Engineering Innovations Volume 17

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Volume 17 of
Engineering Innovations
ISSN print 2813-0995
ISSN web 2813-1002

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Table of Contents

Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Samuel Odiri Obakanure, Ebenezer Ayoola Agboola, Badmus Alao Tayo, Oluwaseun Temitope Faloye, Ayodele Ebenezer Ajayi

A Review on Biochar in Agriculture: Production, Applications, and Impacts

Badmus Alao Tayo, Ayodele Ebenezer Ajayi, Oluwaseun Temitope Faloye, Ayoola Olawole Jongbo

Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Ochuko Mary Ojo, Omoyele F. Abayomi

Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Pawenary Pawenary, Hari Purnomo, Winda Nur Cahyo

A Study on Environmental Sensors for Low-Cost Weather Stations

Irvan Budiawan, Naufal Ananda, Haryas Subyantara Wicaksana, Azka Muji Burohman, Faqihza Mukhlish, Estiyanti Ekawati, Deddy Kurniadi

Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Henry Osorto, Javier Enrique Delcid Carrasco

Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

José Francisco Deras Pérez, Alicia María Reyes-Duke, Héctor Fernando Villatoro

Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

Raden Bagus Seno Wulung, Aida Nur Mashito Putri, Justinus Sigit Purwoko

The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Mohamad Fajar Natawijaya, Tota Pirdo Kasih

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/engineering-innovations-vol-17/978-3-0364-3042-3

Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 1-17

doi: 10.4028/p-4EHWUu

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

Submitted:
2025-06-20

Revised:
2025-08-19

Accepted:
2025-09-25

Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

The original version of this paper is available on https://www.scientific.net/EI.17.1.pdf

Samuel Odiri Obakanure , Ebenezer Ayoola Agboola , Badmus Alao Tayo , Oluwaseun Temitope Faloye , Ayodele Ebenezer Ajayi

¹Department of Agricultural Engineering, Federal University of Technology, Akure, Nigeria

Department of University of Calaba, Nigeria

Departnent of Water Resources, Federal University of Oye, Oye-Ekiti, Nigeria

obakanureso@futa.edu.ng, b*eaagboola@futa.edu.ng, faloye.temitope@gmail.com, aeajayi@futa.edu.ng

Keywords: banana peels, banana bunch stalks, pyrolysis, biochar, soil amendment, crop yield.

Abstract

Soil degradation in agriculture represents a significant global challenge, primarily caused by nutrient depletion, erosion, and the loss of organic matter. A potential solution involves amending the soil to improve some properties that impact resistance to erosion, while also enhancing nutrient availability. Biochar has been widely used for soil amendment. This study evaluated the impact of biochar obtained from the slow pyrolysis of banana peels and banana stalks on some soil hydrophysical properties. The parameters assessed were: water retention in coarse-textured soils, swell-shrinkage characteristics in fine-textured soils. The experiment's findings indicated that banana peel biochar (BP) is slightly alkaline with a pH of 9.05 . It was also rich in potassium ( ) and phosphorus , with a relatively high carbon content ( ). In contrast, banana stalk biochar (BS) had a higher pH (9.50), lower carbon content (37.89%), and relatively lower levels of potassium ( 83.30 ) and phosphorus ( ). The fibrous structure of the stalk resulted in a more porous biochar. While banana peel biochar provides immediate nutrients to plants, banana stalk biochar is better suited for moisture retention and long-term carbon storage. Both types of biochar improved water retention, particularly in coarse-textured soils. Notably, banana stalk biochar outperformed banana peel biochar regarding surface area, porosity, cation exchange capacity (CEC), and moisture retention, enhancing the soil's water-holding capacity by up to . This characteristic makes it particularly effective for sandy soils susceptible to water and nutrient leaching. Additionally, banana stalk biochar was more effective in mitigating swell-shrink behavior in fine-textured soils, contributing to greater aggregate stability and reduced volume fluctuations. Amending fine sand with 3% of either biochar type did not significantly enhance total porosity; however, there was a significant increase ( ) in volumetric water content at 0 kPa . The results indicated that retained the most water, with an value of 0.7599 , followed by BP10% at 0.745 , demonstrating that higher application rates of biochar correlate with improved water retention. The study also revealed a negative correlation between soil suction and water retention, indicating that as suction levels rise, the soil's ability to retain water decreases. This relationship is vital for agricultural practices, irrigation planning, and understanding soil hydrology. By leveraging these findings, farmers and land managers can optimize irrigation strategies, enhance crop yields, and adapt to changing environmental conditions.

