choosing solar panels. With each having advantages and limitations, monocrystalline and polycrystalline panels are popular options. Poly crystalline solar panels are thought to be the most effective, with an average efficiency of about and a fair price. The total number of panel is 400 Numerous PV panel possibilities were researched for this design in terms of power, price, type, and warranty. As a result, 320 Wp of commercial solar power was chosen for this work[10].

4.2 Selection of Inverter

Grid-tie inverters are necessary to convert the DC power generated by solar panels into usable AC power for the building and grid. Micro and string inverters are the two most common varieties. Each has advantages. But in design configuration two string inverter is used such as Inverter 1. RPI M50 and Inverter 2. RPI M50 which having DELTA inverter model. The size, design, and price of the system all affect the inverter that is chosen[11].

Fig. 3 The PV array and inverter characteristics

Fig. 3. The PV array and inverter characteristics

5. Result and Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Results from a suggested photovoltaic system's simulation are examined in this section. The 100 kW polycrystalline photovoltaic system's simulation model's output is displayed in PV systems in accordance with the project's requirements and limits. In this simulation, energy, particular production, and performance ratio were the key outputs. collected data was analysed to determine how well the polycrystalline photovoltaic system performed[12].

Fig. 4 System output power distribution at college of technology and engineering, Udaipur

Fig. 4. System output power distribution at college of technology and engineering, Udaipur

Fig. 5 Daily Input/ Output Diagram

Table 2 Result Overview of 100kWp Poly Crystalline Photovoltaic System

System Kind | No shading
System production | 182MWh/yr
Specific production | 1818 kWh/kWp/yr
Performance ratio | 0.791
Normalized production | 4.98 kWh/kWp/yr
Array losses | 1.14 kWh/kWp/yr
System losses | 0.18 kWh/kWp/yr

5.1 Main Simulation Results

Table 3 100kWp Poly Crystalline Photovoltaic System Balances and Main Results

Months
GlobHor
(kWh/m²)
DiffHor
(kWh/m²)
T_Amb
°C
GlobInc
(kwh/m²)
GlobEff
(kwh/m²)
EArray
kwh
E_Grid
kwh
PR ratio

January
138.8
41.77
15.56
189.6
180.2
16230
15660
0.826

February
159.5
39.23
23.81
201.0
191.3
16351
15769
0.785

March
199.0
56.39
24.23
222.6
211.0
17993
17351
0.779

April
222.5
64.59
30.15
224.9
212.7
17751
17133
0.762

May
234.4
79.18
32.86
219.2
206.8
17298
16/16
0.763

June
183.0
87.60
30.47
166.8
157.0
13479
13049
0.782

July
156.6
95.10
26.69
145.3
136.8
12069
11697
0.805

August
145.0
89.29
25.21
141.2
132.8
11806
11437
0.810

September
165.2
68.29
25.80
176.4
166.9
14510
14021
0.795

October
173.5
54.93
25.41
206.9
196.4
16858
16271
0.787

November
151.1
40.26
22.57
203.4
193.2
16768
16180
0.796

December
142.0
38.19
18.03
201.9
192.0
17069
16469
0.816

Year
2070.7
754.80
25.05
2299.1
2176.8
188182
181753
0.791

Where;
GlobHor: Global horizontal irradiance.
DiffHor: Diffuse horizontal irradiance.
T_Amb : T ambient.
Global incident in a plane, Glob Inc.
GlobEff: Globally Effective, corresponding for 1 AM and shadings.
EArray stands for effective energy at the array's output.
E_Grid: The grid's energy injection.
Performance Ratio (PR)

5.2 Performance ratio and normalized production

The performance ratio is a grading factor that evaluates the quality of a PV facility. It demonstrates how the theoretical and actual energy output of the PV plant relate to one another. After deducting
energy losses and consumption, the PR shows the energy. Due to the inevitable losses that occur during operation, the performance ratio typically hovers around .

Fig. 6 Performance ratio (%)

Fig. 6. Performance ratio (%)

As the PR gets closer to , the system will become more effective and efficient. The performance ratio (PR) of the 100 kW polycrystalline photovoltaic plant is depicted in the following graph. Since the PV system covers of MPUAT university, the monthly performance ratio of the plant is roughly , which is regarded as a significant quantity[13].

Fig. 7 Normalized production energy per month

Fig. 7. Normalized production energy per month

The PV power plant's normalised output is shown in the graph in figure 7 below. It covers the amount of usable energy generated by inverter output, system losses, and PV array collection losses. The monthly output and losses per kwh are both clearly displayed.
Draw a graph between the yield, which represents the annual loss factor, and normalised power production. In this case, the rate of useable energy production is day, the rate of collecting losses (losses on the PV array) is day, the rate of system losses and battery charging is day, and the rate of leftover energy (the energy remaining after the battery is fully charged) is .

Fig. 8 Loss Diagram of Solar PV System

Fig. 8. Loss Diagram of Solar PV System

6. Financial Consideration

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Evaluating costs, incentives, and return on investment (ROI) is necessary for rooftop solar installation financial elements to be optimised. The economic viability of the project can be considerably impacted by financial incentives such as tax credits, rebates, and net metering. Planning should take accurate cost projections and finance possibilities into account[12-13].

Table 4 Financial Consideration by PVSyst

Total Installation Cost | 6,873,000.00 INR
Operation Cost (Incl. inflation 1.00%/year) | 22,594.56 INR/year
Produced Energy | 182 MWh/year
Cost of Produced Energy (LCOE) | 0.124 INR/kWh
Feed-in tariff | 9.3000 INR/kWh
Duration of tariff warranty | 20 years
Payback period | 4.1 year
Net present value (NPV) | 41,698,021.89 INR
Return of investment (ROI) | 606.7 %

7. Environmental Benefits

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Grid-connected rooftop solar panels benefit the environment in addition to the economic benefits. Because it helps to lessen greenhouse gas emissions and prevent climate change, solar power is crucial to creating a cleaner and more sustainable energy future.

Saved CO2 Emission vs. Time

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

Fig. 9 Saved Emission vs. Time

Fig. 9. Saved Emission vs. Time

The environment will benefit from the use of a PV power plant. When energy is produced from fossil fuels, it helps to lower greenhouse gases including carbon dioxide, sulphur dioxide, and nitrogen oxide. Additionally, it aids in reducing ash accumulation[14-15].

Table 5 Values for power plant goods with information on system lifecycle emissions

Item | LCE | Quantity | Subtotal [kg CO₂]
Modules | 1712 kg Co₂/kWp | 97.8 kWp | 167418
Supports | 6.24 kg Co₂/kg | 3910 kg | 24411
Inverter | 619 kg Co₂/units | 2.00 units | 1237

8. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

The electrical department of CTAE, MPUAT, Udaipur, which is situated at the latitude of 24.60 N and longitude of 73.73 E, conducted an energy yield analysis for 1818 kWh of PV solar power generation utilising PV SYST simulation. About of the ratio was performance. 1818 kWh are produced in total. This much energy can be produced each month by setting up 100 kW . This study presents the design modelling and simulation of a grid-connected solar PV power generation facility with a monthly capacity of 100 kW , along with its technical and financial possibilities. Energy injection into the grid has fluctuated from 17351 kWh in the month of March to 11437 kWh in the month of August. The study's findings were as follows: This investigation unequivocally demonstrates the impact of temperature change on the daily and annual performance of solar modules. Compared to solar radiation, efficiency is more responsive to temperature. The plant's efficiency is highest in the morning, peaks in the afternoon, and then starts to decline until dusk. With variations in average module temperature from to , the efficiency of the module ranges from to . Therefore, cooling solar modules may be advised to improve efficiency[16].

References

The original version of this paper is available on https://www.scientific.net/EI.13.25.pdf

A.F. Minai, T. Usmani, A. Iqbal, Performance Evaluation of a 500 kWp Rooftop GridInteractive SPV System at Integral University, Lucknow: A Feasible Study Under Adverse Weather Condition. AI and Machine Learning Paradigms for Health Monitoring System: Intelligent Data Analytics, 2021, pp. 389-96.

M.H. Ons, E.S. Hassan Modwi, Engineering-Economic Evaluation of Al-Fashir 5 MWp Mini-Grid Connected Photovoltaic Power Plant. InSustainability Challenges and Delivering Practical Engineering Solutions: Resources, Materials, Energy, and Buildings, 2023 May 9, pp. 31-33. Cham: Springer International Publishing.

S. Chabachi, A. Necaibia, O. Abdelkhalek, A. Bouraiou, A. Ziane, M. Hamouda, Performance analysis of an experimental and simulated grid connected photovoltaic system in southwest Algeria. International Journal of Energy and Environmental Engineering. 2022 Jun, 13(2), pp.831-51.

T. Adefarati, G.D. Obikoya, G. Sharma, A.K. Onaolapo, K.T. Akindeji, Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings. Energy Systems, 2023 Apr 25, pp.1-60.

M. Benchrifa, J. Mabrouki, M. Elouardi, M. Azrour, R. Tadili, Detailed study of dimensioning and simulating a grid-connected PV power station and analysis of its environmental and economic effect, case study. Modeling Earth Systems and Environment. 2023 Mar; 9(1), pp.53-61.

N.A. Matchanov, K.O. Seok, A.A. Mirzaev, M.A Malikov, D.S. Saidov, Study of energy yield on grid connected micro-inverter type 2.24 kW PV system using PVsyst simulation software. Applied Solar Energy. 2020 Jul, 56, pp.263-9.

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F.F. Ahmad, M. Abdelsalam, A.K. Hamid, C. Ghenai, W. Obaid, M. Bettayeb, Experimental validation of PVSYST simulation for fix oriented and azimuth tracking solar PV system. InModelling, Simulation and Intelligent Computing: Proceedings of MoSICom 2020, pp. 227-235. Springer Singapore.

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H. Al-Zoubi, Y. Al-Khasawneh, W. Omar, Design and feasibility study of an on-grid photovoltaic system for green electrification of hotels: a case study of Cedars hotel in Jordan. International Journal of Energy and Environmental Engineering. 2021 Dec, 12(4), pp.611-26.

Y. Kebbati, L. Baghli, Design, modeling and control of a hybrid grid-connected photovoltaicwind system for the region of Adrar, Algeria. International Journal of Environmental Science and Technology. 2023 Jun, 20(6), pp.6531-58.

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F. Tasghat, A. Bensenouci, M. Fathi, Y. Belkhiri, PVsyst sizing of a PV system for a water supply of an agricultural farm in an isolated area using pivot technique. InArtificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities: Case Study: Tipasa, Algeria 2022, pp. 193-200. Springer International Publishing.

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Chapter 2

Sustainable Approaches in the Development of Urban Drainage Systems

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 37-42

doi: 10.4028/p-MZM0Qd

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-20

Revised:
2024-10-24

Accepted:
2024-11-06

Online:
2025-02-06

Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility

The original version of this paper is available on https://www.scientific.net/EI.13.37.pdf

Fu-Ming Chang , Yi-Pin Wu

¹National Taitung University, No. 369, Sec. 2, University R d., Taitung City 950, Taitung, Taiwan

National Taichung University of Education, No.140, Minsheng Rd., West Dist., Taichung City 403514, Taichung, Taiwan

Changfm@nttu.edu.tw, a0979464992@gmail.com

Keywords: Sustainable Development Goals(SDGs), Climate change, Environmental adjust-ment, Laboratory experiment.

Abstract

Due to the impact of climate change, the accompanying disasters affect the operation of the world. In addition, the development of urbanization has also increased the frequency of disasters. Therefore, the United Nations has established the Sustainable Development Goals (SDGs) to guide the global joint efforts towards sustainable goals. Vegetated Swales are a ubiquitous and growing green storm water type, primarily Low Impact Development (LID) that attenuates peak flow, reduces storm water volume, and treats storm water. Aiming at environmental factors, the research in this paper mainly uses the innovative method of low-impact development, using Vegetated Swales to reduce surface runoff and store The role of water. This study mainly uses the method of Laboratory experiment to simulate the surface runoff caused by urbanization floods, and uses Vegetated Swales to reduce the surface runoff and increase the water permeability. In the experiment, the 5-year, 25year, and 50 -year frequency floods were used as the inflow flow. The common grass speciesPaspalum conjugatum Berg. was used as the test species, and the test slopes is . The results show that, from the test data, with a slope of and a 5 -year frequency flood; 25 -year frequency flood; 50 -year frequency flood the water retention capacity is , and the flood peak value is reduced by . The low-impact development of Vegetated Swales facilities in the initial stage of establishment can be used as a reference for flood treatment.

Introduction

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In recent years, global warming has affected climate and environmental changes, making extreme weather more frequent, not only increasing the incidence of dis-asters, but also increasing the intensity of disasters. There are many environmentally disadvantaged areas in Taiwan, which are bound to become flooded during typhoons and heavy rains. In the past few years, the Taiwan government has actively promoted flood control plans in areas prone to flooding and stepped up water control work, which has achieved remarkable results. However, as global warming causes extreme weather and changes in typhoon rainfall, it is difficult to completely prevent and control extreme weather solely through engineering construction. According to the United Nations Office for Disaster Risk Reduction, the impact of climate change on urban space is increasing. There are many factors, including the increasing urban population and density, as well as the impact on land and infrastructure carrying capacity. Many pressures have resulted in considerable environmental risks and dis-asters in Taiwan's urban areas. To this end, Taiwan must use non-engineering means, coordinate with land planning adjustments, and use the United Nations 2030 sustainable development framework to target the 17 ideals and 169 sustainable development goals in the SDGs in a horizontally connected manner towards sustainability. Sexual development is oriented towards integration, and finally achieves the purpose of coping with extreme climates. This study mainly focuses on the SDG11 goal strategy and SDG13 goal strategy in the United Nations 2030 Sustainable Development Goals (SDGs) to strengthen the ability to adapt to climate change, extreme weather, drought, floods and other disasters, and gradually improve land and soil quality. Utilize low-impact development facility units - grass ditches, which are commonly used in parks and green spaces. Understand the characteristics of grass
ditches, by reducing surface runoff and a little water storage, through experiments, and through experimental settings and The experimental results can reduce surface runoff and flood peak flow, and can be used to alleviate problems caused by urbanization.

Literature Review and Discussion

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As the impact of global climate change and the greenhouse effect becomes increasingly apparent, how to respond to the impact of climate change and achieve a stable balance of natural systems to ensure national security and sustainable development is a challenge that must be faced and should be actively resolved [1]. Since the greenhouse effect was discovered and scientists raised the alarm, the United Nations, governments and non-governmental organizations have begun to develop various types of mitigation strategies, including: saving energy [2] and improving energy efficiency[3], develop emerging and renewable energy [4], develop greenhouse gas reduction technology [5]; however, the trends of global warming and climate change are no longer dependent on human beings can be avoided by reducing greenhouse gas emissions. Therefore, how to adjust social and economic development models so that humans can adapt to the impacts of climate change and continue to survive, live and develop under extreme weather events and warming effects is as important as mitigation. For this reason, mitigation and adaptation have become two important strategies for governments to respond to the threat of climate change [6]. Low Impact Development (LID) facilities, or Green Infrastructure (GI), are mainly used to alleviate problems caused by urbanization [7] and improve the environment by coexisting with water. In addition to reducing the impervious area in urban areas, it can increase the infiltration and evaporation functions of soil and vegetation, reduce surface runoff and reduce peak flow, and conduct rainwater management at the source in a decentralized manner [8], and current research rarely discusses the flood hazards caused by development in non-urban areas and the benefits of location of low-impact development facilities. This study uses grass planting trenches in low-impact development facility units to explore how storage of rainwater through infiltration behavior during the installation process can reduce surface runoff and reduce flow at peak flood times. Through experiments, the main functionality can be under-stood.

Research Methods

The original version of this paper is available on https://www.scientific.net/EI.13.37.pdf

This study adopted the method of grass ditches, that is, most The runoff flows away from the surface and only a small amount of water infiltrates. The inflow conditions in the study are based on the Horner design rain pattern of the Taiwan rainfall station. Common heavy rainfalls are used to simulate the 5 -year flood frequency (Figure 1), 25-year flood frequency (Figure 2), and 50-year flood frequency (Figure 3). The experimental conditions of each group of this study are shown in Table 1. Each group of conditions is given a number to facilitate identification. The numbers of Vegetated Swales are V1-V3. In the experimental configuration of this grass ditch facility unit, the layers from bottom to top are: filtering water storage layer, geotextile fabric, growth medium (improved mixed layer), and covering layer. This study uses the common grass species Amphora as the test grass species, and the common set-ting slope of as the research and discussion. The growth medium uses the com-mon red soil.

Table 1. Vegetated Swales facility test conditions

Experiment number | Slope | Inflow conditions
V1 |  | 5-year frequency floods
V2 | 1% | 25-year frequency floods
V3 |  | 50-year frequency floods

Fig. 1 The rainstorm hydrograph with the recurrence period of 5 years

Fig. 1. The rainstorm hydrograph with the recurrence period of 5 years

Fig. 2 The rainstorm hydrograph with the recurrence period of 25 years

Fig. 2. The rainstorm hydrograph with the recurrence period of 25 years

Fig. 3 The rainstorm hydrograph with the recurrence period of 50 years

Fig. 3. The rainstorm hydrograph with the recurrence period of 50 years

This study uses a slope of and three inflow conditions of 5,25 , and 50 -year design storm water inflows. Based on the experience of the United States [9], a good LID design area ratio must be able to reduce the surface runoff volume. Use effective impermeability as an indicator to deduce appropriate low-impact development (LID) facilities, the ratio to impervious area is about . The planting ditches in this study can be divided into two types: dry ditches and grass ditches. Because the principle of dry ditches is to allow most of the water to penetrate into the subsurface, similar to the rain garden test. In order to avoid repetition, this study adopts the method of grass ditches, that is, most of the runoff flows out from the surface, and only a small amount of water infiltrates.

This study used a slope, combined with the three inflow conditions shown in Figures 1 to 3, to design heavy rain inflows in 5, 25, and 50 years respectively, and placed them in an acrylic box of 60 cm (length) * 120 cm (width) * The test is carried out in a 60 cm (high) container. The conditions of each group are as shown in Table 1. Each group of conditions is given a number to facilitate identification. The planting trench numbers are V1-V3. The configuration and elevation of the test unit is shown in Figure 4. In this facility unit, the layers from bottom to top are: filtering water storage layer, geotextile fabric, growth medium (improved mixed layer), and covering layer. In this study, two-legged grass was used as a test grass species to explore

According to the design conditions and according to the layers in Figure 4, lay gravel grading (coarse aggregate) on the graded base layer, and place a water guide plate on the rectangular opening of the PVC pipe. The water board uses capillary holes to conduct water from the water body into the PVC pipe to maintain uniform water conduction in the acrylic box. Finally, the gravel is graded to cover it to 20 cm , and a layer of non-woven fabric is laid on the graded bottom layer. The growing medium, fresh sea red soil, is laid on it with a thickness of 30 cm , and finally the two-eared grass is laid on top as a covering layer (Picture 5). The predetermined design storm water inflow is applied, and the entire water volume of the vegetation ditch unit is directly runoff. Overall unit (Figure 6). Use a triangular weir to measure the outflow of the outflow box. Based on the water depth at the top of the triangular weir, compare the calibration curve and deduce the test outflow.

Fig. 4 Configuration and elevation of Vegetated Swales test unit.

Fig. 4. Configuration and elevation of Vegetated Swales test unit.

Fig. 5 Paspalum conjugatum Berg. Plant laying.

Fig. 5. Paspalum conjugatum Berg. Plant laying.

Fig. 6 Vegetated Swales Facility test unit overall.

Fig. 6. Vegetated Swales Facility test unit overall.

Experimental Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.37.pdf

Conduct 3 types of inflow tests ( 5 years, 25 years, 50 years) on the LID unit. Conduct 3 tests each, and select one set of results that is more reasonable. Draw the test results and draw the inflow calendar and outflow calendar. The acrylic test chamber used in this study is equipped with three types of inflow flow rates ( 5 years, 25 years, and 50 years) and a configuration with a slope of 1 degree. Due to the experimental design, it is similar to the traditional grass ditch flow mechanism. If a dry grass ditch is used, the mechanism is similar to a line-ar rain garden). The water retention capacity of the grass ditch is summarized in Table 2. Here, the rainfall conditions and test slope are discussed. Effect on water retention capacity. Draw the inflow and outflow calendars of each group of tests in Vegetated Swales, as shown in Figures 7 to Figures 9. Due to the poor permeability of the soil used in the unit test, soil in general areas will cause soil compaction over time, resulting in poor soil permeability, so most of the water is surface runoff. This experiment is also in line with the on-site conditions. It flows out from the outlet at the top of the test box. Even if it infiltrates, the amount of infiltration is very small. It mostly accumulates at the bottom of the sand box and cannot be measured. Therefore, regardless of whether the rainfall conditions are 5 years, 25 years, or 50 years, the water retention capacity in the Vegetated Swales test is less than . The final results of this experiment show that when the slope of the vegetation ditch is , the water retention capacity of floods occurring once in 5 years, floods occurring once in 25 years, and floods occurring once in 50 years is , and respectively, and the peak flood peak is reduced by and , . Low-impact development in the initial stage of construction of vegetation swale facilities can be used as a reference for flood treatment. Grass-planting ditches can be set up according to specific
needs. Abroad, they are mainly used to filter dirt and purify water. In subtropical climate areas, maintenance costs will increase due to excessive growth of grass seeds. . In addition, planting turf can also have the effect of cooling and reducing carbon, so as to achieve the purpose of sustainable environment.

Fig. 7 Vegetated Swales experiment results for 5-year return period

Fig. 7. Vegetated Swales experiment results for 5-year return period

Fig. 8 Vegetated Swales experiment results for 25-year return period

Fig. 8. Vegetated Swales experiment results for 25-year return period

Fig. 9 Vegetated Swales experiment results for 50-year return period

Fig. 9. Vegetated Swales experiment results for 50-year return period

Table 2. Water retention capacity of Vegetated Swales planting trench test conditions

Test conditions | Rainfall conditions | Test slope
Group 1 | 5 years | 1%
Group 2 | 25 years | 1%
Group 3 | 50 years | 1%

References

The original version of this paper is available on https://www.scientific.net/EI.13.37.pdf

Hecht, Alan D. Making America green and safe: A history of sustainable development and climate change. Cambridge Scholars Publishing, (2018).