1. Introduction

The original version of this paper is available on https://www.scientific.net/EI.17.1.pdf

Soil degradation in agriculture is a pressing global issue, driven by factors such as nutrient depletion, erosion, and loss of organic matter. One solution is adding biochar made from different feedstock (including banana peels) to the soil to improve the physico-chemical properties of the soil and help recovery of pore functions [1.] Traditionally seen as waste, banana peels and stalks are now valued for their bioactive compounds. Banana stalks are the long, flexible part that carries the bunch of bananas.

Often called a peduncle, or sometimes an inflorescence stalk [2.]. Banana stalks are now applied in fiber production, bioenergy, and livestock feed. As the world shifts toward sustainability, both banana peels and stalks, when pyrolyzed, offer promising opportunities for economic growth, environmental preservation, soil structure enhancement, and improved water retention [3]. Biochar, a carbon-rich material produced through pyrolysis, offers a sustainable method for enhancing soil health and productivity [4]. Its benefits include improving soil structure and nutrient availability and boosting water retention, while also serving as a carbon sink. As a soil amendment material, biochar has gained significant attention for its role in promoting soil health and addressing climate change by sequestering carbon, improving nutrient retention, and supporting long-term soil sustainability [5].
Pyrolysis, a thermochemical decomposition process for carbon-rich materials, has significant benefits from a sustainability standpoint, utilizing various feedstock while emitting fewer greenhouse gases than conventional incineration [6.]. Since biomass-derived is re-captured during plant growth, pyrolysis can achieve net-zero emissions. The effectiveness and yield of pyrolysis depend on several operating conditions, primarily temperature, heating rate, and residence time, which dictate product composition. Slow pyrolysis heats materials at rates of per minute, lasting from minutes to hours, allowing reactions to reach equilibrium and yielding more solid char products. In contrast, fast pyrolysis, with rates exceeding per minute and reaction times as short as two seconds, favors liquid bio-oil production with minimal secondary reaction [7]. These differences in heating rates and time produce distinct product distributions. Biochar from slow pyrolysis also holds potential as a biofuel and soil enhancer [8]. On the other hand, fast pyrolysis is frequently employed for bio-oil extraction, though it also produces biochar suitable for soil amendment and other applications, as demonstrated in studies of rice husk, elm, and meranti wood biochar [9].
The assessment of soil linear shrinkage and cracking, particularly in fine-textured soils, is according to the Atterberg limits, such as the liquid limit (LL), and modulus-based evaluations. These properties are critical in understanding soil behaviour during the wetting and drying cycle, associated with rain and the dry season. Drying results in volume reduction and crack formation, whereas during wetting, the soil expands in volume. The liquid limit relates to the soil's plasticity, while modulus-based approaches evaluate its mechanical response to shrinkage. Water retention is essential in soil, influencing plant health, soil erosion, and water management. Coarse-textured soils, such as those with sand, typically have low water retention, which is particularly problematic for agriculture in arid and semi-arid areas where water is scarce [10,11]. In agriculture, soil amendments are key to enhancing soil physicochemical properties and fertility, increasing crop productivity, and supporting sustainable practices. Thus, the purpose of this study was to investigate and compare biochar derived from slow pyrolysis of banana peels and banana stalks as soil amendments, focusing on their effects on soil physicochemical properties, including shrinkage and cracking behavior.

2. Materials and Methods

The original version of this paper is available on https://www.scientific.net/EI.17.1.pdf

2.1 Feedstock Selection and Availability in Nigeria

Nigeria is among the top producers of bananas globally. In 2022, the country produced approximately 8.02 million metric tons of bananas, marking an increase from the previous year [12.]. This production level positioned Nigeria as the fourth-largest banana producer worldwide. Banana cultivation is predominantly practiced by smallholder farmers in the southern states, including Oyo, Ogun, Osun, Lagos, Delta, and Rivers. These farmers often integrate banana plants into mixed cropping systems, reflecting traditional agricultural practices [13.]. Bananas play a vital role in Nigerian cuisine and daily life. A notable example is "Boli" (also spelled "Bole"), a popular street food consisting of roasted plantain or banana. In southwestern Nigeria, Boli is commonly consumed with groundnuts, while in the south-south region, it's often paired with grilled fish. This delicacy is widely sold by street vendors and is integral to local food culture. Despite its prominence, banana cultivation in Nigeria faces several challenges: Banana Bunchy Top Virus (BBTV), limited adoption of improved varieties [14.], and pest and disease pressure. According to [15.], banana cultivation in Nigeria results in substantial agricultural. residues, notably banana peels and stalks, which have
significant potential as feedstock for biochar production that could be used in soil amendment. Nigeria, being one of the leading banana-producing countries, generates considerable quantities of these residues [16.]. Given the country's substantial banana production, large volumes of peels and stalks are discarded annually [17]. Utilizing these residues for biochar production offers a sustainable solution to manage this waste effectively [18.].