Aboelata, Amir. "Assessment of green roof benefits on buildings' energy-saving by cooling outdoor spaces in different urban densities in arid cities." Energy 219: 119514(2021).

O'Rielly, K., and J. Jeswiet. "Strategies to improve industrial energy efficiency." Procedia Cirp 15: pp. 325-330 (2014).

Raihan, Asif, et al. "The role of renewable energy use, technological innovation, and forest cover toward green development: Evidence from Indonesia." Innovation and Green Development 2.1: 100035(2023).

DeLisi, Charles, et al. "The role of synthetic biology in atmospheric greenhouse gas reduc-tion: prospects and challenges." BioDesign Research (2020).

Gan, Rong, et al. "Effects of projected climate change on the glacier and runoff generation in the Naryn River Basin, Central Asia." Journal of Hydrology 523: pp. 240-251(2015).

Ahiablame, Laurent M., Bernard A. Engel, and Indrajeet Chaubey. "Effectiveness of low impact development practices: literature review and suggestions for future research." Water, Air, & Soil Pollution 223: pp. 4253-4273 (2012).

Tansar, Husnain, Huan-Feng Duan, and Ole Mark. "Catchment-scale and local-scale based evaluation of LID effectiveness on urban drainage system performance." Water Resources Management: 1-20 (2022).

Blackler, Gerald E., and James CY Guo. "Paved area reduction factors under temporally varied rainfall and infiltration." Journal of irrigation and drainage engineering 139.2: pp. 173179(2013).

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Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 43-48

doi: 10.4028/p-Vy0QHL

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-20

Revised:
2024-10-24

Accepted:
2024-11-06

Online:
2025-02-06

The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

The original version of this paper is available on https://www.scientific.net/EI.13.43.pdf

Fu-Ming Chang

¹National Taitung University, No. 369, Sec. 2, University R d., Taitung City 950, Taitung, Taiwan

Changfm@nttu.edu.tw

Keywords: Sustainable Development Goals(SDGs), environmental improvement, Green Roof, Green Infrastructure, Laboratory experiment.

Abstract

At this stage, the United Nations has established the Sustainable Development Goals (SDGs) to guide global sustainability goals. In order to improve the environment and achieve the goal of sustainable development. This study mainly uses green roofs, the most common among green infrastructures, as the object of study. Green roofs are an auxiliary facility commonly developed on general buildings. They can reduce flood flow and increase water storage capacity through infiltration. This study mainly uses indoor experiments, using green roofs as experimental facilities to simulate the effect of increasing infiltration. The experiment uses an indoor rainfall machine, sets situational simulation conditions as the inflow flow, and uses common grass species as planting. The final experimental results show that this experiment mainly uses a horizontal slope of as the condition for a flat roof. The infiltration rate is as high as more than , and the flood peak can be reduced by more than . Therefore, in the initial design of the building structure, green roof facilities can be considered to achieve the water retention effect through the infiltration rate and reduce the peak flood. It can later be used as a reference for water retention in the building base.

Introduction

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In the process of urbanization, economic development has reduced the usable space on the original land, and at the same time changed the land use pattern, and will also change the original hydrological environment and ecology of the land. As the surface impermeability gradually increases in the region, the hydrological cycle function of the original land is reduced, rainfall infiltration into the soil is reduced, flood peak flow is increased, and groundwater replenishment is also limited. Green infrastructure can mitigate the negative impacts of urbanization. Managing rainwater at the source in a decentralized manner not only increases the soil infiltration rate, reduces peak flow, and slows down the trend of hydrological changes after development, but also increases the area of green space, reduces the pressure on the ecological environment, and restores the help of plants to the environment. The water stored in the facility can also moderately alleviate the urban heat island effect through evaporation. This study mainly takes green roofs in green infrastructure as the re-search object. It uses indoor artificial rainfall machines to simulate general rainfall patterns, and uses scenario conditions as simulation conditions to explore the impact of green roof facilities on general buildings on flood peaks. The research results can be used as a basis for the construction of new building water storage systems in the future and the improvement of the environment in flooded areas around the building. Indoor experimental results show that the peak flow reduction benefits are greater than .

Literature Review and Discussion

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Cities often develop rapidly due to commercial, economic or people's livelihood needs. Largescale construction consumes the original green space in the city, causing the original green space to become fragmented and shrink. Therefore, the ecological and social impact of urban development can be reduced through the development of green infrastructure. The impact caused by green spaces and open spaces is solved, and the problem of landscape fragmentation such as green spaces and open spaces is solved. Generally, basic public facilities systems need to be constructed to maintain urban development, which can be collectively referred to as gray infrastructure [1]. Green infrastructure
(G.I.) is a relative concept. Through the interconnected green The spatial network is composed of various open spaces and natural areas, surrounding and connecting gray infrastructure and other components to form an interconnected and organically unified network system. [2]

Low Impact Development (LID) facilities, or Green Infrastructure (GI), are mainly used to mitigate problems caused by urbanization [3] and improve the environment by coexisting with water. In addition to reducing the impervious area in urban areas, it can also increase the infiltration and evaporation functions of soil and vegetation, reduce surface runoff and reduce peak flow, and manage rainwater at the source in a decentralized form. However, current research rarely discusses the development of non-urban areas. Flooding caused by behavior, and the benefits of low-impact development facilities. The green roof is mainly constructed to improve the urban eco-logical environment. It is a multi-functional roof structural component option; it can provide green shade through plants and condensation and evaporation (water is gradually released into the surrounding air through the pores of the plants). To reduce the urban heat island effect, filter, absorb or retain rainwater, and help reduce the impact of urbanization on water quality [4]; it is a lightweight growing medium structure, beautifies urban roofs, reduces the impact of the greenhouse effect, and achieves The function of energy saving and carbon reduction. The structure of green roofs in Europe can be simply divided into: the basic components of roof greening are a vegetation layer, a substrate layer and a drainage layer [5]. The prerequisite for setting up a green roof is to ensure the safety of the building's roof structure. The components of a green roof must at least include: waterproofing, drainage, growing media, plants and other structural facilities. Each structural layer must have many types of materials to choose from.

Based on the depth of the base material layer, green roof types are also divided in-to two main types in Europe [6] as follows: "extensive green roofs" (extensive green roofs), the maximum depth of the base layer is about 150 mm [7]. Species often form an important part of the vegetation. This type can also be installed on sloping surfaces, with slopes up to 45 degrees, and "intensive green roofs" have a base layer depth of more than 150 mm [8].

Referring to the concept of relevant green infrastructure operation cases in the United States and the United Kingdom, it is known that the most important thing when implementing a green infrastructure plan is to conduct an inventory of green resources to identify appropriate protection, consolidation, coordination, and reconstruction development targets in the future [9]. The uniqueness of green infrastructure planning is that it combines a systematic planning approach with conservation concepts, incorporates natural asset management into the overall land use planning process based on local land use planning, and develops green infrastructure based on local conditions and site characteristics. By defining the resource types and projects included in the green infrastructure, the resource classification types include the cur-rent land use status and the existing land use status [10].

Research Methods

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The design size of the green roof test (2) is 120 cm (length) * 60 cm (width). Based on the recommended area ratio of the LID facility, this LID facility is scheduled to be used to process rainfall in a catchment area of . Runoff and rainfall conditions are designed for 60-minute heavy rainfall. Refer to the rainfall pattern used in Alfredo et al., 2010, and use the instrument conditions of the wind and rain laboratory to design three scenario rainfall calendars as shown in Figure 1 and Figure 2. As shown in Figure 3.

The green roof receives the rainfall from the artificial rainfall machine, and flows into the rain barrel from the seepage port through the pipeline. The green roof and rain barrel of the LID are integrated. This study used 1 slope (horizontal ), combined with the three inflow conditions shown in Figures 1 to 3, to reduce the design storm inflow for the three scenarios, in a wooden box of 60 cm (length) (width) The test is carried out in a (high) container. The conditions of each group are as shown in Table 1. Each group of conditions is numbered to facilitate identification. The numbers of the green roof + rain barrel are GR1-GR3. The configuration and elevation of the test unit is shown in Figure 4. In this facility unit, the layers from bottom to top are: waterproof layer, root blocking layer, drainage and water retention layer, geotextile, growth medium layer (intensive type),
and covering layer (planting). This study uses Amphora as the test grass species, and studies the green roof + rain barrel with a horizontal slope of . The growing medium is fresh sea red soil.

Table 1. Vegetated Swales facility test conditions

Experiment
number
Slope
Inflow conditions

GR1

situational rainfall(1)

GR2
0%
situational rainfall(2)

GR3

situational rainfall(3)

Fig. 1 Situational Rain-1.

Fig. 1. Situational Rain-1.

Fig. 2 Situational Rain-2.

Fig. 2. Situational Rain-2.

Fig. 3 Situational Rain-3.

Fig. 3. Situational Rain-3.

Fig. 4 Green roof test configuration.

Fig. 4. Green roof test configuration.

The green roof receives the rainfall from the artificial rainfall machine, and flows into the rain barrel from the seepage port through the pipeline. The green roof and rain barrel of the LID are integrated. In this study, three different slopes ( slope, combined with the three inflow conditions in Table 1, were used to reduce the design storm inflow for three scenarios. In this study, wooden boxes were made of 60 cm (length) * 120 cm (width) * The test is carried out in a 30 cm (height) container. Each set of conditions is numbered for easy identification. The green roof + rain barrel number is GR1-GR3. The configuration and elevation of the test unit is shown in Figure 4. In this facility unit, the layers from bottom to top are: waterproof layer, root blocking layer, drainage and water-retaining layer, geotextile, growth medium layer (intensive culture type), and covering layer (planting). In this study, Amphora was used as the test grass species. The green roof + rain barrel
slope (horizontal 0%) was used as a research and discussion. The growing medium was fresh sea red soil.

Green roof grass seeds are laid with amphora. The layout of the test chamber is shown in Figure 5. The bottom of the test chamber is supported by a stainless steel iron frame. The iron frame device can adjust the slope to meet the slope required by the test (Figure 6). Place the facility flat in the center of the artificial rain machine, and measure the outflow of the green roof test box using the rainfall of the rain ma-chine as the inflow condition.

Open a 1-inch PVC water pipe in the test box to facilitate drainage, connect the rain barrel with a plastic pipe, and place the rain barrel on an electronic scale. Measure the weight of the water discharged from the green roof test box and enter the rain barrel, and convert it into a volume (equivalent to 1 kilogram) 1 liter), the test device is shown in Figure 7. The actual rainfall situation is shown in Figure 8.

Fig. 5 Test box layout diagram.

Fig. 5. Test box layout diagram.

Fig. 7 Experimental configuration.

Fig. 7. Experimental configuration.

Fig. 6 Adjustable stand.

Fig. 6. Adjustable stand.

Fig. 8 Actual experimental situation.

Fig. 8. Actual experimental situation.

Experimental Results and Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.43.pdf

Using a 60 cm (length) (width) (height) test chamber, with three inflow flows (scenario 1 , scenario 2 , scenario 3 ), and a configuration with a slope of , the test results are as follows: The test results of slope 0 degrees (GR1~GR3) are shown in Figure 9- Figure 11. The water retention capacity of the green roof + rain barrel is summarized in Table 2. In this facility, the impact of rainfall conditions and test slope on the water retention capacity is discussed. Among the three rainfall scenarios, the rain pattern designs of Scenario 1 and Scenario 2 are similar, while Scenario 3 cooperates with the wind and rain laboratory to design rain patterns that increase over time. The overall total rainfall is: Scenario Scenario 2. In terms of water retention capacity GR1-GR3, the
infiltration capacity of low rainfall frequency is better than that of high frequency rainfall. In terms of flood peak, Scenario 2 is better than Scenario 1. With the indoor rainfall machine controlling the intensity of rainfall, the green roof and rain barrel test result is a horizontally placed (flat-top) green roof. In terms of the water retention capacity of green roof facilities, with a slope of , the horizontal water retention capacity of 0 degrees is the best. This study mainly uses green roof facilities, whose purpose is to slow down the rise in building temperatures and help reduce carbon emissions, thereby achieving the United Nations' sustainable development goals.

Table 2. Water retention capacity of Vegetated Swales planting trench test conditions

Slope | 0%
Experiment number | GR1 | GR2 | GR3
Flood conditions | situational rainfall(1) | situational rainfall(2) | situational rainfall(3)
Infiltration (%) | 59.23 | 56.66 | 57.12
Peak reduction (%) | 32.99 | 35.26 | 38.86

Fig. 9 Green Roof Test Results - Scenario Rainfall 1.

Fig. 9. Green Roof Test Results - Scenario Rainfall 1.

Fig. 10 Green Roof Test Results - Scenario Rainfall 2.

Fig. 10. Green Roof Test Results - Scenario Rainfall 2.

Fig. 11 Green Roof Test Results - Scenario Rainfall 3.

References

The original version of this paper is available on https://www.scientific.net/EI.13.43.pdf

Benedict, Mark A., and Edward T. McMahon. "Green infrastructure: smart conservation for the 21st century." Renewable resources journal 20.3 (2002): 12-17.

Chenoweth, Jonathan, et al. "The interrelationship of green infrastructure and natural capi-tal." Land use policy 75 (2018): 137-144.

Ahiablame, Laurent M., Bernard A. Engel, and Indrajeet Chaubey. "Effectiveness of low impact development practices: literature review and suggestions for future research." Water, Air, & Soil Pollution 223 (2012): 4253-4273.

Park, Jinsil, et al. "Efficient plant types and coverage rates for optimal green roof to reduce urban heat island effect." Sustainability 14.4 (2022): 2146.

Mentens, Jeroen, Dirk Raes, and Martin Hermy. "Greenroofs as a part of urban water management." Progress in Water Resources 8 (2003): 35-43.

Krupka, Bernd. "Dachbegrünung: Pflanzen-und Vegetationsanwendung an Bau-werken." (1992).

Nash, C., Ciupala, A., Gedge, D., Lindsay, R., & Connop, S. (2019). An ecomimicry design approach for extensive green roofs. Journal of Living Architecture, 6(1), 62-81.

Teotónio, Inês, Cristina Matos Silva, and Carlos Oliveira Cruz. "Economics of green roofs and green walls: A literature review." Sustainable Cities and Society 69 (2021): 102781.

Leal Filho, Walter, et al. "Addressing the urban heat islands effect: A cross-country assess-ment of the role of green infrastructure." Sustainability 13.2 (2021): 753.

Zhang, Xindi, et al. "Development of a cross-scale landscape infrastructure network guided by the new Jiangnan watertown urbanism: A case study of the ecological green integration demonstration zone in the Yangtze River Delta, China." Ecological Indicators 143 (2022): 109317.

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Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 49-54

doi: 10.4028/p-lU6byG

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-07-18

Revised:
2024-08-29

Accepted:
2024-10-16

Online:
2025-02-06

Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

The original version of this paper is available on https://www.scientific.net/EI.13.49.pdf

LAM Tri-Khang , NGUYEN Dinh-Thang , NGO Van-Anh , NINH Hoai Phuong Duy and HUYNH Trong-Phuoc

Faculty of Civil Engineering, College of Engineering, Can Tho University, Campus II, Street, Ninh Kieu District, Can Tho City 94000, Vietnam

Faculty of Automotive Engineering Technology, Vinh Long University of Technology Education, No. 73, Nguyen Hue Street, Vinh Long City 85000, Vinh Long Province, Vietnam

Faculty of Transportation Engineering, College of Engineering, Can Tho University, Campus II, 3/2 Street, Ninh Kieu District, Can Tho City 94000, Vietnam

ODA Project Management Unit, Can Tho University, Campus II, Street, Ninh Kieu District, Can Tho City 94000, Vietnam

htphuoc@ctu.edu.vn (*Corresponding author)

Keywords: Pervious concrete pavement, flexural strength, compressive strength, infiltration rate, dry density.

Abstract

This study presents an experimental investigation of the load-bearing behavior and drainage ability of pervious concrete (PC) produced using local materials in Vietnam with differently designed porosities of , and . The PC samples were cast at the laboratory and cured in open air before being tested for drainage ability, flexural and compressive strengths, and dry density. The relationship between load and displacement of the PC was also analyzed in this study. Results showed that the designed porosity significantly affected the mechanical strength, density, and infiltration rate of the PC. In detail, the compressive strength of PC ranged from 1 to 5 MPa while the flexural strength values were , and 1.71 MPa for samples designed at the respective porosities of , and . In addition, the dry density of PC specimens ( ) decreased with an increase in the designed porosity. The infiltration rate values were measured at , and corresponding to the R15, R20, and R25 specimens. Besides, the load-displacement curves under flexure and compression revealed a significant impact of void contents on the strength-bearing capacity of PC as the R15 specimen exhibited the highest slope, indicating the higher brittle behavior and lower deformation. Overall, the experimental results further demonstrated the high applicability of the PC for various construction applications in real practice.

Introduction

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Urbanization and modernization are growing faster in the modern days. Cities nowadays have grown considerably and have a greater potential to continue expanding. As a result, there are now many structures and concrete roads, which creates many problems related to drainage. So far, conventional pavement has inhibited water permeability. This leads to rising temperatures, reduced traffic safety, escalation of the urban heat island effect, and an increased risk of sudden floods . This creates many problems for infrastructure and citizens. It is important to note that uncontrolled runoff water can transform into sewage, putting groundwater resources at risk of contamination. Therefore, in response to these challenges, pervious concrete (PC) has become an essential component of modern civil engineering, providing innovative solutions for urban infrastructure and environmental sustainability.

Pervious concrete, also known as porous concrete, permeable concrete, gap-graded concrete, no-fine concrete, and enhance-porosity concrete [3]. It is a special concrete that refers to an almost zero-slump, with a high interconnected pore structure and large pore sizes ( typically). In the PC mixture, fine aggregate is non-existent or present in very small amounts [4]. Thus, the porosity in

PC is significantly higher than that of normal concrete, which is typically designed within a range of to [3-6]. The void and pore structure are important properties that influence the performance of PC. Because of its high porosity and permeability, PC can control stormwater runoff, restoring groundwater supplies as well as the urban enthalpy and heat island effects [6]. Besides, PC is also commonly used for building materials because of its high permeability and acceptable strength. In the past, the PC was first used in 1852 in Europe due to a lack of sand and other fine aggregates [3]. Over time, PC has demonstrated its great application potential. Currently, this type of concrete is widely used in road surface pavement, parking areas, sidewalks, pathways, residential roads, driveways, drainage materials, structural walls, floors for greenhouses and zoos, and swimming pool decks [4,5,7].

With high applicability, several studies have utilized various mineral additives to manufacture PC such as fly ash, metakaolin, and slag [1,8,9]. Additionally, the influences of different particle sizes, artificial aggregates, aggregate-to-binder ratio, and water-to-binder ratio have been investigated using different methods and different sample sizes [10-13]. Some key findings drawn from previous studies indicated that the water-to-binder ratio commonly fell within the range of 0.26 to 0.4 [1,4,14], aggregate sizes ranged from 2.36 to 30 mm [1,4], aggregate contents ranged from to [1,15], and the paste volumes ranged from to [15]. Besides, PCs with mineral additives exhibited higher performance in engineering properties, cost savings, and energy efficiency [8,9]. In terms of strength and durability of PC, the crucial factors impacting its technical characteristics significantly include the porosity, pore sizes, cementitious contents, paste volume, water-to-binder ratios, compaction level, and aggregates gradation and quality . Particularly, the variation of permeability of PC may be in the ranges of , and the typical compressive strength is between 2.8 to 28 MPa , depending on compaction, void content, materials, and sub-base infiltration rates [4,14]. The compressive strength and flexural strength had a similar trend [12].

As presented, most of the previous research concentrated on the mechanical strength while not much research has been carried out to evaluate the load-displacement behavior of PC. Besides, the challenge in PC mixture proportioning is achieving a balance between an acceptable percolation rate and an acceptable compressive strength [4]. Thus, both the load-displacement behavior and drainage ability of PC produced using locally sourced materials in Southern Vietnam were evaluated. The potential applications of such concrete in real practice were also considered in the present study.

Experimental Details

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Materials and mixture proportions. Grade-40 Portland cement in compliance with TCVN 2862:2020 standard [17] was used as binder material and No. 8 natural crushed sand (ranges size of 2.36-9.5 mm, density of , and water absorption of ) as per ASTM C33 was used as fine aggregate for the preparation of PC samples. Using a lower aggregate diameter resulted in a greater strength and durability value for a given paste volume due to the higher packing density [4,5]. Therefore, the No. 8 classified aggregate was commonly used in the manufacturing of PC samples [4].

Table 1. Mixture proportions of of pervious concrete

Mix designature | Designed porosity (%) | Cement (kg) | Aggregate (kg) | Water (kg)
R15 | 15 | 497.1 | 1824.9 | 185.3
R20 | 20 | 429.4 | 1935.1 | 167.2
R25 | 25 | 369.2 | 2033.3 | 151.0

The design of PC used in this experiment was primarily based on ACI 522R-10 [4] with the designed porosities of , and and the water-to-cement ratio in the ranges of to ensure a good aggregate coating and paste stability. Note that the porosity in the PC should be from to [4] because, in this range, the water could percolate through the PC. At a porosity level of below , there was insufficient interconnectivity between the porosities to allow for quick percolation, leading to insignificant percolation [3,4]. In addition, porosities of to are necessary to ensure the strength capacity of the PC as a porosity of higher than could cause a
decrease in mechanical strength in the PC. By following the designed procedures of ACI 522R-10 [4], the material proportions of PC with different porosities are calculated as shown in Table 1.

Sample preparation and test methods. To prepare the PC samples, cement and aggregate were first prepared. All of the materials were then dry-mixed in a laboratory mixer for 2 mins. After that, water was gradually added and mixed until a homogeneous mixture. The fresh mixture was poured into a steel mold with dimensions of (Fig. 1, left). The samples were compressed under 1.5 MPa and then covered with a thin plastic to maintain the moisture [1]. After 24 hours, the samples were de-molded and cured in the open air until testing days (Fig. 1, right).

Fig. 1 Casting of pervious concrete samples

Fig. 1. Casting of pervious concrete samples

After 28 days of curing, the casted samples were cut for testing of dry density, flexural and compressive strengths, and infiltration rate. The density of PC was determined on specimens following ASTM C642. The flexural strength (Fig. 2a) and compressive strength (Fig. 2b) of PC were tested using the and the specimens following ASTM C78 and ASTM C39, respectively. Besides, the infiltration rate (Fig. 2c) was measured according to the ASTM C1701. The load-displacement curves of the PC under flexure and compression were also recorded to clarify the relationship between the load and deformation behavior of the PC under loading. Finally, the potential applications of PC were suggested based on the experimental results.

Fig. 2 Setup for testing of (a) flexural strength, (b) compressive strength, and (c) infiltration rate

Fig. 2. Setup for testing of (a) flexural strength, (b) compressive strength, and (c) infiltration rate

Results and Discussion

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Dry density. Table 2 illustrates the variation in dry density values of PC specimens. Particularly, the density of all PC specimens was below , which is much lower than that of conventional concrete because of the significantly higher porosity in the PC system [14]. Results also found that there was an around difference between the minimum and the maximum dry density values of the PC, which is primarily due to the change in the designed porosity. Previous studies proved an inverse correlation between porosity and density of concrete [ ]. Thus, the R15 specimen with the lowest designed porosity ( ) exhibited a denser structure and the highest dry density ( ).

Table 2. Dry density measurements of pervious concretes

Mix designation | Dry density (kg/m³) | Infiltration rate (cm/min.)
| Value | Standard deviation |
R15 | 1688 | 36.429 | 19.4
R20 | 1620 | 32.182 | 38.3
R25 | 1560 | 22.948 | 1215

Infiltration rate. The infiltration rate (permeability) is a critical engineering characteristic of PC, indicating the rate at which water can pass through the material. The speed of infiltration in PC with different porosity is shown in Table 2. As a result, the infiltration rate of PC specimens ranged from 19.4 to . The lowest infiltration rate of was observed at the designed porosity of . However, the infiltration rate increased to in the R20 specimen. The highest infiltration rate of was recorded in the R25 specimen. These results proved that porosity was the most important feature of PC, significantly affecting its permeability. Furthermore, the relationships among infiltration rate, dry density, porosity, and strength of PCs were observed in previous studies as higher density was associated with lower porosity, lower permeability, and higher strength [5,13,14].
Flexural strength. PC's flexural strength is defined as its capacity to bear bending or flexing forces without cracking our serious deformation. PC is typically used for applications such as sidewalks, parking lots, and low-traffic pavements, where it is subjected to loads that can cause bending stresses. Test results showed that the flexural strength values of PC were in the ranges of (Table 3). In which, the R15 specimen exhibited the highest flexural strength of 1.71 MPa . This value was decreased by approximately and when the designed porosity increased to and , respectively. Hence, the porosity played an important role in controlling the flexural strength of the PC as the higher the porosity, the lower the flexural strength [16]. This trend was also observed in previous studies [15,16]. As presented in previous sections, the R15 specimen had lower porosity, resulting in higher flexural strength. Furthermore, the results also demonstrated a correlation between compressive strength and flexural strength as higher compressive strength was associated with higher flexural strength. This trend was in good agreement with a previous study [12].

Table 3. Flexural and compressive strengths of pervious concretes

Mix designation | Flexural strength (MPa) | Compressive strength (MPa)
| Value | Standard deviation | Value | Standard deviation
R15 | 1.71 | 0.048 | 5.08 | 0.391
R20 | 0.99 | 0.045 | 2.76 | 0.048
R25 | 0.86 | 0.089 | 1.09 | 0.264

Fig. 3. Flexural load-displacement curves

Fig. 3. Flexural load-displacement curves

Fig. 4. Compressive load-displacement curves

Fig. 4. Compressive load-displacement curves

Besides, Fig. 3 depicts flexural load-displacement relationships for the PC proportioned for , , and porosities. As a result, the R15 specimen exhibited the highest displacement at the point of failure, demonstrating that R15 was stiffer and more brittle than others. The more porosity in R20 and R25 specimens caused a reduction of flexural strength and strain-carrying capacity [15].

Compressive strength. The compressive strength of a PC refers to its ability to withstand axial loads or forces applied along its axis without failing or breaking. PC is designed to allow water to pass through, but it still needs to have enough compressive strength to support the intended loads in applications such as sidewalks, parking lots, and low-traffic pavements. The 28 -day compressive strength of PCs is presented in Table 3. As a result, the compressive strength value reached 5.08 MPa for the PC specimen with designed porosity of . Increasing the designed porosity to and reduced the compressive strength values by about and , respectively. This trend was in line with previous studies .

Fig. 4 shows the compressive load-displacement curves recorded from the compressive strength test of PC specimens. This figure provided a comprehensive reflection of the compressive behavior of the PC as the greater porosity caused a faster decrease in the compressive strength. The consistent decline in peak compressive strength was found with rising porosity, aligning with expectations for porous materials. Notably, different pore structure aspects (i.e., pore sizes, spacing, and connectivity) in the PC may significantly influence the compressive response. Besides, the slopes of the ascending portions of the curves were different [7,19]. The R15 specimen exhibited the highest slope, followed by the R20 and R25 specimens. This might be attributed to the compactness of the specimens. As aforementioned, the R15 had a higher density, making it more compact with lower deformation than the others. The compressive load-displacement curves also revealed the brittleness of the specimens. A previous study proved that the PC samples with lower porosity exhibited higher brittle behavior, which was indicated by a sudden fall in post-peak load [7].

Potential applications of pervious concrete. PC is increasingly used due to rapid urbanization and has been used widely across various climatic, structural, and environmental conditions. Specifically, PC should be applied in certain cases such as pervious pavement [20], bridges [21], parking lots (with a thickness of 125-300 mm), pathways, low-volume roads (with a thickness of 150-300 mm) [22], permeable bases and edge drains (with a strength of ), shoulders (with compressive strength ), drains, greenhouses, tennis courts, noise barriers, and building walls [4]. Moreover, the sound restriction of PC is proved when it was applied in transportation [23,24]. Besides, PC can be applied to residential streets for typical design [25] and other constructions, especially in the USA, Japan, Europe, and China [26]. However, PC cannot be used for reinforcement, high-speed roads, and heavy-load traffic roads due to its mechanical properties and the open pore in the structure. This study aims to understand the mechanical characteristics and behavior of PC under loading. The results showed that PC can be used in many constructions, proving the potential of using PC. With relatively low strength, the PC produced in this study may be suitable for applications in drainage layer and pathway construction.

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.13.49.pdf

This study experimentally investigated the load-bearing behavior and drainage ability of PC produced using local materials in Vietnam with differently designed porosities (i.e., 15, 20, and 25%). Based on the experimental results and the associated discussions, the following conclusions can be drawn:

PC specimens registered dry density values in the ranges of , which were significantly lower than conventional concrete. This study also found that higher dry density values were strongly associated with lower porosity in the PC specimens.

The infiltration rate of PC specimens ranged from to . The infiltration rate increased up to when increasing the designed porosity from to . Further increasing the designed porosity to resulted in a remarkable infiltration rate of .

The 28-day flexural and compressive strengths of PC were distributed from 0.86 MPa to 1.71 MPa and 1.09 MPa to 5.08 MPa , respectively. Generally, the lower the designed porosity of the PC, the higher the mechanical strength. The load-displacement curves of the R15 specimen were steeper and dropped suddenly after reaching the peak point, indicating that R15 was more brittle than that of the R20 and R25 specimens.

Literature reviews showed that PC can be applied for many construction purposes, proving the high potential of using PC. The results of this study suggested that the produced PC was suitable for applications in drainage layer and pathway construction.

References

The original version of this paper is available on https://www.scientific.net/EI.13.49.pdf

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Chapter 3

Water Treatment and Distribution

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 57-73

doi: 10.4028/p-h2WAGU

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Revised:
2024-11-27

Accepted:
2024-11-28

Online:
2025-02-06

Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

Marie Fe Y. Lacsado , Doris B. Montecastro

Department of Civil Engineering and Professional Schools, University of Mindanao, Matina, Davao City 8000, Philippines

School of Engineering and Architecture, Ateneo de Davao University, Davao City, Davao del Sur, Philippines

Environmental Science Department, Ateneo de Davao University, Davao City 8000, Philippines

mfylacsado@addu.edu.ph, dbmontecastro@addu.edu.ph

Keywords: Geographic Information System, Spatial Analysis, Rainfall Data, Rainwater Harvesting Potential, Digitized Roof Area, Vector Grid, Seawater-Based Community.

Abstract

A lack of fresh water is a serious issue in communities near or surrounded by seawater, such as Sitangkai Poblacion, where they rely heavily on the ocean for their water source. QGIS with vector grid as the tool will help to study the possibility of efficient rainwater harvesting. Data processing for analysis of rainfall data from Weatherlink station in Bongao, Tawi-Tawi, and roof area digitization conducted for Sitangkai Poblacion. The spatial distribution of rainwater harvesting was assessed systematically at the vector grid level. Data was collected on monthly precipitation during June 2023 and March 2024 to estimate possible water collection volumes. The results present significant grid-to-grid variability in the potential for rainwater harvesting, driven by roof area and regional rainfall patterns. The months when there was a lot of rainfall during the rainy season are significantly associated with the month peak rainwater harvesting occurred; this means an ideal time to boost water collection. The study demonstrates the necessity of specific rainwater harvesting approaches that focus on dominant household variables in conjunction with improved storage and holistic water handling procedures for sustainable solutions for these areas. Vector grid in QGIS can be a potential methodology for researchers to spatially analyze and guide sustainable water resource planning addressing freshwater scarcity.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

Due to the scarcity of freshwater sources, many seawater-based communities [1], [2] require solutions for water-related issues since access to this limited supply and its salty nature have created several problems in their daily lives. To address water scarcity, a solution named Rainwater Harvesting (RWH) has rightly come into existence globally, particularly in rural or inaccessible regions. This study systematically assessed the available terrestrial water resources aimed at rainwater harvesting potential in a seawater-based community known as Poblacion Sitangkai, Philippines. The spatial distribution of rainwater harvesting potential was evaluated using a vector grid approach in QGIS [4],[5], providing valuable insights for sustainable water resource planning [8],[9],[10], as well as resilience against freshwater scarcity. The general objective of this study is to determine the potential for rainwater harvesting in the seawater community, mainly focused on Poblacion Sitangkai. Specifically, it aimed to determine the following: Climatological data; digitize boundary maps and vector polygons representing roof areas; estimate rainwater collection volumes; identify peak rainwater harvesting months and optimal periods for rainwater collection.

For this purpose, a vector grid approach is utilized in QGIS to determine the spatial pattern of rainwater harvesting potential over Poblacion Sitangkai, with the ultimate goal of providing data for sustainable water resource planning and vulnerability reduction against the freshwater shortage.

Scope and Limitation. Rainwater harvesting potential Poblacion Sitangkai spatial distribution vector Grid QGIS Macro grid water collection estimates were calculated monthly precipitation data from June 2023 through March 2024 except February since there was no recorded data for technical
reasons behind (Weatherlink station in Bongao, Tawi-Tawi). They were using rain data, digitizing an area of the roof to calculate its potential rainfall catchment. The study also sought to establish when rainwater harvesting peaked by linking precipitation patterns with appropriate time for maximum water collection.

Data Sources and References. These weather stations provide precipitation data from local climatological reports (Davis Instrument, Weatherlink Network). Boundary's data is from DENR (Department of Environment and Natural Resources), Region XI - CDD, OpenStreetMap. The boundaries for barangays and the island are as per references - they have been sourced from different authorities and digital platforms.

Study Area. The location of the study area is shown on a map in Fig. 1 using WGS84/UTM zone 31 N as a coordinate system; the Universal Transverse Mercator (UTM) and geodetic datum used are, respectively, UTM and World Geodetic System 1984. A map scale of means one unit on the map units on the ground. It indicates the kind of detail and area you want to cover. These are boundaries established by the DENR Region XI - CDD, and this area was digitized using a reference boundary from OpenStreetMap for the Sitangkai Boundary. Its nine barangays are Datu Baguinda Putih, Imam Sapie, North Larap, Panglima Alari Sitangkai Poblacion South Larap Tongosung and Sipangkot

Fig. 1. Map of the study area.

Fig. 1. Map of the study area.

Conceptual Framework. A conceptual framework for rainwater harvesting potential analysis at each step, with input-process-output Fig. 2. The steps of rainwater harvesting potential estimation consist of working out the observed precipitation data at a high density, locally relied on climatological sources, and it counts with Davis Instruments and Weatherlink Network, among others. The data are collected through local weather stations, making it accurate and relevant.

The data on regional boundaries are taken from DENR Region XI - CDD for the next step. OpenStreetMap has helped digitize this data for use as part of GIS analysis. The outcome is a digital map of the boundaries defining the object's geographical extent under discussion. All the roof areas of buildings are identified and digitized as vector polygons using GIS software within the digital boundaries. This step digitizes roof segments and makes them usable for spatial analyses. This gives a highly detailed map of vector polygons showing the exact footprints for each roof. This has to be done on top or over a grid system and the digitized roof area polygons. As a result, this process can then estimate how much rainwater could be collected depending on the size of each roof. Its results consist of a series showing the quantity of rainwater that could be collected from each respective roof
area. Correlation maps of the captured precipitation data with estimated rainwater collection volumes and maximum rainwater harvesting potential periods are determined. Analyzing these patterns allows the study to identify which months of rainwater collection would be most effective. It identifies which months to harvest the most rainwater. Lastly, the associated data is analyzed to determine when rainwater harvesting should occur for each place. This requires a step-by-step analysis of various peaks in the precipitation pattern and their corresponding alignment with volumes estimated to be collectible. The result is a list of optimal times to harvest rainwater, allowing correct utilization and maintaining water condition efficiency.

Fig. 2. Conceptual Framework.

Fig. 2. Conceptual Framework.

Data Collection. This data is from local weather stations with state-of-the-art instruments, such as those made by Davis Instrument, & connected to the Weatherlink Network. These stations monitor meteorological parameters like the amount, frequency, and intensity of rainfall. The acquired information is subsequently compiled into a comprehensive data set that accurately portrays the precipitation in any given area.

Boundary Data Acquisition. The DENR Region XI - CDD provided the boundary data of the study area. This data is essential for identifying the regional borders to confine this analysis. This data is converted to digital format using OpenStreetMap, a free and open-source software tool that accurately illustrates boundaries on maps. This step involves digitizing the spatial information from physical boundaries and creating a digital map for further GIS analysis.

Roof Area Digitization. The next thing to retrieve digitalization is the roof areas of buildings within the defined boundaries by making accurate vector polygons in the form of roofs. It is done using GIS software with precise digitization. It allows the creation of vector polygons, which are digital versions of each physical roof area. The above polygons are crucial to computing rainwater harvesting capacities due to their easy recovery and quantifiable representation of the collection area.

Overlay Analysis. The first thing to do is try doing overlay analysis with the digitized roof polygons. First, make a fishnet from the roof polygons. The grid system allows the study area to be divided into regular units, helping researchers approximate volumes of rainwater that can be collected. This step aims to get an approximate amount of rainwater that we can collect for all roof areas. Overlaying the polygons over a grid allows for computing how much rainwater can be collected from each polygon for different storm events and applied to rooftop drainage areas.

Correlation and Analysis. This step combines the measured precipitation data with an estimate of rainwater collection volumes. The other describes collected precipitation data, how rainfall varies over time, and the estimated volume of rainwater that can be harvested. Analyzing these two datasets together made it possible to determine which months of the year the rainwater application would be most effective. This makes the periods with water resource rainfall potentially most significant to a regional extent, as shown by this correlation analysis.

Optimal Period Identification. Then, choose periods of rainwater collection using the correlated data. This looks at the peak harvesting months identified and then provides time slots within these months that rainwater collection would be most beneficial. This is to design rainwater harvesting (RwH) systems centered around these times, thereby harnessing the greatest efficacy and return from water catchment. The result was a schedule for the optimal times for rainwater harvesting to help design and install an appropriate collection system.

Result and Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

Climatological Data Analysis. In June, the map depicts several pockets with "very low" to" very high precipitation impactful zones. Most of the island is in "Very Low" to "Low" precipitation ( ), particularly central portions and parts of the northwest. This persistence results in dry conditions prevailing across these areas during June. A section in the southern coastal regions shows "Moderate" rainfall ( ). Known to witness marginally higher rain, this only in some of the isolated pockets, especially around the southern part near Datu Baguinda Putih and Panglima Alari in Fig. 3; area rainfall is consistently ranked as "High" or even better depending on the categorization used, i.e., "very high"( 18.19 -associative estimate mean- ). This may include areas with a higher topographic elevation or coastal zones where winds increase precipitation.

Fig. 3. Monthly Rainfall map.

Fig. 3. Monthly Rainfall map.

The Precipitation ranges of July 2023 are categorized from very low ( ) to very high ( ). The island's northern part experiences more rainfall, especially in "Sipangkot" and the north coast of reps ( 34 km ). This indicates that in July 2023, rainfall was very high to extreme. Precipitation is moderate to high in the central regions, such as "South Larap" and "Tongsung." This might also suggest a rainfall gradient, which may be governed chiefly by topographical features from elevation changes. At the same time, "Sitangkai Poblacion" and "Imam Sapie," in the southern portion, are classified as having very low to low precipitation. The wet winds from the northeast, carried by prevailing wind currents, bring air warmed up due to its increased water content over tropical seas, resulting in higher levels of precipitation on the side accordingly exposed. July is generally the wet season for this area, which adds to its already high level of rainfall.

In August 2023, precipitation levels across the island were categorized into five distinct ranges: Very Low ( ), Low ( ), Moderate ( ), High ( 33.25 mm ), and Very High ( ). The island, characterized by its long and narrow shape, includes several named locations such as Sitangkai Poblacion, North Larap, South Larap, Tongosung, Tongmageng, Datu Baguinda Putih, Panglima Alari, and Imam Sapie. Precipitation varied significantly across the island, with the northern part near Sipangkot and the southern tip experiencing 'Very High' levels, marked by the darkest blue on precipitation maps. In contrast, the central part of the island, including areas like South Larap and Tongosung, exhibited 'Low' to 'Moderate' precipitation levels. Sitangkai Poblacion, located toward the island's southern end, was identified as being in a 'Low' precipitation zone.

From Sitangkai Island, it is expected to remain in the rainfall of September 2023. This means Very Low ( ), Low ( ), Moderate ( ), Dark blue shows the highest precipitation levels on the island, mainly in the southern part near Imam Sapie. The south part of Sitangkai Island, especially around Imam Sapie, saw the most precipitation in September 2023 - registering as "Very High" on this map. Such differences could be attributed to various factors such as geographical position, elevation, and weather development during that particular month. High precipitation was noted in North Larap, South Larap, Tongsung, Tongmageng, Datu Baguinda Putih Panglima Alari, and Sitangkai Poblacion. The island's northern half, where Sipangkot is located, has decreased rainfall.

Sitangkai Island October 2023 Precipitation Situation map The northern side of the island, topped by areas such as Sipangkot and North Larap, might have a reduced amount of rainfall (in both categories, "Very Low & Low" with 0 to nineteen. As we head on further southwards, the rainfall slowly rises. The rest of the island is under "Moderate" rainfall (19.41-29.11 mm), like in South Larap and Tongusong, both in the central part of Buayan Island. The southern part of the island, especially in Imam Sapie's neighborhood, even has a 'Very High' rainfall level ( ).

Fig. 4. Monthly Rainfall map.

Fig. 4. Monthly Rainfall map.

This Sitangkai Island rainfall map shows the wettest and driest parts of the island in November 2023. It grades precipitation into one of five categories: Very Low ; Low ( ); Moderate ( ). Some parts of the island will be wet, others dry. More rainfall has led to increased intimidation on the island. Both the far northern and extreme southern tips of Basilan, including localities categorized as "Sipangkot," "North Larap," "Imam Sapie," and both Panglima Alari districts recorded Very Low-Low to Low levels of rainfall. Very High rain has been recorded in the central region, particularly around "Tongmageng." The precipitation is divided into five ranges, from Very Low ( ) to Very High ( 38.63 mm ). These ranges show how wet or dry any place on the island is. This map shows the rainfall distribution on Sitangkai Island. General and abnormal rains will also increase in the island's northern part (near Sipangkot), falling from High to Very High. No continuous rainfall was recorded, while

South Larap - Tongmageng - Sitangkai Poblacion has received Moderate to High rainfall. However, as the data is for December (which may be associated with rain in this region), rainfall distribution could still make sense compared to other events at similar times. Precipitation range in Sitangkai Island for January 2024, with the thematic map visualized using different colors based on interval classification: Interval Class Very Low ( ); Low ( ); Moderate (7-25) ranges from blue to green. The darkest colors indicate the highest precipitation levels.

Fig. 5. Monthly Rainfall map.

Fig. 5. Monthly Rainfall map.

Now, the 2024 Sitangkai Island March precipitation ranges await the map. Different classifications of the precipitation in and more than 33.37 mm serve as accurate trackers of extreme rainfall events within their domains.

Monthly Rainfall Analysis. The graph illustrated that the June 2023 precipitation data shows a scattered rainfall pattern throughout the month. Most days, it doesn't have rain, but sometimes, the reality changes its mind. Most of the rain was heavy, which occurred on only four days: the 5th (up to a maximum rainfall in one day), the 14 th, and the 23 rd, with peaks exceeding daily values over those periods with returns up to the month as shown during violent daily events of the past two-three decades per hour. The substantial swings across these peaks and the many zero-rain days indicate a highly erratic precipitation regime. This would suggest the area had some brief, heavy showers throughout an otherwise dry period, which can be challenging regarding water-based activities that depend on regular rainfall, such as rainwater harvesting. These peaks significantly impacted the average precipitation at these higher values compared to an intervening sequence of small days. The rarity of rain events also indicates that when there was rainfall, it may not have been for prolonged periods but in short, intense bursts.