2.2 Feedstock Collection and Preparation

A total of 25 kg of banana peels and 20 kg of banana bunch stalk were collected from roadside roosted plantain sellers and Obaja farms, off Ilesa garage, Akure. These materials were pre-prepared and transported to the laboratory where they were washed and sorted to remove contaminants before sun drying for approximately two months to achieve a moisture content of as shown in Plate 1.

Plate 1. Dried feedstock after sun drying.

Figure 1. Plate 1. Dried feedstock after sun drying.

The moisture content of the banana peels and stalks was calculated using Equation 1. Fresh banana peels and stalks were collected, cleaned, and cut into smaller pieces to ensure uniform drying. A known mass of each sample was weighed using a digital balance. The samples were dried in an oven set at for 24 hours until a constant weight was achieved. After cooling in a desiccator, the dried samples were reweighed. At for 24 hours, the moisture content was found to be for banana peels and for banana stalks. After the initial moisture content was determined, the banana peels and stalks were sun-dried to moisture content.

Where: W1 is the Initial wet weight of the sample (g), and W2 is the Final dry weight of the sample (g)

2.3 Biochar Production

The sun-dried banana peels and banana stalks were pyrolyzed in a muffle furnace (kiln) under limited oxygen conditions in batches. The materials were slowly pyrolyzed at an average temperature of for 2 hours using a laboratory-scale pyrolyser unit. Slow pyrolysis promotes higher retention of nutrients and the formation of stable carbon structures [19.]. After the biochar (banana peel biochar BPB , banana stalk biochar BSB) was produced in each batch, they were allowed to cool for 4 days, sieved to obtain particles less than 2 mm in diameter, and stored in airtight containers (Plate 2).

2.4 Analysis of the Biochar

Proximate and ultimate analysis of sieved biochar was carried out in the Crop, Soil, and Pest Department laboratory of the Federal University of Technology, Akure, to determine the chemical composition and ash content of each biochar (BPB and BSB), offering insight into the quality of the resulting biochar. The micro- Kjeldahl method (AOAC, 1995) was employed to determine nitrogen content. This method consists of three stages: digestion, distillation, and titration. The digestion process involves converting the nitrogen in the sample into ammonium sulphate by boiling it with concentrated sulphuric acid . The total nitrogen content of the sample was calculated using the formula provided in Equation 2.

Where: V1 is final volume of the digest, V2 is the volume of the digest used in the distillation and Wt is the weight of the sample taken.
The percentage ash content was calculated using Equation 3. The crucibles were then removed from the furnace and transferred into a desiccator to cool before reweighing (W3) AOAC, (1995).
Percentage ash content was calculated as Ash

Where: W1 is the weight of empty crucibles, W2 is the weight of crucibles and samples, W3 is the weight of crucible and ash samples.
The Walkey-Black method was used for determining the organic carbon content in biochar. The method involves oxidizing organic carbon with potassium dichromate in the presence of sulphuric acid . Organic carbon content was calculated using Equation 4.
Organic carbon

Where: A is the volume of , B is the volume of is the equivalent weight of carbon, V is the volume of Ortho-phenanthroline and W is the weight of sample.
Mineral analysis was conducted on the solution obtained from dry asheing. The minerals analysed included calcium, iron, potassium, zinc, magnesium, and sodium. Phosphorus was determined calorimetrically using the phosphovanadomolybdate method (AOAC, 1995).
The colorimetric determination of phosphorus was done using vanadomolybdate method. The Vanado molybdate solution was prepared from 20 g of ammonium molybdate ( NH 4 ) 6 MO 7.4 H 2 O which was dissolved in 200 ml of hot water and cooled. One gram of ammonium metavanadate was dissolved separately in 120 ml of water, cooled and 140 ml conc. HNO 3 was added in a fume cupboard, molybdate solution was gradually added to the vanadate solution and dilute to 1 litre. 10 mm of sample solution was pipetted into 50 ml volumetric flask and 10 ml of Vanado-molybdate reagent was added with few ml of distilled water mixed and diluted to the volume with water and allowed to stand for 10 minutes. The absorbance of the sample solutions was determined at 470 nm and phosphorus content were expressed in sample.