Fig. 6. Monthly precipitation trends for June.

Fig. 6. Monthly precipitation trends for June.

The other process of detecting the irregular yet heavy July 2023 rainfall is to study its precipitation data. The most significant rain event happened on the 5th: a notable 86.614 mm , enough to supplement that monthly total. Following on from June, we also had some rain in July: after a wet few days to the 10th and another downpour lasting all day long on the 14th, it rained particularly
heavily again later in the month—on both the 19th—and then typically almost seven inches of rainfall (!) fell with one helluva bang overnight at half-past-three AM (or something like that), that is caused by an approaching weather front off-again-the North Atlantic-with this sudden deluge registering officially as forty-seven-point-four-nine-eight millimeters. They come after a seemingly interminable dry spell. The high rainfall on the en rue nocturnes would indicate heavy soil loading because of long, steady rains. Such episodic and intense rainfall on an ad hoc basis can pose problems for those sectors reliant on consistent rainfall; the month-to-month irregularity of precipitation this century may cause theoretically significant fluctuations in water availability.

Fig. 7. Monthly precipitation trends for July.

Fig. 7. Monthly precipitation trends for July.

In conclusion, the rainfall pattern performance for July complements why robust water management systems should be implemented to accommodate excess when experiencing water scarcity when experiencing water scarcity when experiencing water scarcity after-Aug23 The precipitate action distribution for domestic and export products in August 2023 strikes a diverse nature as it is scattered, with some days of drying out time to periods that have rain events at varying intensities. Significantly, the maxima of rainfall on days 9, 16, and day intervals (other) could be related to episodic weather events, possibly with storm systems transiting through this region. We see the maximum single-day rainfall at 42.672 mm on the 16th, which is high but less excessive than in July. Consecutive dry days are observed in stretches of 2 to 5 days at various points in the month, mainly during its latter half, and rain sets into some regular rhythm towards subsequently trying dates. The precipitation measures are lower than the previous month, seen from light showers and moderate rain events. This pattern will likely be less impactful on flash flooding issues than we saw in the past several months. This irregular rainfall can influence urban water reserves, reinforcing the need for flexible mechanisms that support water resource management in varying weather patterns. This month's scattered rain also requires checks and balances to ensure the viability of some projects that rely on good conditions to be successful.

Fig. 8. Monthly precipitation trends for August.

Fig. 8. Monthly precipitation trends for August.

Chart Graphic showing how September 2023 had a strange pattern where almost all days in the month got no rain. The exception to this is a distinct peak in which 70.104 mm of rainfall was logged on the 3rd, a remarkably high volume likely indicative of a significant weather event (storm). The other two light showers occurred only on the 1st ( 3.56 mm ) and the 8th ( 8.89 mm ). The wet day stands apart in dryness and frequency among otherwise seasonally extremely low-average days. The
pattern indicates that the water resources depend significantly on this single event, which could impact how we store and conserve water. Dry conditions for the remainder of July could strain water supplies in communities. As this precipitation pattern holds, it serves as a reminder of the unpredictable nature of weather and why food storage is essential on both ends of the spectrum.

Fig. 9. Monthly precipitation trends for September.

Fig. 9. Monthly precipitation trends for September.

The October precipitation data for 2023 initially suggests a substantially dry period with some exceptions in store. On the 2nd, a significant rainfall occurred, with 44.490 mm , followed by another notable rain on November 01 that registered 48.514 mm . A considerable spike in rainfall suggests a particular weather event: one hardcore storm system drenched the region with an intense deluge over two days. Afterward, things were quiet, with just a trace of rain on the 5th compared to before. There is no rain in the data for the remainder of the month, pointing to a prolonged dry spell extending into February.

Fig. 10. Monthly precipitation trends for october.

Fig. 10. Monthly precipitation trends for october.

The consequences of this pattern could vary depending on an area's water needs and climate. However, the absence of significant rain after the 5th emphasizes the need for effective water-saving measures, particularly in water management. The heavy rain at the start of the month might offer temporary relief from water shortages. Still, it also comes with risks, especially if the ground is stable from an extended dry period, meaning very little may be absorbed quickly. This stark contrast between a day of heavy rain and weeks without rainfall illustrates the challenges in managing water resources as weather patterns become more unpredictable, highlighting the importance of adaptive strategies to mitigate potential impacts on natural ecosystems and human activities.

On the other hand, the moisture graph shows a series of storms combined with gaps, probably with more improved activity than in months prior. There is rain patterned throughout the month, but on the 23rd, we have a very high record of rainfall, measured to be around 45.974 mm . Rainfall for this day in history, all years agree, displays the rainfall spike at this time and again on the 1st, 13th, 21st, 27th, and 29th, indicating variability in patterns with several separated wet periods between them. Most of these are mild to moderate showers, except near-heavy rain on the 23rd. Note that the distribution of rain during the month is better, providing some balance in water availability and usage, with caution, compared to the severe dryness experienced in previous months.

Heavy rainfall, even in the presence of a wetland, could still result in flooding if its capacity is surpassed or overwhelm roads and drains that are not designed to withstand such volumes. A few isolated extreme events cannot be ruled out during November, posing a risk for comprehensive planning and implementation at strategy and crop management levels. The long-term drought begs to be quenched by the recurring rainfall. However, spans of rainy days like last month put a final exclamation point on how much climate variability should still inform our design and infrastructure.

Fig. 11. Monthly precipitation trends for November.

Fig. 11. Monthly precipitation trends for November.

Early in December 2023, the month hosted significant rainfall events alongside numerous dehydrated days. The relatively sharp peak on the 5th ( 67.818 mm ) would represent a significant rainfall event, likely during a severe storm. Hence, this day accounts for the most essential proportion of immediate effects and the month's total. On the 1st, 2nd, and then on the 4th, there was a little, but not too much, with further rain (albeit lighter) bringing rainfall again on several days, including a hefty dose of nearly half an inch which fell as heavy showers from mid-morning into early afternoon.

The pattern for December was consistent with some rainfall events. Still, these were balanced by dry days as well, which should boost the water table without causing problems associated with prolonged heavy rainfall. However, the water management systems would have been taxed, especially as the rains on the 4th ultimately led to very high rainfall rates on the 5th, a precursor to potential flash floods. The distribution could be suitable for recharging water resources, but the variability might prompt additional management strategies to cope with drought and excess rain. This month's ups and downs in rainfall intensity emphasized the need for resilient infrastructure and flexible planning to respond to climate change.

Fig. 12. Monthly precipitation trends for December.

Fig. 12. Monthly precipitation trends for December.

The January 2024 precipitation chart shows a deficient, nearly non-existent rainfall for the month. The maximum rainfall recorded is on the 6th, with 7.366 mm , far short of the significant rain events in previous months. There was a slight shower on the 4th at just 1.524 mm , and a trace amount of precipitation was measured earlier in the month at 0.254 mm . The dry spell extends through the rest of the month, which could lead to drought conditions.

This prolonged absence of rain could have various implications. If January typically contributes significantly to the region's annual rainfall, water shortages could arise due to how dry this month has been. However, if this dry period occurs during an expected dry season, its impact on nearby water
resources might be less severe. The sparse rainfall events also indicate that when rain falls, more water is needed to significantly replenish systems from previous months, suggesting a tendency toward aridity or natural variability. The lesson is evident in both cases-strong water management practices are essential, and preparation for more frequent drought-like conditions may be necessary.

Fig. 13. Monthly precipitation trends for January.

Fig. 13. Monthly precipitation trends for January.

Precipitation data for March 2024 shows the rainfall as more distributed than the dry spell seen earlier in January. Although not many, the month had some rainy days, with the most significant event on the 24th reaching 33.528 mm . This event alone contributes significantly to the month's total and likely had the most important effect on local water resources. Smaller amounts of precipitation were observed throughout the month, with a light rainfall on the 11th at 6.858 mm , followed by a moderate 16.51 mm event on the 26th, and lighter events again on the final two days, the 29th and 30th, with 7.874 mm and 4.064 mm , respectively.

Fig. 14. Monthly precipitation trends for March.

Fig. 14. Monthly precipitation trends for March.

Given the sporadic rainfall in March, we may see periods where water use is stressed between intervals of water abundance. The mid-month rainfall likely provided relief from any water deficits that had built up during the preceding and following dry days. Overall, March presented some hydrological challenges, with the prospect of ongoing dry conditions being alleviated. This mix of wet and dry spells could be exacerbated by a warming climate, leading to the need for approaches like integrated water resource management (IWRM) that promote adaptability and efficient rainwater harvesting when it does occur.

The average monthly precipitation fig. 15 for Sitangkai Island from June 2023 to March 2024 has a very pronounced seasonal characteristic, which could have significant consequences for rainwater harvesting. The peak of the rainy season in September and October reveals these as the best months to maximize water collection. With average precipitation as high as 20.7 cm , these months could fill water stores substantially if systems are adequately sized and designed for the region.

Fig. 15. Average monthly precipitation.

Fig. 15. Average monthly precipitation.

However, rainwater harvesting has its limitations. The reduction in rainfall recorded this past January makes it an unreliable water source. This variation demands significant water storage capacity to bridge the dry periods and necessitates measures to conserve water, ensuring its utility even during extended droughts.

Historical vs. June 2023-March 2024 - Rainfall Average Comparison

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

NASA's POWER CERES/MERRA2 meteorological parameter data, spanning from January 01, 1981, to December 31, 2021, with native resolution (monthly and annual mean), provides a comprehensive dataset [16], [17], [18]. The coordinates are pinpointed at a latitude of 4.6615 and a longitude of 119.392. The dataset also reports an elevation average of only 0.07 meters, indicating a flat area, possibly near sea level.

Fig. 16. Monthly Average Rainfall Historical vs. June 2023-March 2024.

Fig. 16. Monthly Average Rainfall Historical vs. June 2023-March 2024.

The model provides parameters 10 meters above ground level (AGL) and other wind and atmospheric pressure altitudes. Wind direction is measured in degrees (up to WD10M and WD50M), offering a good characterization of the leading wind trends in the area. Wind speed is reported at 10 meters (WS10M) and 50 meters, with maximum (WS10M_MAX, WS50M_MAX), minimum (WS10M_MIN, WS50M_MIN), and range values, which are essential for examining meteorological patterns and gradients over the years.

Fig. 16 plots rainfall events for each month, comparing historical averages with actual data from June 2023 to March 2024. June 2023 rainfall is close to the expected historical value, indicating stable rainwater harvesting potential for this month. The data for July 2023 shows a slight increase over historical averages, suggesting a marginally higher potential for water collection. August 2023 also aligns closely with historical averages.

September 2023 stands out with significantly higher rainfall than average, and this trend continues into October 2023. However, rainfall in November 2023 dips slightly below the historical mean. December 2023 maintains an average rainfall, while January 2024 experiences a significant drop, far below historical averages. Fortunately, March 2024 shows a recovery, with rainfall returning closer to historical norms after the dry January.

This comparison highlights the need for flexible and adaptive rainwater harvesting strategies to manage surpluses and shortages. While some months deviate significantly from historical averages, others follow expected patterns. Planning should focus on average conditions while accounting for increasing variability and potential extremes due to climate change. These anomalies should be considered for infrastructure resilience, ensuring that rainwater harvesting systems are designed to handle the unpredictability of rainfall patterns.

Roof Area Digitization. It can be seen in Fig. 17 that data for roof areas in yellow have been gathered and digitized into vector polygons. A grid is laid on top of these roof areas, and the data in each grid cell are summarized as shown in the table below. This analysis estimates the potential capacity for rainwater harvesting by considering the roof areas within each cell and making a regression across the volume that can be collected using precipitation data. The summary table shows how the total roof area and number of houses per grid cell have been computed, which is crucial for planning rainwater harvesting capacities.

It also notes substantial heterogeneity across grids in the bar graph of Fig. 5 of the average monthly rainwater harvested per grid. This is likely due to differences in roof area-more square footage would catch more rain-or variations in geography from one rainfall pattern locality to another. These differences can highlight the potential for optimization, such as improving roof designs or maintenance operations to increase rainwater capture efficiency. A more detailed analysis of seasonal trends month-by-month may also inform the time-varying capabilities of rainwater harvesting systems, supporting policy decisions on resource management and infrastructure investments. Optimizing the use of rainwater, particularly in regions where it is scarce, supports operational improvements and advances sustainability goals.

The analysis of the annual RWH volumes in Fig. 18 demonstrates significant differences between various grids. Grid XXX has the highest annual RWH, reaching liters, which is much higher than the second-ranked grid. High-potential grids ranked 2-10, show relatively high RWH performance, with volumes ranging from to 942,780 liters. Mid-potential grids, ranked 11-24, have RWH volumes between 872,240 and 300,934 liters. Finally, low-potential grids, ranked 25th to 48th, show the lowest RWH volumes, ranging from 300,437 to 4,963 liters.

Fig. 17. Digitized roof area of Grid.

Fig. 17. Digitized roof area of Grid.

Notably, Grid XXX and Grid XLI in Table 1 demonstrate the most significant difference, with over 1.74 million liters in the first case and less than 5,000 liters in the second. The grid volumes highlight that the top 10 grids account for most of the RWH, while the lower-ranked grids contribute relatively little.

Fig. 18. Average monthly rainwater harvested per grid.

Fig. 18. Average monthly rainwater harvested per grid.

The total roof area per grid analysis indicates significant variation, likely contributing to the observed differences in annual rainwater harvesting (RWH) volumes. Grid XXX, with the most extensive roof area of square meters, shows the highest potential for RWH. Other grids with large roof areas include VI ( square meters), XLVI ( square meters), and XXIX (18,594.301 square meters).

Table 1. Annual Rainwater Harvested Potential Per Grid ( 200 m by 200 m ).

Rank | Grid Identification | Annual RWH, Liters/Year | Rank | Grid Identification | Annual RWH, Liters/Year
1 | Grid XXX | 1,748,029 liters | 25 | Grid XV | 300,437 liters
2 | Grid XLVI | 1,617,698 liters | 26 | Grid XIX | 284,357 liters
3 | Grid VI | 1,553,309 liters | 27 | Grid V | 237,426 liters
4 | Grid XXIX | 1,515,807 liters | 28 | Grid XVI | 223,896 liters
5 | Grid XXXVIII | 1,237,772 liters | 29 | Grid XXIII | 208,660 liters
6 | Grid XXVII | 1,212,690 liters | 30 | Grid XVII | 157,987 liters
7 | Grid VII | 1,122,168 liters | 31 | Grid XIX | 284,357 liters
8 | Grid XL | 1,019,307 liters | 32 | Grid V | 237,426 liters
9 | Grid XVIII | 981,515 liters | 33 | Grid XVI | 223,896 liters
10 | Grid XXIV | 942,780 liters | 34 | Grid XXIII | 208,660 liters
11 | Grid XXI | 872,240 liters | 35 | Grid XVII | 157,987 liters
12 | Grid XIII | 849,836 liters | 36 | Grid XXXVII | 156,677 liters
13 | Grid XLII | 697,025 liters | 37 | Grid XXXVI | 152,123 liters
14 | Grid IX | 672,651 liters | 38 | Grid X | 144,322 liters
15 | Grid XXVIII | 668,742 liters | 39 | Grid II | 126,677 liters
16 | Grid XXXII | 624,936 liters | 40 | Grid XXXIII | 123,648 liters
17 | Grid XXII | 606,452 liters | 41 | Grid XLVII | 76,687 liters
18 | Grid III | 597,036 liters | 42 | Grid XX | 65,285 liters
19 | Grid XXXIX | 532,196 liters | 43 | Grid XII | 33,789 liters
20 | Grid XIV | 505,177 liters | 44 | Grid XXXI | 33,328 liters
21 | Grid XLIII | 456,839 liters | 45 | Grid IV | 19,062 liters
22 | Grid XXXIV | 449,774 liters | 46 | Grid XLIV - | 16,875 liters
23 | Grid XXVI | 347,239 liters | 47 | Grid I | 7,036 liters
24 | Grid VIII | 300,934 liters | 48 | Grid XLI | 4,963 liters

Conversely, table 2 Grid I features a small roof area of 86.311 square meters, while XLIV (207.007 square meters) and XLI (60.876 square meters) have only slightly more significant areas.

These differences in roof areas account for the significant variations in RWH volumes. The bestperforming grids in RWH have significantly higher roof areas, highlighting the importance of roof size in successful rainwater harvesting.

Table 2. Total Roof Area Per Grid.

Grid Name | Order | Total Roof Area (Square meter) | Grid Name | Order | Total Roof Area (Square meter)
I | 1 | 86.311 | XXV | 25 | 999.784
II | 2 | 1553.94 | XXVI | 26 | 4259.552
III | 3 | 7323.803 | XXVII | 27 | 14875.986
IV | 4 | 233.828 | XXVIII | 28 | 8203.405
V | 5 | 2912.488 | XXIX | 29 | 18594.301
VI | 6 | 19054.328 | XXX | 30 | 21442.944
VII | 7 | 17256.509 | XXXI | 31 | 408.834
VIII | 8 | 4027.986 | XXXII | 32 | 7666.041
IX | 9 | 8251.357 | XXXIII | 33 | 1516.786
X | 10 | 1770.39 | XXXIV | 34 | 5517.346
XI | 11 | 1507.687 | XXXV | 35 | 1348.434
XII | 12 | 414.483 | XXXVI | 36 | 1866.08
XIII | 13 | 10424.876 | XXXVII | 37 | 1921.951
XIV | 14 | 6196.972 | XXXVIII | 38 | 15183.664
XV | 15 | 3685.443 | XXXIX | 39 | 6528.41
XVI | 16 | 2746.511 | XL | 40 | 12503.767
XVII | 17 | 1938.014 | XLI | 41 | 60.876
XVIII | 18 | 12040.176 | XLII | 42 | 8550.355
XIX | 19 | 3499.029 | XLIII | 43 | 5604.011
XX | 20 | 800.842 | XLIV | 44 | 207.007
XXI | 21 | 10699.706 | XLV | 45 | 1553.465
XXII | 22 | 7439.304 | XLVI | 46 | 19844.188
XXIII | 23 | 2559.612 | XLVII | 47 | 940.711
XXIV | 24 | 11565.019 | XLVIII | 48 | 1135.758

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

This study emphasizes the potential of rainwater harvesting as a highly effective practice for increasing water availability, particularly for Sitangkai Island. Based on a vector grid approach in QGIS, the analysis evaluates the spatial potential for rainwater harvesting systems across nine barangays. The study suggests that potential water yield varies substantially due to differences in roof space and precipitation patterns across different geographic regions.

Future Works

The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

In the follow-up, design optimization for roof systems will be essential to enhance rain capture on a building scale. This requires assessing harvested rainwater's monthly demand and sufficiency to ensure it meets the community's needs. Additionally, investigating the possible development of a basic rainwater collection storage system within a grid rooftop is essential. Creating a viable storage system capable of housing the water collected from each optimized roof sector is also crucial. These research directions will contribute to a better understanding and application of rainwater harvesting as an alternative, sustainable solution for freshwater scarcity.

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The original version of this paper is available on https://www.scientific.net/EI.13.57.pdf

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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-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 75-81

doi: 10.4028/p-iYFS8v

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-11-03

Revised:
2024-06-13

Accepted:
2024-08-19

Online:
2025-02-06

Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

Zara Elena Sharon Lah and Nurmin Bolong

¹Rural Water Supply Department, Limbang Division, 98850 Lawas, Sarawak, Malaysia

Faculty of Engineering, University of Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia

sharonyon@gmail.com, b*nurmin@ums.edu.my

Keywords: Water Demand, Residual Pressure, Pressure Gauge, Ultrasonic Meter, Electromagnetic Meter

Abstract

The Lawas community in Sarawak, especially in the town area, heavily depends on treated water from the Trusan Regional Water Treatment Plant (WTP) for daily water needs. However, the hilly and remote areas face several challenges, such as deteriorated water distribution, inadequate supply, and low water pressure. These issues are worsened by the increasing number of new water meter connections each year and the difficulties in supplying water to remote areas. As a result, there have been more complaints to water authorities from the community due to the impact on their essential survival needs. This study aims to comprehensively investigate, analyse, and assess water demand and residual water pressure to determine if the current water supply is sufficient for the population in Lawas. By utilising population data from the 2022 Lawas District Office and recent water pressure measurements obtained using a Pressure Gauge and Prime Log, along with water flow data collected using a Portable Ultrasonic Flow Meter (PT900), Electromagnetic Flow Meter, and Insertion Flow Meter, this research identifies the factors contributing to the inadequacy of water demand. The recommended solutions optimise water pressure and flow to ensure reliable data for future operations and maintenance. These findings provide a reference for designing an efficient water distribution system and promoting optimised water usage tailored to specific areas based on their needs.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

Water distribution networks (WDN) are vital hydraulic infrastructures for providing consumers with sufficient water of appropriate quality [1]. WDN transmits water to consumers after physical and chemical treatment, an essential part of water supply systems [2]. This water grid system refers to the part of the water supply network that distributes portable water from a centralized treatment plant or a storage system for residential, commercial, industrial, and firefighting purposes [3]. Water distribution systems (WDS) connect consumers to water sources using hydraulic components such as pipes, valves, and reservoirs. Choosing operational components that can provide adequate pressure is essential when meeting demands. [4]. A change in one path affects the flow of other paths in the network [5]. A portable water hydraulic analysis constrains pipe diameter and nodal pressure [6]. The transmission of water from a WTP transmits treated water from the WTP which directly pumps into the Ground Tank Reservoir capacity of 7 million Liter located at 113 m height from sea level. Treated water is distributed by gravity from the reservoir toward the Lawas.
The water agency in Lawas is the Rural Water Supply Department Limbang Division also known as Jabatan Bekalan Air Luar Bandar (JBALB) Bahagian Limbang [7]. As of May 2023, the data from the JBALB Water Billing System, reveals 6864 active water meters for consumers in the Lawas Community. However, only of these active meters are connected to the treated water, serving approximately 24,560 consumers. The rapid population growth in Lawas, accompanied by the expansion of new developments and houses, has affected the water supply for the community. This deficiency mainly affects the far-end areas of the gridline and poses a significant challenge for residents residing in hilly regions. Immediate action is required to address these concerns and minimize the affected areas.

Residents in hilly regions rely on alternative sources such as gravity-fed systems and rainwater harvesting for their clean water needs. However, during the dry season, these alternative sources cannot meet the water demands, resulting in severe difficulties for the villagers in their daily activities. To address this issue, the water agency has resorted to supplying treated water through tanker trucks. Moreover, this reliance on tanker deliveries has increased transportation costs, adding a financial burden to the annual Operation and Maintenance budget. It is anticipated that this study holds great significance as it aims to assess the adequacy of water demand fulfillment from the Trusan Regional WTP supply grid. The treated water is pumped to a reservoir at a higher altitude that provides water to the entire population. However, an elevated tank or reservoir is constructed if this is not feasible. The pumping mechanism fills the tank in such systems, and gravity regulates the water supply [8]. The reservoir height is crucial in generating water head pressure, enabling water to flow by gravity. The reservoir outlet releases water with significant pressure, necessitating the selection of an appropriate pipe size capable of handling high pressure.
The water pressure is also known as a force that makes a water flow strong or weak. Water pressure is the force that pushes water through the pipes and is typically created by the altitude and elevation measured in pounds per square inch (psi) and is essentially the force at which the water exits the faucets [9]. Higher range water pressure could damage the plumbing system. Meanwhile, low water pressure leads to several house cores being disrupted. The longer the distance of the conveyance of fluids, the less the water pressure due to water loss occurrence.

1. Methodology

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

1.1. Background analysis of the case study area

This paper focused on analyzing the sufficiency of water demand from the Trusan Regional WTP water supply system. Water demand data could be tracked from the data given by the Lawas District Department. The primary occupations of the Lawas population revolve around agriculture, livestock rearing, and river fishing. The Malays and Kedayan communities residing along the river primarily engage in agricultural and fishing activities. In contrast, the Lun Bawang indigenous people residing in the upstream areas are predominantly involved in agriculture [10]. Rapid population growth in Lawas, it is anticipated that the population will continue to rise in the coming decade [11].
The population of Lawas has been steadily increasing over the years, with a few fluctuations. From the department of statistic Malaysia [12]; in 1970, the population was 16,979; by 1980, it had increased to 19,777 , with an average annual growth rate of . The population grew, reaching 29,276 in 1991 and 32,726 in 2000, with average annual growth rates of and , respectively. By 2010, the population had reached 37,212, with an average annual growth rate of . However, in 2020, the population slightly decreased to 36,604 , with an average annual growth rate of . Despite the slight decrease in 2020, this steady growth trend highlights the need to address the water supply needs in Lawas. Whereby according to official records from the Lawas District Department dated 19th November 2020, the population in 2020 was reported to be 42,010 , indicating a increase over the past decade since 2010. The projected population in Lawas for the next ten years is estimated to be approximately 42,619, reflecting a 1.45% increase from 2020.
Given the projected population growth, the current water supply capacity needs to be increased. The data indicates a need for a water supply of about 53 MLD . This underscores the immediate need to enhance the current water capacity to meet future demands.

1.2. Zero Pressure Test

Zero Pressure Test (ZPT) is a conventional way to check on the source of the water supply in the areas. It is a methodology to identify if the zone or area is completely tight or if there are no water escapes or feeding through any boundary valve. The supply valves are closed for a period and the pressure is monitored. A sudden drop to zero pressure means the boundaries of that area are watertight. The first step of the ZPT is to pre-configure the pressure logger to record each 1-minute interval inside and outside of the zone. The ZPT conducted during the night at 11 pm started and
ended at 2 am is an ideal time to close the supply valve at that sub-main area for checking purposes. For example, a few kampungs at Sundar junction were tested and the graph profile from the pressure logger was used to identify which area is inside the coverage supply area from the inlet valve. The same method was carried out for each of the zones for reference and record.

1.3. Water Pressure Gauge and Prime Log

A gauge pressure logger is a measurement instrument that indicates the water pressure in a system. It is a calibrated pressure indicator that is connected to a water pipe or tank and converts the force exerted on it into units such as Pascal, bar, psi, and Torr. Water pressure gauges provide real-time pressure measurements in domestic, commercial, and industrial applications. They are easy to use, durable, and require little maintenance. A system's water pressure can be static or dynamic. Static pressure is the pressure of water when it is not moving. Conversely, dynamic pressure is the pressure of flowing water. Static water pressure can help ensure that the plumbing system's design can handle the pressure of supply water. If the pressure is too high, it can cause damage to fixtures and pipes. Otherwise, if the pressure is too low, getting enough water flow to certain areas of the system can be challenging. Dynamic water pressure determines the flow rate through the system. Dynamic water pressure can ensure that the plumbing system can handle the anticipated demand for water in the home or building. [13].
The model analysis in this study is based on static hydrostatic pressure based on each zone's water demand and compared with the actual reading of water pressure at each zone junction and the residual pressure at the end of the pipelines. To control water pressure, a Pressure Release Valve (PRV) were installed at the designated zones with recommended supply pressure within 2.5 bar to 3 bars. The other basic way to avoid further damage to fixtures and pipes is for the workers need gently turn off or on the Sluice Valve (SV) in situations where the shutdown of the water supply for repairs works to avoid water hammer occurring in the pipe. The installation of Air Valves (AV) is important to release air traps in the pipes after SV are closed.

1.4. Ultrasonic Water Flow Meter PT900 (UFM)

The pipeline is segmented by the installation of a clamp-on UFM across the targeted area. Each flow meter measures the flow rate of the commodity at its location. This transPort PT900 measures the velocity, volume, mass, totalizer, and energy flow rate. It is suitable for most pipe sizes and materials, both lined and unlined, and suitable for many fluids including portable water. Firstly, this Ultrasonic Water Flow Meter PT900 installs the clamping fixture with minimal confusion and repositioning. After that, connect the transmitter and transducer cables correctly, turn on the tablet, and pair it via Bluetooth with the transmitter. The appearance reading is recorded. The PT900 apps can be installed from the Play Store apps and paired with a mobile phone.

1.5. Electromagnetic Flow Meter (EFM)

Electromagnetic Flow Meter (EFM) has been researched and the subject of analysis for over 180 years with indisputable advantages earned on the flow measurement, especially with regards to measurements taken in pipelines [14]. The velocity profile from measured potential differences can be measured within the electromagnetic flow meter [15]. EFM requires the installation of the short pieces upstream (minimum pipe diameter) and downstream (minimum pipe diameter) to ensure constant flow record by the logger and to avoid turbulence of water inside the pipe. The installation of EFM was chosen because of its high-value, precision solution for remote water metering applications. It also has a built-in multi-sampling rate, pressure, and flow data logger. Data could be accessed and all logged data remotely via wireless communication via a built-in Global-System-Mobile-Communication (GSM) modem. This could reduce the requirement for costly and time-consuming site visits.

1.6. Insertion Flow Meter (IFM)

The Insertion Flow Meter (IFM) is one of the additional equipment that has been installed on the ground for measuring water flow in a pipe. IFM is installed together with a Gauging Rod, a tool to
measure internal pipe diameter at 700 mm Nominal-Diameter of Mild Steel Concrete Lining Pipe of distribution main pipe from Trusan. To produce ideal, fully developed flow requires either a minimum of 100D of straight pipe or the use of a flow conditioner, therefore, in many situations, particularly if the pipeline has a larger diameter or space is limited, insertions flowmeters are installed in non-ideal flow conditions. In a fully developed flow, the velocity at in the center of the pipe is equal to the maximum velocity [16].

2. Results

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

2.1. Residual Pressure

The data of residual pressure from the initial point and endpoint at each network zone (refer Fig. 1). The data at the initial water pressure point was manually recorded using a pressure gauge and UFM. From the graph, most of the network zones received enough residual pressure at the initial point. However, at the endpoint, most of the residual pressure was recorded below 10 m , below the minimum requirements, except for the zone of Jalan Trusan, which is slightly above the active meter demand. Minimum requirements related to the essential minimum pressure requirements for some home appliances, such as washing machines, require 10 m and above water pressure. Residual pressure of less than 10 m could be considered insufficient because of appliance requirements in residential areas [17]. Hence, the following criteria were set up under the Guidelines and Requirements of the Sarawak State Water Authority. The baseline water pressure was set at 10 m for each zone highlighted in red on the graph.

Fig. 1. Initial vs. Residual Pressure

Fig. 1. Initial vs. Residual Pressure

2.2. Water Demand

After analyzing the demand, it has been determined that the actual demand profile of each zone (refer with: Fig. 2), indicates that the starting point of water flow at each main junction has sufficient flow to cater to the demand of all zones. Unfortunately, the endpoint of all five zones found inadequate water flow. These results reflect the actual situation that is currently happening in the Lawas community.

Fig. 2. Population Demand correlation to the Active Meter Demand

Fig. 2. Population Demand correlation to the Active Meter Demand

3. Discussion

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

In 2016, studies recommended establishing dedicated District Metering Areas (DMA) for AwatAwat. By establishing the DMA, proper monitoring, and improvements in pressure and supply issues of the Awat-Awat area can be done. Another DMA was needed because the current DMAs cover a large area and are difficult to maintain and monitor [18]. In the current study, the mentioned DMAs are currently established and are still under installation stages. Establishing a dedicated District Metered Area (DMA) not only for the Sundar and Awat-Awat areas but for all zones can help compile data. Additionally, installing Prime log and i2O loggers at the DMAs and IFM can provide further insights on the real-time data monitoring.
Based on the results, the water pressure is below 10 m in all areas except Jalan Trusan, indicating low pressure. This could lead to water shortages and affect daily laundry and toilet use in hilly areas. The main factor affecting pressure is the elevation of the ground. While the supply from Trusan Regional WTP should be enough, the study shows that end flow demand in some areas needs improvement. Lawas town has high demand due to its proximity to essential establishments. Addressing factors like pipe leaks, illegal tapping, and rapid development affecting water flow is crucial. To improve water flow, consider increasing the sub-main pipe diameter to reduce friction, installing pumps to boost flow during low pressure, and using pressure break tanks in hilly areas.

4. Conclusion

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

In conclusion, this study has investigated the water supply challenges faced by the Lawas community, particularly in hilly and remote areas. By analysing water demand and residual water pressure, the study has revealed significant issues with water distribution, inadequate supply, and low pressure. These issues are further exacerbated by increasing consumer demand and the complexity of supplying remote areas. The study, by utilising population data and advanced measurement tools, has identified key factors contributing to the insufficiency of the current water supply, underlining the critical need for immediate action. The recommended temporary solution for areas with water supply issues is to use individual water pumps and provide a one-day storage tank to meet the total water demand at the end of the water supply grid. This approach could reduce reliance on the water tank and decrease operational costs. To minimize pressure losses, installing an Air Valve (AV) at the highest elevation is recommended to release airlocks. It is also suggested that additional booster pumps be constructed to increase water flow. An analysis of ground elevation will help determine the location and design
of the pump head. Calculating the height difference between the initial point and the highest elevation is essential to ensure the pump head design can elevate the water past the highest height. The pump location could be positioned before the highest elevation to boost the water pass-through. In addition, a new water treatment plant (WTP) is recommended at the endpoint of Lawas Town, especially in Pengalih, to meet the surrounding demand. The water authority has proposed rehabilitating the old Lawas town storage tank as a secondary storage tank for the town area. These suggestions are highly recommended to address the water supply issues in the Lawas community. A detailed study of the overall system, incorporating detailed hydraulic calculations of the new booster pump head and appropriate location, is recommended. These factors should be considered for the future water grid system design. Specific locations require a booster pump station to enhance the pressure for continuous and stable water pressure. Secondary or high-level storage tanks are also necessary to create water head pressure.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

The authors would like to acknowledge the support from the Ministry of Higher Education, Universiti Malaysia Sabah (UMS), and JBALB Limbang Division Sarawak. In addition, a thousand thanks to the family members, friends, staff of EXXOR Technologies Sdn Bhd, the JBALB Limbang Division PIPE fitters, and the trainees for their assistance in this research. We also wholeheartedly thank the reviewer's input and helpful suggestions for the paper's improvement.

References

The original version of this paper is available on https://www.scientific.net/EI.13.75.pdf

Uchit Sangroula, K.-H. Han, K.-M. Koo, K. Gnawali, K.-T. Yum, "Optimization of Water Distribution Networks Using Genetic Algorithm Based SOP-WDN Program" Water 2022, 14, 851

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Nibi Kulangara Velayudhan, Preeja Pradeep, Sethuraman N Rao, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh, "IoT-enabled Water Distribution Systems - A Comparative Technological Review", DOI 10.1109/ACCESS.2022.3208142

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Work Progress - October 2021, Program Non-Revenue Water (NRW) Nasional Pendekatan 1 Negeri Sarawak - Established of GIS, Pipe Digitalization Works - Limbang Division

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Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 83-96

doi: 10.4028/p-SWu9gE

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2023-10-02

Revised:
2024-10-10

Accepted:
2024-10-27

Online:
2025-02-06

Bacterial Strain Identification from Drinking Water in Water Treatment Systems

The original version of this paper is available on https://www.scientific.net/EI.13.83.pdf

Mustapha Salisu Muhammad , Mohd Hafiz Dzarfan Othman , Mohd Hafiz Puteh , Nik Ahmad Nizam Nik Malek , Abdul Razis Saidin , Abdulhalim bin Mohd Yusof , Roziana Kamaludin , Siti Maryam , Ojo Samuel , Liew C.M. , Parvin A.P. , Nurul Huda

Department of Biosciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

Department of Biological Science (SOSE), Federal College of Education (Tech), Bichi, P.M.B 3473, Bichi, Kano State, Nigeria

mustysulu@gmail.com, *bhafiz@petroleum.utm.my, mhafizputeh@utm.my, niknizam@utm.my, razis@utm.my, halimy.utm@gmail.com, @gmail.com, maryamjasman@gmail.com, femioctober22@gmail.com, c.ming@graduate.utm.my,

Keywords: Bacterial isolation, Bacterial strain, Biofouling, Drinking water, Water treatment systems.

Abstract

The availability of clean and safe water for drinking is essential for human life and existence, which ideally should be suitable for consumption and not contain pathogenic microorganisms, or any contamination leading to pollution. Water treatment systems are integral to modern water purification processes, yet they are frequently challenged by biofouling. Biofouling continues to be a major obstacle in water treatment systems, resulting in decreased efficiency, higher energy usage, and increased operational expenses. Therefore, this study aimed to determine the bacteriological characteristics of drinking water by isolating and identifying bacterial strains from water samples contributing to biofouling. Samples were obtained from water treatment systems (WTS) at different locations in Malaysia. Selected isolates of unique bacterial strains were identified and assigned their accession numbers. Phylogenetic analysis revealed that these isolates were related to Bacillus cereus, Stenotrophomonas maltophilia, and Stenotrophomonas pavanii species, suggesting that deterioration in water quality from the source, human error, and technical failure may cause decline even if the most desirable treatment systems and disinfection procedures applied.

Introduction

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Bacterial contamination of water and food is the most pressing issue that leads to infection and diseases in humans. The worsening of water quality generally involves microbial risks, as most recorded health problems in relation to water results from microbial contamination [1, 2]. Nowadays, outbreaks of waterborne diseases have become a serious issue, despite worldwide efforts and newer technologies being used for the production of clean and safe drinking water [3,4]. The availability of safe drinking water constitutes a global challenge with an effort summoned by the authorities for water quantity assurance and quality [5]. Water is a highly important resource necessary for life sustainment, and safe water for drinking is a basic need for human life [6-8].

In addition, drinking water has been a course of antibiotic-resistant organisms spread out among human and animal populations, with proliferation of resistance genes in natural bacterial ecosystems [9-11]. WTSs have transformed the water purification process with effective and affordable techniques for removing contaminants, including desalination, wastewater treatment, and
portable water production. These processes encompass reverse osmosis (RO), ultrafiltration (UF), nanofiltration (NF), microfiltration (MF) and membrane distillation (MD) which are extensively implemented across various industries for water purification [12]. Despite their versatility, microorganisms predominantly bacteria have a tendency to channel on the filter surface, and other parts by producing extracellular polymeric substances (EPS) forming biofilm. Biofouling leads to increased energy consumption, frequent filter cleaning, reduced filter lifespan, and compromised water quality. Nevertheless, water quality from the WTS may quickly change due to day to day activities within the surrounding arena of the WTS [13,14]. The filters in WTS can be accumulated by Coliform bacteria, yielding massive concentrations of microorganism in the whole water [15, 16]. Over time, the build-up of biofilm on the filter may lead to permanent harm, requiring regular cleaning and replacement.

However, according to [17], some heterotrophic bacteria can stick fast to the surface of WTS such as buttons, spouts to form biofilms, reducing the filter life span and compromised water quality. The water treatment machine can be contaminated with heterotrophic bacteria from its inner surface or spout dispenser [15]. In light of these repercussions, it is imperative to ascertain and delineate the bacterial strains implicated in biofouling in order to formulate more efficacious control strategies. For this study, the quality of drinking water from WTS was examined through bacteriological analysis in the vicinity of Malaysia. This is aimed to provide a comprehensive assessment of the cleanliness and water quality of these WTS. Moreover, the discovery of this research can be of great importance to public health and humanity.

Experimental

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Sampling

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The samples were collected from five WTS in different locations of Skudai vicinity sits at the southern tip of the Malay Peninsula. Subsequently, from each WTS1, WTS2, WTS3, WTS4, WTS5 water samples were collected from the spout inside a sterile schott bottle and kept inside a box containing ice to optimize the temperature. The collected samples were, immediately transported to the laboratory for microbial analysis. The temperature of the samples was maintained at . The samples were stored in an incubator at an optimal temperature of prior to use.

Fig. 1. Geographical location of collected sample.

Fig. 1. Geographical location of collected sample.

Bacterial Isolation

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Samples from drain spouts were used as the culture source. Then, 0.1 mL of each sample was added to a culture plate containing nutrient agar for inoculation of bacterial isolates over the solid agar surface. Inoculation was performed inside a laminar flow to prevent contamination. The samples were spread on an agar plate under aseptic conditions using a spreader. The plates were sealed and incubated overnight at for bacterial growth. After overnight culture, colonies were
observed, and prominent single colonies were inoculated on a new agar plate by back and forth streaking using a sterile inoculation loop. The plates were incubated overnight at . The bacterial isolates were subculture until a single pure isolate was obtained on an agar plate.

Identification of Isolated Bacteria

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The isolated bacterial cultures were identified by morphological observation, Gram staining, and 16 S rRNA GENE analysis. Phylogenetic and molecular evolutionary analysis were conducted using MEGA version 6 [18]. Analysis was conducted to study the relationship between the isolate sequence and other known sequences based on their 16S rRNA partial sequences. The 16S rRNA gene sequence was aligned using ClustalW with other sequences from the BLAST results and one outgroup. An isolated bacterial phylogenetic tree was constructed using the neighbor-joining method.

16S rRNA Gene Analysis

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The culture was prepared in a Falcon tube and incubated at overnight with shaking at 200 rpm to obtain pure and physiologically stable cells before DNA extraction. Genomic DNA (gDNA) was extracted from bacteria, followed by amplification of the 16S rRNA gene using the polymerase chain reaction protocol with designated primers to produce large amounts of genes. Agarose gel electrophoresis was performed to analyze the outcomes of both DNA extraction and gene amplification for quality and quantity of the gDNA, which was assessed spectrophotometrically using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, USA).

Table 1. DNA concentrations and purity of extracted DNA from 6 selected isolates.

Sample ID | Nucleic Acid Concentration | 260/280 | 260/230 | Sample type
S1 | 159.5 ng/μl | 1.94 | 1.94 | DNA
S2 | 274.6 ng/μl | 2.12 | 1.93 | DNA
S5 | 278.5 ng/μl | 2.01 | 1.69 | DNA
S7 | 329.8 ng/μl | 2.00 | 1.71 | DNA
S9 | 273.4 ng/μl | 1.98 | 1.66 | DNA
S10 | 563.8 ng/μl | 1.87 | 1.51 | DNA

Results and Discussion

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Based on their individual morphology (Table 2), the isolates were successfully cultivated from the drinking water samples, with six out of the ten being selected for further study. Also, from the Gram staining results, the isolates were identified as Gram-positive and Gram-negative bacteria with rod shape appearance from all strains alongside their morphological identification. From Table 1, for an optimum PCR amplification, the acceptable DNA or relative purity at 260/280 ratio per definition should fall within the limit of . Thus, these values are considered efficient and effective for downstream nucleic acid amplification methods such as PCR, real-time PCR, and DNA sequencing because they fall within the specified limit. The purity of extracted DNA is critical because of its influence on the success of the amplification outcome [21].

Table 1. Morphological characteristics of the bacteria isolated from WVMs.

Sample label
Bacteria Isolate
Morphology
Staining

Positive
Negative

S1
Bacteria isolate S1
Shape : Circular
Size : Small
Color : Beige
Opacity : Opaque
Elevation: Convex
Margin : Entire
Surface : Smooth
Appearance: Glister

S2
Bacteria isolate S2
Shape : A.Circular
Size : Moderate
Color : Cream
Opacity : Opaque
Elevation: Flat
Margin : Undulate
Surface : Concentric
Appearance: Dull

S5
Bacteria isolate S5
Shape : Circular
Size : Small
Color : Cream
Opacity : Opaque
Elevation : Convex
Margin : Entire
Surface : Smooth
Appearance: Glister

S7
Bacteria isolate S7
Shape : Irregular
Size : Large
Color : Cream
Opacity :Transluscent
Elevation : Flat
Margin : Undulate
Surface : Concentric
Appearance: Dull

S9
Bacteria isolate S9
Shape : Circular
Size : Punctiform
Color : Yellow
Opacity: Transluscent
Elevation : Convex
Margin : Entire
Surface : Smooth
Appearance:Glister

S10
Bacteria isolate S10
Shape : Circular
Size : Moderate
Color : Yellow
Opacity : Opaque
Elevation: Raised
Margin : Entire
Surface : Smooth
Appearance:Glister

The amplified PCR products were initially confirmed visually by producing light DNA fragment bands of approximately 1500 bp compared with the standard DNA ladder shown in figure 2. The appearance of a light DNA band of approximately 1500 bp on the gel indicated that amplification of the target gene was achieved. In accordance with [19, 22], 16S rRNA gene should have an approximate length of 1500 bp. By amplifying a single copy of DNA, PCR will generate
thousands to millions of copies of DNA. This is an excellent method for detecting nucleic acids, disease identification, eukaryotic species, human identification, forensic science, and pathogens identification [23].

Fig. 2. Gel electrophoresis showing successful PCR amplification of isolated bacteria unknown to drinking water. Lane 1 represents the DNA ladder. Other lanes represent amplified

Fig. 2. Gel electrophoresis showing successful PCR amplification of isolated bacteria unknown to drinking water. Lane 1 represents the DNA ladder. Other lanes represent amplified

16 S rRNA genes of the isolates from water.

After compiling and performing a comparative analysis of the conserved regions of the 16 S rRNA from the isolates against the GenBank database. The results from BLAST showed that out of the isolates, S2, and S7, are probably belong to Bacillus genus, S5, S9, S10, and S1 under the genus Stenotrophomonas. The 16 S rRNA partial gene sequences of the six species from two genera were then sent for the accession number, as shown in Table 3. However, two of the strains identified from the S1 and S2 isolates had no assigned accession numbers, resulting from low similarity to other 16 S ribosomal RNA sequences in the database. In line with [24, 25], a sequence chromatogram always has some noise, such as low-intensity signals arising from baseline or from baseline of nonspecific primer binding. This can reduce the quality and success of the subsequent analyses. Therefore, strains with accession numbers were subjected to phylogenetic analysis.

Table 3. Accession numbers for the isolated strains.

Isolates | Querry length | Identity | Accession No
S1 | 1314 | Stenotrophomonas sp. strain S1 | Not assigned
S2 | 1451 | Bacillus sp. strain S2 | Not assigned
S5 | 1454 | Stenotrophomonas sp. strain S5 | MG563676
S7 | 1499 | Bacillus sp strain S7 | MG563677
S9 | 1538 | Stenotrophomonas sp strain S9 | MG563678
S10 | 1446 | Stenotrophomonas sp strain S10 | MG563679

The phylogenetic affiliation and, comparative 16 S rRNA gene sequences of 15 consecutive closely related organisms obtained from the GenBank database for each of the four isolated bacterial strains were aligned individually. The bootstrap values are given at the branching points by employing the bootstrap method for the confidence levels of the phylogeny. This was performed to evaluate the tree confidence estimate for branch support. Indications of values above in the phylogenetic tree indicate the reliability of the tree. This is in accordance with a report (inferring evolutionary trees with strong combinatorial evidence) stating that a bootstrap value of less than in a phylogenetic tree has no confidence level and can be considered as unreliable [26-28]. Fujiensis was used as an outgroup species to root a phylogenetic tree. The involvement of outgroups in the phylogenetic tree construction plays a significant role in distinguishing between the two different genera. The bacterial strain S5 was phylogenetically affiliated with the same branch and formed a clade with Stenotrophomonas maltophila ( similarity), strain S7, and Bacillus cereus ( similarity), S9 to Stenotrophomonas pavanii ( similarity), and S10 to Stenotrophomonas maltophilia ( similarity), with bootstrap values above , providing strong support for the close relationship between the two strains. The bootstrap test assesses the internal consistency of a molecular dataset by determining whether slightly altered alignments support the same phylogenetic clades [29].

In compliance with the comparative analysis of the conserved region of the 16S rRNA gene sequence of all the strains against the GenBank database, the results revealed that the bacterial strains have a relatively good similarity of more than with the known species. This is in line with the report of [30-33] stating that an identity of more than DNA-DNA similarity can be defined as genospecies according to bacterial taxonomists. In addition, it was clarified that strains with less than similarity can be regarded as novel species [34]. Therefore, based on our findings, strains S1 ( ), S2, and S5 ( ) could be considered as separate species that may be novel. Then, according to [35-37], to define a bacterial species, a threshold range of is generally used. Thus, strains S7, S9, and S10 exhibited , and gene similarity, respectively, according to the BLAST results. These similarities were shared with strains ATCC 14579, LMG 25348, and ATCC 19861 of B. cereus, S. pavanii, and S. maltophilia.

Fig. 3. Phylogenetic tree highlighting the position of Bacillus sp. strain S 7 and its close relatives based on the 16S rRNA gene sequence.

Fig. 3. Phylogenetic tree highlighting the position of Bacillus sp. strain S 7 and its close relatives based on the 16S rRNA gene sequence.

The bacterial genus Bacillus encompasses different species that are widely distributed in nature and in soil, water and air, proliferating between and . Bacillus genus is composed of aerobic or facultatively anaerobic Gram-positive bacilli that create spores, are typically catalase positive, and display motility [38]. The species B. cereus inhabit numerous environments including fresh and marine waters, vegetables and fomites, the intestinal tract of invertebrates, and decaying organic matter [39-42]. Owing to the widespread distribution of this bacterium spore in the environment, especially in soils and dust, they can be easily spread out into water and food [39, 42-44]. This might be a major source of contamination in the water treatment system, which can interrupt the microbial quality of drinking water. The presence of this substance in potable water may present significant health hazards due to its capability to induce foodborne illnesses and various other infections. Although food poisoning is typically the main concern with Bacillus cereus, it can also result in more serious infections, particularly in people with weakened immune systems [42]. In a finding by [45] indicated that B. cereus is commonly found in farming areas, where it can percolate into the groundwater during rainfall, suggesting that this vital source of drinking water could also act as a reservoir for Bacillus cereus. Bacillus cereus spores are strong and can withstand treatment methods such as filtration and chlorination, which are frequently employed to clean drinking water [46]. This remarkable durability facilitates their continued presence in drinking water, thereby posing a risk of contamination. Previous reports have indicated that B . cereus is capable of producing biofilms [47-49]. Biofilms, intricate bacterial communities that stick to surfaces and are surrounded by a protective extracellular matrix, have the ability to reintroduce bacteria back into the water treatment system. For instance, Bacillus spp. were found in different water sources, including two drinking water sources, with different levels of spores detected, as demonstrated in a study [50]. Similarly, report from South Africa highlights the
presence of Bacillus strains in drinking water, attributed to human activities [51]. From the short review above, key findings emerge on the isolated sample S7 strain 7 is likely a species of B. cereus present in the WTS samples. This is important finding in the understanding of water quality from treatment systems. Therefore, effective monitoring and risk assessment strategies are necessary to prevent potential health risks associated with the consumption of Bacillus cereus in drinking water [52]

Fig. 4. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S9 and its close relatives based on the 16S rRNA gene sequence.

Fig. 4. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S9 and its close relatives based on the 16S rRNA gene sequence.

Fig. 5. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S5 and its close relative based on the 16 S rRNA gene sequence.

Fig. 5. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S5 and its close relative based on the 16 S rRNA gene sequence.

Fig. 6. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S10 and its close relatives based on the 16S rRNA gene sequence.

Fig. 6. Phylogenetic tree highlighting the position of Stenotrophomonas sp. strain S10 and its close relatives based on the 16S rRNA gene sequence.

Stenotrophomonas pavanii is a type of bacteria that is rod-shaped, non-spore former and belongs to the Stenotrophomonas genus. Initially, it was discovered in the stalk of sugar cane in Brazil and is known for its ability to fix nitrogen [53]. This organism can form biofilms in water distribution systems, making disinfection efforts more difficult and enabling the survival of bacteria that would likely be eliminated in treatment processes [54]. The risk of drinking water safety is elevated by the ability of S . pavanii to host pathogenic strains within biofilms [55]. These biofilms can attach to internal WTS components, like water tanks, filters and tubing, complicating treatment efforts and enabling bacteria to remain even after post-cleaning. As a result, it is found in a variety of aquatic environments, including treated wastewater and potential drinking water sources. A study revealed that Stenotrophomonas spp. made up a significant portion of the bacterial isolates, with concentrations around 10.2 CFU/mL in treated wastewater and varying levels in natural water sources like ponds and rivers [55]. The discovery of S. pavanii has implications for the safety and quality of water. The discovery of S. pavanii has significant implications for water safety, quality and human health. If infections arise, treatment options can be complicated due to certain strains of S. pavanii developing resistance to various antibiotics [56]. Research has demonstrated that water vending machine can contain different types of bacteria, such as those belonging to the Stenotrophomonas group [57]. The present study from [58] confirmed about S. pavanii can affect water quality in vending machines in several ways, primarily through its ability to form biofilms and its potential as an opportunistic pathogen. In consonance with our findings, the isolate with the sample label S9 strain S9 sourced from WTS drinking water with similarity to Stenotrophomonas pavanii strain LMG 25348 from the GenBank database can probably be characterized as Stenotrophomonas sp. strain S9. Another promising finding was that, [59] isolated the same strain LMG 25348 from sponges (Gelliodes sp.) in Pahang coastal waters. This correlates with [60], who identified same Stenotrophomonas pavanii strain LMG 25348 as an isolate from the body fluid of an insect (butterfly) in England. Based on this review, we hypothesize that the strains have the closest homology. The identification of the identical LMG 25348 strain suggests a noteworthy ecological connection. This results highlights that the organism can be isolated from WTS drinking water resulting from human act and environmental conditions liaising with the report of its adaptability with various places.

Stenotrophomonas maltophilia isolates are diverse species of gamma proteobacteria that can be found in different environmental niches including soils, plant rhizospheres, surface water, wastewater, drinking water, food, or contaminated medical care fluids [61-63]. A statement from the World Health Organization (WHO), in (WHO; Public health importance of antimicrobial resistance), declared Stenotrophomonas maltophilia as one of the top drug-resistant pathogens in hospitals worldwide [64]. The bacteria can inhabit respiratory tracts and might not necessarily indicate active infection but colonization in critically ill patients [65, 66]. S. maltophilia has been found in multiple environmental sources, including water bodies, raising concerns about potential infection transmission, particularly in healthcare facilities where water contamination may
occur [67]. Isolates from both clinical and environmental sources have demonstrated the ability to survive or grow in treated water and non-carbonated mineral water across varying pH levels and temperatures [68, 69]. The bacterium is capable of growing and forming biofilms in potable water distribution systems, posing a potential infection risk for immunocompromised individuals [69]. Extensive results carried out, show that commonly found in drinking water distribution systems, S. maltophilia has been isolated from tap water, bottled water, and treated water samples [65, 70]. Thus, based on the above enumeration, our results of the isolates from sample labels S10 and S5 were characterized as Stenotrophomonas sp strain S10, and S5 can probably be identified as Stenotrophomonas maltophilia. The organism's ability to thrive in diverse environments raises concerns about the potential for contamination of drinking water supplies through environmental pathways [71]. Studies show that S. maltophilia can be present in different water sources, such as treated wastewater, river water, pond water, and tap water. This found evidence from study conducted by [72] in U.S. University, where samples collected were found S. maltophilia. However, the growing recognition of Stenotrophomonas maltophilia as an environmental organism affecting water treatment systems strongly supports our hypothesis.

The presence of resilient bacterial strains like Stenotrophomonas and Bacillus spp. has major implications for the effectiveness of water treatment systems. Their capacity to form dense, resistant biofilms results in higher energy usage, shorter treatment filters lifespan, and increased operational costs. The inhabitance of biofilm bacteria in drinking water is generally harmless, except for species declared as opportunistic pathogens, and under certain conditions may cause disease [1]. Moreover, biofouling can compromise the quality of treated water, as bacteria within biofilms may release endotoxins or other harmful substances into the water supply. This creates a potential public health risk, particularly in systems intended for potable water treatment. In line with the previous studies [73], indicated that the presence of bacterial biofilm population may be due to improper cleaning or contamination rising from users in his study. This can be explained by the fact that humans are a source of WTS contaminating. As such, in view of our observations, the placement sites of the WTS must be considered in addition to regular cleaning and maintenance. This can be compared with the findings of [74] that water treatment systems kept in open public areas with direct sunlight exposure are high in total coliform bacteria as the increase depends on ambient temperature rise. Because some of the bacteria from our findings produce biofilms, this may be an indication of the presence of potential pathogens from the supply of drinking water.

Conclusion

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The microbial quality of all samples was observed and identified by 16 S rRNA sequencing and phylogenetic analysis, which reveals two bacterial genera. The appearance of these bacteria can be associated with the contamination of the water supply and environmental sources. These isolates were anthropogenically influenced by the environmental isolates. Meanwhile, these bacteria could be easily distributed to WTS due to frequent usage by humans, as well as to the influence of environmental bacterial sources. Drinking water from WTS's are confirmed as a medium for bacterial accumulation, and possibly can be transmitted to humans, which should be considered. However, regarding public health, we can conclude that WTSs may represent one of the most important sources leading to the transmission of bacteria to humans. This give a highlights that engineering WTS filters to resist bacterial adhesion, such as through the application of antifouling coatings, or surface texturing that target the specific characteristics of the bacterial strains present could prevent the initial attachment of biofilm-forming bacteria. Also, then WTSs should be periodically tested with proper maintenance, particularly when it is constantly used by people. Educative awareness on personal hygiene should be done to people to avoid cross contaminations cause by human activities. As such, this finding can be used to suggest that regular inspections of WTS are important to maintain the hygienic conditions and avoid microbial contamination.

Acknowledgment

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The authors would like to acknowledge Universiti Teknologi Malaysia for funding through R.J130000.7809.4J663 (Geran Penyelidikan Hi-Tech (F4)).

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Chapter 4

Seismic Design of Buildings and Environmental Geology

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 99-112

doi: 10.4028/p-tYU8GD

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-11-27

Accepted:
2024-12-02

Online:
2025-02-06

Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

The original version of this paper is available on https://www.scientific.net/EI.13.99.pdf

Adi Tonggiroh , Hendra Pachri , Muhardi Mustafa

¹Department of Geological Engineering, Faculty of Engineering, Hasanuddin University, JI. PorosMalino Km.6, Bontomarannu, Gowa 92171, South Sulawesi, Indonesia

Mining Department, Halu Oleo University, JI. H.E.A. Mokodompit, Kampus Hijau Bumi Tridharma, Andounohu, Southeast Sulawesi, Indonesia

atonggiroh@gmail.com, hendrapachri@unhas.ac.id, geodisar65@yahoo.com

Keywords: Soil fertility, post mining, laterite nickel.

Abstract

The aim of this study was to assess soil fertility and heavy metal contamination in soil on reclaimed land in two sample areas. Area R1, trees are difficult to grow and R2, trees can grow. This investigation was conducted to reveal the level of soil fertility, concentration and enrichmentof various heavy metals in contaminated soil. Twenty-two samples, twelve sampling positions inR1 and ten sampling positions in R2. Inductive study combined ICP-MS (Inductively Coupled Plasma Mass Spectrometry) and ICP-OES (Inductively Coupled Plasma-Optically Emission Spectrometry) methodologies to determine the concentration levels of and total of four R1 soil samples and four R2 soil samples were measured by XRD to determine the mineralogy and study the flame emission photometry method, extraction and Walkley-Black to determine the soil physicochemistry: texture, , Available Phospor, K , organic carbon, base saturation and cation exchange capacity (CEC). Correlationcoefficient analysis and cluster analysis, coupled with principal component analysis, were used to analyze the data and to identify possible sources of these heavy metals. Overall, the levels of heavy metal enrichment are arranged in the following EF order: Cr R1 < Cr R2; Mn R1> Mn R2; Zn R1= Zn R2. Heavy metals generally exceed moderate pollution and even strong pollution ( Igeo Igeo Igeo , and Zn , Cu low to moderate pollution levels ( Igeo Igeo ), but , As low pollution levels ( Igeo). The results showed that the difficulty of tree growth in all places in R1 was indicated by the physicochemical properties of soil texture and CEC which were in line with mineralogy in influencing soil fertility and even distribution of heavy metals. Compared to R2, the presence of tree growth was related to a positive correlation between soil texture and organic carbon which played an important role in fertilizing the soil and mineralogy support to control the uneven distribution of heavy metals.

Introduction

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Nickel laterite deposits are formed from the laterization process of ultramafic rocks (Golightly, 1979, 1981; Elias, 2002 Marsh et al., 2013). Since 2004, Southeast Sulawesi has been the location for open-pit mining activities to extract nickel ore from laterite soil, while non-ore laterite rocks andsoil as waste are used as overburden material for post-mining land topographic degradation. Overburden includes soil and rock that are removed to gain access to ore deposits (Rankin, 2011; Vela-Almeida et al, 2015). Overburden is nutrient poor and deeply excavated soils can be phytotoxic, thus these are not suitable for reclamation without amendments (Carrick and Krüger, 2007) but are often used for landscape contouring (Rankin 2011). Improper waste handling resultsin high concentrations of heavy metals (Mitchell et al. 2016).

The concept of mining sustainability generally mandates the recovery of functional land use (Ross & Simcock, 1997; Bowman & Baker, 1998; Cao, 2007; Maczkowiack, et al., 2012). Focusingon postlaterite nickel mining land in Southeast Sulawesi, it turns out that dozens of topographies show minimal success in revegetation. Mining activities change land topography and ultimately influence hydrogeological conditions (Bell et al. 2000; Santos et al. 2002). Mining alters the natural landscape
and discharges large volumes of waste that pose serious pollution hazards to the environment and to agriculture (Festin et al, 2019). It is estimated that because the use of overburden material is only to cover the topography, there will be changes in texture and decreased soil resistance which is susceptible to the accumulation of heavy metals by rainfall. Muhardi et al (2022) stated that the tailings had a positive correlation with the heavy metals . The migration and transformation of heavy metals is closely related to soil resilience (Xu et al, 2017). Toxicity and persistence in the environment (Facchinelli et al. 2001; Loska and Wiechula 2003; Mehrabi et al, 2015). The sorption/desorption reactions of metal(loid)s on/from soil sorbents are influenced by pH , nature of soil components, and presence and concentrations of cations and inorganic anions (Caporale and Violante, 2016). Mineral dissolution from overburden material and high adsorption capacity of laterite soil are responsible for the elevated concentrations (Das and Chakrapani, 2011).

In this paper, the heterogeneous chemical nature of heavy metals contained in laterite soil is the reason for using multivariate statistical techniques that support the literature, namely: Pearson Correlation (PC), Cluster Analysis (CA), Principle Component Analysis (PCA). Multivariate analysis has the power of grouping individual parameters and variables (Ruiz et al, 1998; Yang etal, 2009; Franco-U et al, 2009; Hu et al, 2011; Yao et al, 2013). A multivariate statistical method such as the principal component analysis (PCA) and cluster analysis (CA) is a powerful tool for evaluating pollution levels among samples (Huang et al, 1994; Hani and Pazira, 2011).

The texture, pH and geochemical properties of soil are an ideal medium for water to transport, infiltrate and deposition heavy metal elements. Accumulation of heavy metals in soil may result in deteriorating soil quality, reducing soil fertility (Zang et al, 2017; Fei et al, 2020). Soil geochemical characteristics have undergone significant changes by anthropogenic activities (Alekseenko et al, 2017). So this study uses soil as a source of information in environmental geochemistry, for example the basic chemical properties of trace elements, mobility, enrichment, mineralogy, soil fertility and heavy metal contamination.

The aim of this research is to assess soil fertility and heavy metal contamination in soil from samples taken from two reclamation sites (R1 and R2) based on determining their concentrations and potential toxicity when they appear in the soil. The results obtained can serve as a reference level for future studies of post-mining nickel laterite lands providing insight into heavy metal speciation and as a selection of remediation reference options for suitable soil improvement.

Site Description

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The research was conducted on post-mining reclaimed land for nickel laterite in the North Konawe administrative area, which is known as a mining economic area in Southeast Sulawesi. The research study was carried out in two post-mining land reclamation areas with revegetation characteristics that had not succeeded in growing with sample code R1 and vegetation characteristics that had succeeded in growing with sample code R2, dominated by only one type of Falcataria moluccana plant (Figure 1). Area R1 extends northeast-southwest and ) and area R2 extends east-west ( and ).

The southern Konawe area has a clear difference between the dry season and the rainy season. The dry season lasts for 5 months from July to November, and the rainy season from December to June with the lowest rainfall in September, namely 61 mm . The northern part of Konawe is known as a non-monsoon zone, that is, there is no clear boundary between the rainy and dry seasons. In fact, the rainy season occurs almost every month with an average rainfall of and annual rainfall > 2000 mm (BMKG, 2022).

Some of the nickel mining entities are in South Konawe, North Konawe which is a geological regional ultramafic rock belt on Sulawesi Island which is dominantly located in the southeastern part. Laterization of ultramafic rocks produces alkaline chemical composition minerals, mainly nickel laterite, iron, manganese, cobalt, chrome and gangue oxide minerals such as quartz, magnesium, aluminium, in varying proportions. Apart from that, the presence of minerals with an acidic chemical composition such as copper sulphide, lead and cadmium in very low proportions, is related to ultramafic and metallogenic province types (Zhang et al, 2019; Scoates et al, 2017;Barnes and Lightfoot, 2005).

Sampling and Analysis

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The research location was selected based on similarities in reclamation since 2008, identification of soil profiles and differences in revegetation. Using the Global Positioning System (GPS), sampling locations were designed to obtain representative samples which were carried out on two topographic reclamation hills with different characteristics of no vegetation in the north and vegetation in the south (Figure 1).

Fig. 1. Sample groups R1 and R2 at the research location.

Fig. 1. Sample groups R1 and R2 at the research location.

A series of standard soil collection procedures refers to Zheng (2005) and Guo (2009), where one kg of fresh soil is collected at each sampling point for one analysis method. In this study, three investigation methods were used, so that at one sampling point there was three kg of soil in different bags. Surface soil samples were taken using a hand drill where the sampler handle was made of steel with an overall length of 1.0 m and a diameter of 2.5 cm , carried out at a depth of ( ), . Soil samples per kilogram were placed in polyethylene bags and transported.

Analysis Method 1. A total of 22 samples were divided into two groups: R1 (Reclamation 1) 12 samples and R2 (Reclamation 2) 10 samples. For R1, it is divided into R1A representing depth and R1B , and R2A , R2B . R1A has 6 samples: 1A-6A and R1B has 6 samples: 1B-6B, for R2A has 5 samples: 7A-11A and R2B has 5 samples: 7B-11B. All samples for investigation of major elements, minor elements, trace elements using neutron activation analysis at X-Ray Laboratories PT Intertek Jakarta using plasma ICP-MS (Inductively coupled plasma mass spectrometry) and ICP-OES (Inductively Coupled Plasma-Optically Emission Spectrometry). Detection limit for As; for Cd ; for Cr ; for . The results of investigations on sample quality control, accuracy of minor element and trace element analysis are in accordance with international standards.

Analysis Method 2. A total of 8 samples for soil fertility analysis, for R1A represented by 4 samples ( 1 A to 4 A ) and for R 2 B represented by 4 samples ( 7 A to 10 A ). Soil samples were air-dried and sieved (mesh size 2 mm ). Investigations include: texture following USDA standards (1999), pH values were analyzed in H 2 O (1:2.5 soil:solution ratio); and by flame emission photometry after extraction with Mehlich-1; Ca and Mg by titration after extraction with solution; Available Phosphorus (AP) and K etraction . Organic carbon (OC) was determined by the Walkley-Black method. Cations concentrations were used to calculate base saturation and cation exchange capacity cmol/kg. All samples for psychochemical soil investigations were determined
according to the method described by Blackmore et al (1981), Rayment and Higginson (1992) and carried out at the Soil Chemistry and Fertility Laboratory, Department of Soil Science, Hasanuddin University.

Analysis Method 3. A total of 8 bulk soil samples for mineralogical analysis, representative for R1A (1A, 2A, 3A, 4A) and for R2A (7A, 8A, 9A, 10A). Preparation of 10-20 grams of soil samples was weighed using a scale with an accuracy of 4 decimals. Destruction, the soil was put into a 1000 ml beaker, then poured 200 ml of solution. Continued with a heating process at a temperature of , following the stages of Moore and Reynold (1997), Kahle et al (2002). All samples for the investigation of mineralogical composition used X-ray diffraction (XRD, shimadzu maxima X-7000 diffraction meter) and were carried out at the Department of Geological Engineering, Hasanuddin University.

XRD Analysis

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XRD analysis Bulk mineralogy of soil samples from selected representative samples for R1A and for R2A (7A,8A,9A,10A) was examined by X-ray diffraction method (Shimadzu Maxima X- 7000 difracrometer with Cu Ka irradiation). The 2 lm clay mineral fraction was obtained using the sedimentation method (Rowell 2014). Samples were centrifuged twice for 15 minutes using a Sigma 4-15 centrifuge. The clay suspension was pipetted onto a glass slide and allowed to dry at room temperature. Air-dried, ethylene glycol-saturated and heated samples were used for determination of clay mineral species according to the method described by Moore and Reynolds (1989). Measurements were performed at 40 kV and 40 mA and scanning speed from 3 to 302 H (or from 2 to 302 H ) at a step size of 0.022 H min-1. Phase identification and peak area determination were carried out using Win XRD software.

Statistic Analysis

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Pearson correlation statistics (CP) have analytical power in their coefficient values which explain the size of the relationship between two variables (Ma et al, 2015). For this study, CP was used to determine the relationship between different heavy metals, then required an analysis of the sensitivity of each heavy metal in relation to its source.Similarities and differences in the chemical properties of heavy metals are variable classifications in the hierarchical agglomeration type cluster analysis (CA) method. The results are in the form of a dendogram where automatic steps with a hierarchical CA procedure in standardizing groupings are a solution of the distance values between clusters (squared Euclidean distance) to assess compactness and correlation. The purpose of CA isto identify groups or clusters of similar sites on the basis of similarities within a class and dissimilarities between different classes (Sparks, 2000). Strengthening CP and CA results using additional analysis, namely principal component analysis (PCA), a variable reduction technique in which a number of measured parameters can be extracted into a small number of variables into principal components. PCA was interpreted in accordance with the hypothetical source of heavy metals (lithogenic, anthropogenic or mixed) (Yuan, 2013). Classification of variables in the Pearsoncorrelation analysis (CP), Cluster Analysis (CA) and principal component analysis (PCA) methods is carried out using using SPSS Version 26.IBM.

Calculation of Indices to Assess Anthropogenic Influence

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Enrichment Factors (EFs)

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Quantitative assessment of the relative levels of heavy metal contamination in soil by lithogenic and geogenic for two sequences of sample groups R1A and R1B to R1AB, and two sequences of sample groups R2A and R2B to R2AB. So a comparison is made with the background Concentration of the earth's crust using Fe as a reference element following the assumption that its content in the earth's crust is not disturbed by anthropogenic activities.

EF was calculated according to the regenerated equation of Zoller et al. (1974) (Lu et al., 2009) (Eq.(1)) :

Where (Cx/Cref) sample is the concentration of the heavy metal being examined in the sample and Fe in the sample. (Cx/Cref) background is the ratio between the heavy metal concentration and the reference Fe concentration of the upper continental crust (Wedepohl, 1995). Five contamination categories are recognized on the basis of the EF: these are denoting deficiency to minimal enrichment; 2-5, moderate enrichment; 5-20, significant enrichment; 20-40, very high enrichment; >40, extremely high enrichment (Lu et al., 2009).

Geo-Accumulation Index (Igeo)

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Applying a quantitative assessment of the geoaccumulation index (Igeo) of contamination in sediments (Muller, 1969) and soil pollution (Loska et al, 2003) for two sequences of sample groups R1A and R1B to become R1AB, and two sequences of sample groups R2A and R2B to become R 2 AB . So Igeo makes it possible to assess contamination by comparing the latest concentrations of metals in post-mining fields. Igeo is calculated using the equation:

Geo-accumulation was proposed by Muller (1969) and was defined as follows:

where Cn is the metal concentration measured in n samples, and Bn is the average geochemical background of n elements in the earth's crust. The geo-accumulation index has been categorized into seven levels of contamination ranging from highly contaminated to not contaminated (Muller 1969; Barbieri 2016).

In this study, the homogeneity of laterite soil has been chosen as a special criterion for ultramafic rocks as geochemical background concentrations of , and general criteria for igneous and sedimentary rocks for geochemical background concentrations of , As. The constant 1.5 compensates for possible natural fluctuations in the content of certain substances in the environment, as well as detecting very small anthropogenic influences (Loska et al, 2004; Ji et al, 2008). Seven Igeo classes (Muller, 1969) represent the level of soil pollution, as follows: Class Igeo (practically uncontaminated); Class 1=0<Igeo (uncontaminated to moderately contaminated); Class Igeo (moderately contaminated); Class Igeo (moderately to heavily contaminated); Class Igeo (heavily contaminated); Class Igeo (heavily to extremely contaminated); Class Igeo (extremely contaminated).

Results and Discussion

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Physicochemical Soil Characteristics on Post-Mining Land

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Soil properties measured on post-mining land R1A were identified as showing parameters that influence soil fertility. Table 1, the physical properties of soil texture R1A are dominated by clay silty, while R2A varies with the sand clay silty texture. So, the layer depth of in R1A has lower porosity than the soil in R2A. Porosity is very important because it greatly influences the physical properties (soil structure and aeration), soil chemistry (nutrient movement) and biology (soil microorganism activity) which influence plant growth, so that the physical and chemical properties of R2A soil are relatively good compared to R1A.

OC content and Ntotal R1A R2A. So in RA1 the soil's ability to provide nitrogen and the solubility of basic cations is reduced, thereby affecting CEC (Cation Exchange Capacity). The study area has high rainfall causing acidic pH , so that in R1A, R2A there is penetration and leaching by rainwater of the soil texture which affects the reduction of nitrogen. The influence of rainfall was identified on the tendency of low AP which was influenced by Ca and metals, this was strengthened by the similarity of the CEC R1A, R2A values. That rainfall affects the mobilization of increased metals (generally Mn in laterite soils) and disrupts soil adsorption, so that in R1A soil fertility is very low to provide nutrients for plant growth.

Table 1. Physicochemical soil characteristics at R1A and R2A, R1A and R2A.

Sample
OC
(%)
N
(ppm)
AP
Sand Silt Clay
pH
H₂O
Ca
Mg
Na
cmol/kg
K
CEC

R1A

1A
0,92
0,08
9,13
37,04
17,10
45,85
5,49
2,96
2,38
0,14
0,34
27,69

2A
0,59
0,09
10,94
33,75
15,84
50,40
5,27
3,17
2,46
0,14
0,25
24,66

3A
0,62
0,05
8,16
5,39
51,41
43,18
5,75
3,40
2,23
0,23
0,18
20,43

4A
1,72
0,15
9,19
29,41
33,29
37,29
6,04
3,91
2,60
0,22
0,42
21,35

5A
0,66
0,12
9,73
6,83
63,29
29,87
5,92
2,13
1,85
0,21
0,22
19,15

6A
0,80
0,07
10,22
7,86
72,99
19,14
5,75
2,53
2,23
0,19
0,16
18,32

R2A

7A
0,98
0,09
7,68
36,99
28,90
34,10
5,58
2,99
1,71
0,15
0,19
26,32

8A
0,95
0,12
9,74
15,22
65,46
19,31
5,74
2,32
1,84
0,22
0,32
21,35

9A
1,00
0,07
10,83
32,34
34,51
33,13
5,84
3,38
1,31
0,15
0,23
18,15

10A
0,95
0,10
10,28
34,36
36,12
29,51
5,46
2,53
1,16
0,23
0,28
19,31

11A
1,05
0,10
8,11
25,55
35,61
38,82
5,92
3,40
1,79
0,17
0,16
18,36

The soil texture properties in R1A are correlated with the XRD results where the mineral composition shows enrichment in quartz, chlorite, diopsid, montmorillonite, hematite, magnetite, goethite, spinel, talc, maghemite. The discovery of spinel, montmorillonite and talc which supplies to the clay texture which disrupts the hydrous phase balance of hematite, maghemite, geotite, and activates spinel to significantly increase the mobility of heavy metals. Spinel often results in the sequestration of contaminants such as (Schindler et al, 2019). Thechemistry of the spinel mineral in ultramafic rocks is of the low Al and high Cr variety (Sahu et al, 2020). The minerals observed in quartz, diopsid, chlorite, spinel are an indication that the soil texture originates from the high intensity of weathering of serpentinized ultramafic rocks. Chlorite is rarely found in soil and is an indicator of intensive weathering (Schulze, 2005). Diopside represents the Ca product of serpentinized ultramafic pyroxene, and spinel forms in surface soils, contributing to Zn and Pb (Schindler et al, 2021). Large amounts of fertilizer added regularly to the soil contain small amounts of the heavy metals which significantly increase in the soil (Jones and Jarvis, 1981). Application of certain phosphatic fertilizers inadvertently adds Cd and other potentially toxic elements to the soil, including Pb (Raven et al, 1998).

Multivariate Statistical Analysis

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Correlation Analysis

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The correlation matrix to identify the correlation between metal concentrations and soil properties in R1A at the significance level of and . Based on the Pearson correlation coefficient, it appears that the nine heavy metals ( ) found a significant positive correlation except , in 4 groups: (1) Mn concentration was strongly correlated with (correlation coefficients respectively and 0.95 ); (2) Cr concentration is strongly correlated with (correlation coefficients 0.83 and 0.84 , respectively); (3) Co concentration is strongly correlated with Pb , As (correlation coefficients 0.85 and 0.87 , respectively); (4) Pb concentration is strongly correlated with Cu , As (correlation coefficients 0.87 and 0.89 respectively). Shows that the geogenic group of metals have the same source of rock weathering. And the sources of rock weathering are different from the metal groups , As, Zn.

Correlation matrix analysis of all measured soil properties showed several typical correlations identified. Significant positive correlation was observed between sand, silt, clay and CEC ( , 0.05 ). These findings show that the study area which is classified as having monthly rainfall has an effect on relatively smaller spaces in the soil and allows water penetration to increase oxidation, which is strengthened by the positive correlation between Ca and and Mg accompanied by
found in chlorite, diopsid, montmorillonite. Apart from that, significant correlations were observed between Na and pH , between K and OC, in fact a correlation was also found between Ntotand Ni, where Ni was not included in the correlation matrix. This implies that a significant positive correlation causes a decrease in pH values thereby reducing the supply of nitrogen to the soil. It wasfound that CEC, , Ntot and soil texture directly or indirectly influence soil fertility and heavy metal mobility, as shown by the positive correlation between Ntot and , between K and Cr ( ). Other metals did not show significant correlations with all soil properties. The next finding was that there was no significant correlation between pH and heavy metals (except ), but at least pH played a role in heavy metal concentrations. Soil with low pH can facilitate the migration and availability of heavy metals (Zhang et al, 2001; Li et al, 2005). The Pearson R2A correlation matrix, was used to identify the correlation between metal concentrations and soil properties. It was found that the heavy metals were significantly positively correlated ( ) in 2 groups: (1) Mn concentration was strongly correlated with Co (correlation coefficient 0.90 ); (2) Zn concentration is strongly correlated with Cr and Cu (correlation coefficients 0.92 and 0.93, respectively). Shows that this metal has a geogenic source.

Correlation matrix analysis shows that only a few soil properties have been identified as having significant correlation. Significant negative correlations were observed between Ntotal and Cr, then between Na and Cr , but positive effects between Ca and Cr , and . These findings indicate that an increase in the concentration of the heavy metal Cr can cause a decrease in Ntot and Na values, while an increase in Ca actually increases the soil organic value (SOC) which is supported by the negative correlation of silt and sand particles, and silt and clay.

Cluster Analysis

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Analysis of the R1A cluster results is illustrated in the dendogram (figure 2), showing two clusters: (1)Mn ; (2)Cr
Zn and without Ni, Cd, Cu clusters. The manganese, arsenic and lead clusters form independent groups according to group 1 of the Pearson Correlation (CP) R1A results, as do chrome and zinc, according to group 2 of the Pearson Correlation (CP) R1A results.

Fig. 2. Dendrogram derived from hierarchical cluster analysis of heavy metal content in soil in R1A.

Fig. 2. Dendrogram derived from hierarchical cluster analysis of heavy metal content in soil in R1A.

Analysis of the R2A cluster results is illustrated in the dendogram (figure 3), showing two clusters: (1) Mn ; Zn and without , Ni clusters. The results of this grouping are in accordance with the results of the Pearson Correlation (CP) R2A. That the independent group manganese, cobalt corresponds to group 1 and the zinc, copper, chrome cluster corresponds to group 2 according to the Pearson Correlation (CP) R2A analysis results. The lead, arsenic, cadmium and nickel clusters form their own group, which shows that the heavy metal group comes from geogenic sources.

Dendrogram using Average Linkage

Fig. 3. Dendrogram derived from hierarchical cluster analysis of heavy metal content in soil in R2A.

Fig. 3. Dendrogram derived from hierarchical cluster analysis of heavy metal content in soil in R2A.

PCA Analysis

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Through rotation, the distribution of variables becomes clear and real in R1AB in table 4, the large factor loadings are increasingly increased in value and vice versa, compared to before the rotation was carried out. 3 rotations of matrix components are produced, according to the number of factors obtained, namely the distribution of variables into factors with the rotation process. From the results above, after rotation, there are seven elements that are highly correlated (cut offpoint ) except Ni and Cd . Factor 1 is , As ( ). Factor 2which is highly correlated is .

Through rotation of the variable distribution, it becomes clear and evident in R2AB, that large factor loadings increase in value and vice versa, compared to before the rotation was carried out. 3 rotations of matrix components were produced, according to the number of factors obtained, namely the distribution of variables into factors with the rotation process. From the results above, after rotation, there are seven elements that are highly correlated (cut offpoint ) except , with factor 1, namely, Zn and and 0.873 and . For factor 2 which is highly correlated, namely . For factor 3 which is highly correlated, namely .

Enrichment Factors (EF)

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The mean EF for heavy metal enrichment in R 1 AB very high enrichment ( ) was observed in to 49,117 , significant enrichment in to 11,336 and to 10,604 . Minimal enrichment was observed in to 0.007 , to 0.17 ), to 1.117 .

Mean EF for heavy metal enrichment in R2AB extremely high enrichment ( ) was observedin Cr ( to 64,208 ), significant enrichment in to 7,479 and to 11,909 . Minimal enrichment was observed in to 0.006 to to 1.262 . From the category of all heavy metals, only Pb , Cd, As whose EF shows no pollution from these three heavy metals in the study area and refers to the physical properties of the soil, then is the possible source of pollution from the addition of fertilizer. Furthermore, the results of the Pearson Correlation analysis show that the heavy metalsAs, Cu are different sources of pollution from . Geogenic pollution is confirmed by minimal enrichment for Ni and Co related to soil sources, namely: enrichment results from ultramafic weathering. The enrichment factor analysis for is at the highly polluted stage, and the EF of the three metals in R1AB is lower than R2AB. In connection with these metals, differences in the use of overburden as post-mining land cover and other geogenic causes can be attributed.

Presents the spatial distribution of heavy metals in R 1 . The spatial patterns of show the same distribution trend with high concentrations in the north-northeast-south area and low concentrations in the northwest-west area. This distribution pattern appears to be smooth andregular, indicating that soil texture factors, low porosity and OC play an important role in controlling the distribution of these heavy metals. Substantively, these heavy metals are produced from the weathering of ultramafic rocks and soil formation. Of all these heavy metals, it seems that the irregular pattern of high concentrations of Cr and low concentrations of Zn means that thesource of the cover soil used to cover the land is a mixture of top soil taken from within the researchlocation.

Geoaccumulation Index (Igeo)

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In table 2, the Igeo mean for heavy metal contamination in R1AB extremely contaminated (class 6) is observed at to max 7.130) and to 7.172). Heavily contaminated (class 4) was observed in to 4.012) and moderately to heavily (class 3) was observed in to 3.362 , which allows Mn with Co in class 4 . Moderately (class 2 ) was observed in Zn ( 1.207 up to 1.750 ), and uncontaminated status (class 0 ) was observed for , except Cu ( -0.063 to 0.605 ) uncontaminated to moderately.

Mean Igeo on heavy metal contamination in R2AB extremely contaminated (class 6) was observed at to and Ni (6.253 to 6.880). Heavily contaminated (class 4) was observed at to 3,950 and moderately to heavily (class 3 ) was observed at to 2,783). Moderately (class 2) was observed in Zn ( 1.188 to 1.938), and uncontaminated status (class 0 ) was observed in , As, except to 0.802 uncontaminated to moderately. The similarity of R 1 AB and R 2 AB in contamination is the metal mobility behavior which can be caused by rain so that fluctuations in contamination lie at (uncontaminated to moderate contamination, this is reinforced by being at medium to high transition. Likewise, the enrichment of metals by , these two metals originate from the geochemical environmentof laterite soil, so the mobility of both is greatly influenced by water infiltration.

The results of IgeoR1, Igeo R2A, R2B compared with EF R1AB and EF R2AB are not easy because there are differences in the nature of the Igeo calculation which involves a logarithm function and a background value multiplication factor of 1.5. However, at least in this study referring to the Igeo and EF similarity approach, the main observations are aimed at heavy metals, namely Cr (Igeo ), and also Mn , Ni, Co, Zn.

Table 2. Geo- accumulation index at R1AB ( ) and R2AB ( ).

Sample | Element | min | max | Mean | SD | Skew | Kurt | Geo-accumuation Index | Degree Igeo
R1AB | Mn | 1,900 | 3,362 | 2,367 | 0,410 | 1,378 | 2,217 | 2 < Igeo < 3 | Class 3
R2AB |  | 1,777 | 2,783 | 2,364 | 0,283 | -0,624 | 1,182 | 2 < Igeo < 3 | Class3
R1AB | Cr | 5,681 | 7,130 | 6,500 | 0,445 | -0,418 | -0,0502 | 5 < Igeo | Class 6
R2AB |  | 6,148 | 7,535 | 6,925 | 0,405 | -0,366 | 0,245 | 5 < Igeo | Class 6
R1AB | Co | 3,121 | 4,012 | 3,488 | 0,278 | 0,212 | -0,546 | 3 < Igeo < 4 | Class 4
R2AB |  | 2,755 | 3,950 | 3,569 | 0,365 | -1,302 | 1,670 | 3 < Igeo < 4 | Class 4
R1AB | Ni | 6,268 | 7,172 | 6,689 | 0,280 | 0,449 | -0,525 | 5 < Igeo | Class 6
R2AB |  | 6,253 | 6,880 | 6,640 | 0,203 | -0,692 | -0,214 | 5 < Igeo | Class 6
R1AB | Pb | -4,672 | -1,087 | -3,227 | 0,870 | 1,107 | 3,029 | Igeo <0 | Class 0
R2AB |  | -4,672 | -1,865 | -3,116 | 0,760 | -0,599 | 1,300 | Igeo <0 | Class 0
R1AB | Zn | 1,207 | 1,750 | 1,461 | 0,144 | 0,345 | 0,574 | 1 < Igeo < 2 | Class 2
R2AB |  | 1,188 | 1,938 | 1,544 | 0,241 | -0,128 | -0,639 | 1 < Igeo < 2 | Class 2
R1AB | Cu | -0,063 | 0,605 | 0,158 | 0,207 | 0,23 | 0,368 | 0 < Igeo < 1 | Class 1
R2AB |  | -0,063 | 0,802 | 0,338 | 0,321 | 0,020 | -1,496 | 0 < Igeo < 1 | Class 1
R1AB | Cd | -1,907 | 1,000 | -0,448 | 0,870 | -0,375 | -0,088 | Igeo <0 | Class 0
R2AB |  | -1,907 | 1,900 | -0,224 | 1,224 | 0,95 | -0,0845 | Igeo <0 | Class 0
R1AB | As | -1,263 | 1,322 | -0,200 | 0,684 | 0,479 | 1,769 | Igeo <0 | Class 0
R2AB |  | -1,263 | 0,737 | -0,188 | 0,814 | -0,0451 | -1,523 | Igeo <0 | Class 0

Discussion

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The level of fertility at the two different locations is in accordance with the physicochemical and mineralogical properties of the soil (Table 1), which were measured from testing soil samples (representatives of R1A and R2A) at shallow depths ( ) for R1 and R2. The results of the positive correlation analysis of R1 soil texture with Cation Exchange Capacity (CEC) were accompanied by a decrease in oxygen, organic carbon, nitrogen and cation base solubility. This correlation occurs in decreasing porosity, increasing groundwater penetration which results in decreasing pH and phosphorous. In line with the presence of minerals in the soil such as: talc, diopsid, spinel, montmorillonite, where these minerals contain the elements . These elements can reduce the control of soil absorption properties and lead to disruption of the balance of the oxidation phase in hydrous minerals, such as hematite, maghemite and geotite. The physical and chemical atmosphere of the soil is represented by the mineral spinel, which can increase the mobility of heavy metals . Spinel often results in the sequestration of contaminants such as (Schindler et al, 2019; Sahu et al, 2020).

These findings reveal that the difficulty of tree growth at all places in R1 is correlated with the low level of soil fertility and the uneven distribution of heavy metals at shallow depths.

Furthermore, in R2, the results of the positive correlation of soil tests on Ca and Zn lead to the role of these two cations in porosity which gradually increases organic carbon which comes from rotting plant leaves and plant roots. It seems that there are indeed places that have soil fertility at shallow depths, and their distribution could expand to the extent that there is no further reclamation treatment or changes in plant types. Highlighting the presence of spinel, talc, quartz, gibbsite minerals which increase the soil's ability to resist heavy metal toxicity, which is supported by the results of cluster analysis, factor analysis, and negative correlation analysis of nitrogen and sodium. Strengthening soil absorption to inhibit the mobility of heavy metals and apparently Pb , As, , is difficult to reduce soil fertility. Thus, soil fertility R 2 really shows the control of the activity of uneven distribution of heavy metals and the existence of ecological balance in shallow depth soil. One of the main factors that explains the geogenic emergence of heavy metal concentrations is explaining the soil source (soil parent). The research area is filled with laterite soil, which is a type of residual soil resulting from the weathering of ultramafic rocks. Due to the serpentinization process, ultramafic has different chemical and mineral compositions so that laterization produces nickel types: silica, ferrous and fractured (Golightly, 1981; Kadarusmanet al, 2004; Maulana et al, 2015; Zhang et al, 2020; Tonggiroh et al., 2017).

In particular, the results of PCA analysis of heavy metals Mn, Cr, Co (Table 4) in soil R1 are influenced by the geochemical characteristics of nickel-ferrous laterite and serpentinized ultramafic mineralogy, such as: chlorite, diopsite, montmorillonite. In contrast to the PCA results of heavy metals (Table 5) in R2 and the presence of minerals such as: spinel, talc, quartz, gibbsite. The geochemical characteristics of the soil come from silica nickel and fractured nickel. Inaddition to the geochemical and mineralogical characteristics of the soil from geogenic pollution sources, the high anthropopigenic concentrations of heavy metals in the study area are also highlighted by the results of the EF (Table 6) and Igeo (Table 7) analysis. Explanation of EF: Cr R1<Cr R2; Mn R1> Mn R2; EF Ni, Co, Cu; Igeo Mn, Cr, Co, Ni and CP analysis results (Table 2), that the enrichment of Mn is higher than Cr in R1. The enrichment pattern implies a reduction in thesoil's geochemical capacity for heavy metal mobility caused by errors in reclamation of the use of soil obtained from within the R1 location when used to cover post-mining topography. Different in R2, EF value and CP analysis results (Table 3) that Mn enrichment is lower than Cr. The enrichment pattern explains the soil's ability to inhibit the mobility of heavy metals due to the implementation of reclamation using soil taken from outside the R 2 location. From the category of all heavy metals, only whose EF shows no pollution at the study location.

Conclusion

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These findings reveal that the implementation of reclamation has not met the technical rules for using overburden material taken from within the location or from outside the post-mining land location, which of course can affect the physicochemistry of the soil and provide space for the distribution of heavy metal contamination. Assessment of soil fertility and mineralogy as well as heavy metal contamination As, was carried out on soil at two locations (R1 and R2) based on soil physicochemical, mineralogical, EF and I-geo properties. The difficulty of tree growth in all places in R1 is shown by the negative correlation analysis of soil texture and CEC which is in harmony with mineralogy in influencing soil fertility and the even distribution of heavy metals. Compared to R2, the presence of tree growth is related to the positive correlation of soil texture and organic carbon which play an important role in soil fertilization and mineralogical support to control the uneven distribution of heavy metals. Overall, the degree of heavy metal enrichment was arranged in the following EF order: Cr R1 < Cr R2; Mn R1> Mn R2; Zn R1= Zn R2. The heavy metals are generally more than moderately polluted or even strongly polluted ( Igeo Igeo Igeo , and have low to moderate pollution levels ( Igeo Igeo ), but , As pollution levels are low ( Igeo ).

All heavy metal pollution levels were high in R1 and R2, mainly due to geogenic and anthropogenic inputs. Continuous reclamation activities without paying attention to the rules for using top soil as topographic cover, which causes environmental degradation, especially post- mining land, becomes unproductive and poses a greater risk to public health. The results presented will provide valuable reference information on remediation for appropriate soil improvement, for policy makers in determining exactly how to sustainably manage post-laterite nickel mining land in the future.

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The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-13/978-3-0364-1728-8

Engineering Innovations, ISSN: 2813-1002, Vol. 13, pp 113-118

doi: 10.4028/p-A1nWD6

© 2025 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2024-10-28

Accepted:
2024-10-28

Online:
2025-02-06

Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

Andrius N. Lucana , Rick M. Delgadillo and Joan R. Casas

Department of Civil Engineering, Peruvian University of Applied Sciences (UPC), Av. Prolongación Primavera 2390, Santiago de Surco, Lima, Peru

Department of Civil and Environmental Engineering, Polytechnic University of Catalonia (Barcelona Tech), Carrer de Jordi Girona, 31, Les Corts, 08034, Barcelona, Spain.

u201924124@upc.edu.pe, rick.delgadillo@upc.edu.pe cjoan.ramon.casas@upc.edu.pe

Keywords: Seismic design, tall buildings, Nonlinear analysis, FEMA 440, SEOAC.

Abstract

This article develops two three-dimensional models: one for designing an 18-story building, considering the E030 and E060 norms of Peru, and a second nonlinear model aimed at estimating the lateral capacity of the structure through nonlinear static analysis and verification of seismic performance. It highlights the prevalence of informal structures in Peru, which lack a performance-based design methodology. In this context, relying solely on elastic design may be insufficient, especially for tall buildings. A "seismic gap" is identified, referring to the discrepancy between current design practices and the actual seismic demands that structures may face, raising concerns about the vulnerability of existing and future structures to a potential earthquake. The nonlinear static analysis showed that the 18 -story structure reached a maximum base shear of 1752 tons and a maximum displacement of approximately 53 cm in the X direction, and similarly, a maximum base shear of 1538 tons and a maximum displacement of 51 cm in the Y direction.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

Tall reinforced concrete buildings present a significant challenge in seismic design due to their structural complexity and dynamic response to seismic events. In recent decades, various approaches have been developed to improve the seismic performance of these structures, ranging from the implementation of new materials to the refinement of analysis and design methods. In [1], the seismic performance of medium and high-rise buildings constructed with Engineered Cementitious Composites (ECC) was compared with conventional reinforced concrete (RC) structures. The results showed that the use of ECC allowed for a significant reduction in steel reinforcement and improved overall seismic performance, with increased lateral capacity and better cyclic response. On the other hand, [2] focused on the seismic response evaluation of a building with structural concrete walls and coupling slabs in Chile. The study revealed that the fundamental period of the building was 0.613 seconds, with a stiffness index of , classifying it as having normal stiffness. The elastic base shear obtained from the spectral response analysis in the transverse direction was , equivalent to of the seismic weight of the structure. In [3], a new approach was proposed to assess the residual service life of tall reinforced concrete buildings before and after rehabilitation. The presented case study showed that the residual service life of existing beams was 65.6 years, while columns exceeded 94.1 years. Additionally, it was found that story drift increased with the design service life, with seismic action being more influential than wind load after 35 years. An extensive study conducted in [4] on 30 existing reinforced concrete buildings in Turkey revealed that the average displacement ductility factor was 3.78 for frames and 3.28 for dual systems, values lower than those expected by the code. The ultimate displacement-to-total height ratio ( ) was approximately for all buildings, while the yield displacement-to-total height ratio ( ) ranged between and . Regarding analysis methods, Mehmood et al. [5] evaluated the accuracy of the Uncoupled Modal Response History Analysis (UMRHA) method compared to the Nonlinear Response History Analysis (NLRHA) method for predicting the seismic response of tall buildings. The results showed average discrepancies of for floor shear, for floor moment, for
story drift ratio, and for floor acceleration. Finally, Ren et al. [6] investigated the seismic behavior of tall buildings using composite steel-concrete columns and shear walls. The analyses revealed that the scheme with shear walls reinforced with steel plates (SPRSW) was generally the most effective in reducing deformations, especially in the lower floors. In this context, the present study focuses on evaluating the seismic performance of a tall reinforced concrete building using the criteria established by FEMA 440 and SEAOC.

Methodology

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

The main objective of this study is to analyze the seismic design of an 18-story reinforced concrete building in Lima, Peru, to enhance its structural strength and seismic response capacity. To achieve this, the specific goals are: (a) Develop accurate structural models, considering key elements such as columns, beams, and walls. (b) Evaluate the building's seismic response through nonlinear analysis. (c) Assess the seismic performance concerning design, service, and maximum considered earthquakes using FEMA 440 and SEAOC methods. The case study focuses on an 18-story building designed for multifamily residential use, located in Lima, Peru. The reinforced concrete structure has a modern and elegant design that harmoniously integrates with the urban environment of the city. Each floor houses spacious apartments with an average area of , carefully planned to provide comfort and functionality to residents.
Figure 1 shows the procedure followed in this investigation.

Fig. 1. Research flow chart.

Fig. 1. Research flow chart.

Effective Reinforcement Provision. For Columns, the table 1 shows the column reinforcement used for the non-linear model.

Table 1. Reinforcing steel for reinforced concrete columns.

SECTION
MATERIAL
LOC.
STEEL
BAR
DIAMETER
(mm)
STEEL
DISTRIBUTION

C(60×60) 1
Concrete 350 kgf/cm²
A-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @10cm

C(60×60) 2
Concrete 280 kgf/cm²
A-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @15cm

C(60×60) 3
Concrete 210 kgf/cm²
A-5
A615 Grade 60
19 - 25.4
(10-4)/ 3/8" @15cm

C(60×85) 1
Concrete 350 kgf/cm²
C-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @10cm

C(60×85) 2
Concrete 280 kgf/cm²
C-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @15cm

C(60×85) 3
Concrete 210 kgf/cm²
C-5
A615 Grade 60
19 - 25.4
(10-4)/ 3/8" @15cm

C(70×70) 1
Concrete 350 kgf/cm²
B-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @10cm

C(70×70) 2
Concrete 280 kgf/cm²
B-5
A615 Grade 60
19 - 25.4
(0-14)/ 1/2" @15cm

C(70×70) 3
Concrete 210 kgf/cm²
B-5
A615 Grade 60
19 - 25.4
(10-4)/ 3/8" @15cm

For Beams, the table 2 shows the beam reinforcement used for the non-linear model.

Table 2. Reinforcing steel for reinforced concrete beams.

SECTION
MATERIAL
LOC.
STEEL
BAR DIAMETER
(mm)
STEEL
DISTRIBUTION

V(30x45)
Concrete 210 kgf/cm²
C-4
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

V(30x50)
Concrete 210 kgf/cm²
A-1
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

V(30x60) A
Concrete 210 kgf/cm²
A-5
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

V(30x60) B
Concrete 280 kgf/cm²
C-1
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

V(30x60) C
Concrete 210 kgf/cm²
A-2
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

V(30x60) D
Concrete 210 kgf/cm²
A-4
A615 Grade 60
19 - 25.4
Top(3-2) / Bottom(3-0)

For Walls, the table 3 shows the reinforcement steel for the walls located in Block 1 (first 3 levels) as only these levels enter the inelastic range.

Table 3. Reinforcing steel for reinforced concrete walls.

SECTION
MATERIAL
LOC.
STEEL
BAR
DIAMETER
(mm)
STEEL
DISTRIBUTION

Wall T1
Concrete 280 kgf/cm²
A-4
A615 Grade 60
9.5 - 25.4
(30-36) / 3/8" @10cm

Wall T2
Concrete 280 kgf/cm²
B-6
A615 Grade 60
9.5 - 19
(30-36) / 3/8" @10cm

Wall TC
Concrete 280 kgf/cm²
D-4
A615 Grade 60
9.5 - 19
(74-32) / 3/8" @15cm

Analysis of Results

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

Figure 2 shows the capacity curves in the X and Y directions of a structure, revealing that the capacity in the X direction is greater than in the Y direction, as the blue curve reaches a higher base shear. The ultimate displacements are 53 cm in the X direction and 51 cm in the Y direction, indicating that the structure can displace more in the X direction before reaching its maximum capacity. Both curves exhibit an initially linear behavior followed by a nonlinear phase, typical of plastic deformations. In summary, the structure is more resistant and can displace more in the X direction, reflecting differences in structural stiffness and strength in each direction.

Fig. 2. Capacity curves in the X and Y direction.

Fig. 2. Capacity curves in the X and Y direction.

To validate the obtained capacity curves, we compared our results with from [1], which present capacity curves for buildings with 3 to 8 stories in both directions. The general shapes, maximum base shear, and ultimate displacements are consistent. We verified characteristic parameters such as base shear capacity at yield and ultimate displacement, finding a good match. The base shear coefficients and ductility factors also reasonably match the values from the paper. The strength and
stiffness in the X and Y directions of our analysis show similar trends and magnitudes to those in the paper. Finally, our nonlinear analysis methodology is consistent with that described in the paper, reinforcing the validity of our results.

Seismic Performance according to FEMA 440. Figure 3 illustrates the seismic performance of an 18 -story structure in both X and Y directions, marking performance points for Moderate Earthquake (ME), Design Earthquake (DE), and Maximum Considered Earthquake (MCE). The curve shows that while the structure behaves near-elastically under ME, it transitions to highly nonlinear behavior under MCE. The capacity is similar in both directions but slightly better in Y. All performance points are below the ultimate spectral displacement, indicating a safety margin even under severe seismic scenarios. Nonlinear static analysis revealed that at a moderate earthquake (Step 4), the X-displacement was 11.52 cm , slightly above the expected 11.17 cm , but with a drift of 0.00365 , below the international limit of 0.005 . For the design earthquake (Step 7), the horizontal displacement was 24.00 cm , exceeding 22.18 cm , and the drift of 0.0071 was marginally above the Peruvian limit of 0.007 . Under the maximum considered earthquake (Step 10), the displacement reached 36.78 cm , higher than 35.12 cm , with a drift of 0.0100 . Despite these displacements, the structure remains safe, with the X-direction walls on the first level showing a demand-capacity ratio of 0.216 , well below the critical limit of 1 , indicating robust performance even under severe conditions.
"In this section, the guidelines outlined in the FEMA 440 standard[7] were followed."

Fig. 3. Capacity Espectrum.

Fig. 3. Capacity Espectrum.

Seismic Performance according to SEAOC. The figure 4 shows a bilinear capacity curve for a structural model, illustrating the relationship between lateral displacement and base shear. The structural performance levels, from Immediate Occupancy (IO) to Collapse Prevention (CP), are color-coded. Three seismic performance points are highlighted: moderate earthquake ( 11.17 cm ), design earthquake ( 22.18 cm ), and maximum considered earthquake ( 35.12 cm ). The curve features an initial elastic phase and a post-yield phase, with a maximum capacity of 52.80 cm and 1734 tons. The transition to nonlinear behavior occurs at 10.05 cm displacement, demonstrating the structure's ductility. This analysis is essential to understand structural behavior under different seismic intensities and evaluate its resistance capacity.
"In this section, the guidelines outlined in the SEAOC standard[8] were followed."

Fig. 4. Bilineal Model for SEAOC Methodology.

Fig. 4. Bilineal Model for SEAOC Methodology.

"The nonlinear analysis and the results presented, such as capacity curves, forces, displacements, and others, were obtained and calculated using the ETABS software[9]."

Conclusions

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

The 18 -story building showed good structural behavior under moderate (ME) and design (DE) earthquakes, with horizontal displacements slightly exceeding expected values but within acceptable limits. For the maximum considered earthquake (MCE), although displacements were greater, the structure proved to be safe, with a demand-capacity ratio well below the critical limit, indicating robust performance even under severe conditions.

The analysis revealed a bilinear capacity curve illustrating the relationship between lateral displacement and base shear. The structure showed an initial elastic phase followed by a post-buckling phase, with a maximum capacity of 52.80 cm and 1734 tons. The transition to nonlinear behavior occurred at a displacement of 10.05 cm , demonstrating the structure's ductility.

Both methodologies were used to evaluate the building's seismic performance. The seismic performance points under different seismic intensities confirmed that the structure has adequate safety margins, even under severe seismic scenarios, supporting the validity of the nonlinear analysis methods employed.

Based on the results, the implementation of performance-based design methodologies is recommended to improve the seismic resilience of tall buildings in Peru. Incorporating nonlinear analyses in the design process can provide a better understanding of structural behavior under different seismic scenarios and help identify and mitigate potential vulnerabilities.
Furthermore, the analysis demonstrated that the baseline shear capacity and the structural displacements are consistent with values observed in previous studies, which supports the reliability of the models and methods used. The comparison with capacity curves from other studies showed a good match in terms of overall shape, maximum baseline shear capacity, and ultimate displacements.

Finally, the study highlights the importance of considering both local regulations and advancements in seismic design methodology to ensure the safety and durability of tall structures in high seismic activity areas. The adoption of innovative materials and advanced analysis techniques can significantly contribute to improving the seismic performance of buildings, thereby reducing the risk of damage and losses during seismic events.

References

The original version of this paper is available on https://www.scientific.net/EI.13.113.pdf

Malik, U. J., Najam, F. A., Khokhar, S. A., Rehman, F., & Riaz, R. D. (2023). Advancing seismic resilience: Performance-based assessment of mid-rise and high-rise engineered cementitious composite (ECC) Buildings. Case Studies in Construction Materials, 1, 20.

Ramos, L., & Hubeb, M. (2021). Seismic response of reinforced concrete wall buildings with nonlinear coupling slabs. Engineering Structures, 234, 13.

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Mehmood, T., Warnitchai, P., & Suwansaya, P. (2016). Seismic Evaluation of Tall Buildings Using a Simplified but Accurate Analysis Procedure. Journal of Earthquake Engineering, 1-26.

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Keywords Index

1

100 Kwp Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

A

Ann Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

B

Bacterial Isolation Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Bacterial Strain Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Battery Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

Biofouling Bacterial Strain Identification from Drinking Water in Water Treatment Systems

C

Climate Change Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility

Compressive Strength Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

D

Design Optimisation Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

Digitized Roof Area Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Drinking Water Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Dry Density Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

E

Educational Institute Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

Efficiency Improvement Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

Electromagnetic Meter Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

Environmental Adjust-ment Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility

Environmental Improvement The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

F

Fema 440 Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Flexural Strength Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

G

Geographic Information System Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Green Infrastructure The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

Green Roof The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

Grid- Interfaced Rooftop Solar Plant Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

I

Infiltration Rate Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

L

Laboratory Experiment Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility; The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

Laterite Nickel Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

M

Mobilized Thermal Energy Storage (m-tes); Phase Change Materials (pcm); Pcm Phases; Economic Analysis Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

N

Nonlinear Analysis Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

O

Operational Effectiveness Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

P

Pervious Concrete Pavement Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

Post Mining Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

Power Quality Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

Pressure Gauge Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

Pv Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

R

Rainfall Data Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Rainwater Harvesting Potential Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Residual Pressure Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

S

Seawater-based Community Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Seismic Design Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Seoac Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Soil Fertility Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

Spatial Analysis Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Standalone Hybrid System Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

Sustainable Development Goals(sdgs) Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility; The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

T

Tall Buildings Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Technology Selection Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

U

Ultrasonic Meter Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

V

Vector Grid Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

W

Water Demand Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

Water Treatment Systems Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Wecs Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques

Authors Index

B

Bolong, N. Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

C

Casas, J. Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

Chang, F. Exploring Environmental Sustainability Research Using Experimental Methods - Taking the Vegetated Swales Facility; The Concept of Optimizing the Environment and Moving Towards Sustainable Development Goals with Green Infrastructure

D

Daaboul, M. Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

Dave, V. Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques; Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

Delgadillo, R. Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

H

Huda, N. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Huynh, T. Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

I

Imad, R. Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

K

Kamaludin, R. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Kumawat, H. Optimizing Power Quality in Hybrid Renewable Energy Systems through Advanced Intelligent Control Techniques; Optimizing Grid-Interfaced Rooftop Solar Plant Installation for Sustainable Energy Generation

L

Lacsado, M. Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Lah, Z. Water Supply for Lawas Community: Investigating Demand and Enhancing Distribution Efficiency

Lahoud, C. Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

Lam, T. Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

Liew, C. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Lucana, A. Evaluation of the Seismic Performance of a High-rise Building in Lima, Peru, Using the FEMA 440 and SEOAC Methodologies

M

Malek, N. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Maryam, S. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Montecastro, D. Assessing Rainwater Harvesting Potential in Seawater-Based Communities Using the Case Study of Poblacion Sitangkai, Philippines

Muhammad, M. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Mustafa, M. Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

N

Ngo, V. Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

Nguyen, D. Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

Ninh, H. Laboratory Evaluation of Load-Bearing Behavior and Drainage Ability of Pervious Concrete Pavement

O

Ojo, S. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Othman, M. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

P

Pachri, H. Analysis of Soil Fertility and Heavy Metals Contamination on Post-Mining Laterite Nickel Southeast Sulawesi, Indonesia

Parvin, A. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

Puteh, M. Bacterial Strain Identification from Drinking Water in Water Treatment Systems

R

Rishmany, J. Simulation and Economic Analysis of a Mobilized Thermal Energy Storage System for Mediterranean Climate Buildings: Case Study

S

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