Plate 2. The biochar produced (a) BPB (b) BSB.

Figure 2. Plate 2. The biochar produced (a) BPB (b) BSB.

2.5 Soil Sample Collection and Preparation

Two soil samples: coarse-textured and fine-textured soil were collected at depths from adjacent farmland for amendment. The samples were transported in a polythene bag to the soil and water laboratory where they were air-dried for a few days and were sieved to eliminate large aggregates and achieve an even distribution of particle sizes. A 2.0 mm sieve (No. 10 mesh ) was used for the sandy soil, while a sieve (No. 200 mesh) was used for the clay soil. After sieving, soil samples
were treated with each biochar BPB and BSB at three different rates ( 3,5 , and ) according to literature along with control (no Biochar). Thus, the treatments were BP0, BP3, BP5, BP10, BS0, BS3, BS5, and BS10. 5 replicates of each treatment and control were prepared and stored in a 100cl container for further analysis.

2.6 Water Retention Test

Soil moisture retention properties were measured to characterize the effects of biochar on field capacity, wilting point, and plant-available water. The Water Retention Curve (WRC) was determined using the Standard Funnel Test (1986). The WRC illustrates how soil retains water at different suction levels, which is vital for characterizing and understanding some soil-water interaction properties such as field capacity, wilting point, and available water content [20.]. The gravimetric water content ( ) at each suction level was calculated using Equation 4

2.7 Determination of Field Capacity, Bulk Density, and Porosity

At a suction of and -100 , the filed capacity, bulk density and the porosity of the soil amended were determined using equations

2.8 Determination of Soil Linear Shrinkage and Cracking

Linear shrinkage (Equation 8) refers to the percentage reduction in the length of a soil sample as it dries from a plastic state to a completely dry state. The liquid limit (LL), which marks the water content at which soil transitions from a plastic to a liquid state, is often correlated with linear shrinkage, with soils that have high liquid limits typically showing a greater shrinkage potential. Linear shrinkage is directly related to the soil's liquid limit, with higher liquid limit values typically leading to greater shrinkage [21.]. As the soil shrinks, crack formation can be quantified using techniques such as digital image analysis, which assesses crack width, density, and total area [22]. This method offers a more comprehensive understanding of the relationship between the soil's shrinkage properties and its cracking behaviour. For the linear shrinkage measurement, a soil paste was prepared and was filled into a standard shrinkage Mold ( 140 mm long). The surface was smoothed and allowed to dry at room temperature, then oven-dry it at until constant mass. The final length of the dried sample was measured. Equation 8 can be used to calculate the linear shrinkage.

2.9 Data Analysis

Microsoft Excel and ANOVA (Analysis of Variance) was used to analyze the experimental data. The collected data from soil amendment trials involving biochar from banana peels and bunch stalks was computed into Excel spreadsheets. These included parameters such as soil pH , nutrient content ( N , ), and the linear shrinkage and average cracking width. Excel's built-in functions was used to compute descriptive statistics for each treatment group. For inferential analysis, one-way ANOVA was used, using Excel's Data Analysis Toolpak to determine whether there were statistically significant differences between the effects of the two types of biochar on soil properties and plant performance. The ANOVA results helped identify if the observed differences between treatments were due to chance or actual variation in biochar efficacy. Where significant differences were found ( ), post-hoc comparisons were considered to further interpret the findings.

3. Results and Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.1.pdf

3.1 Physicochemical Properties of BPB and BSB

3.1.1 Biochar Properties

As shown in Figure 1, BPB had a pH of 9.05, indicating slight alkalinity, and was rich in potassium ( ) and phosphorus ( ). It also had a relatively higher carbon content ( ) compared to BSB, as illustrated in Figure 1. In contrast, BSB had a higher pH (9.50), lower carbon content ( ), and reduced levels of potassium ( ) and phosphorus ( ) as seen in Figure 1.

Fig. 1. Chemical compositions of the banana peel and banana stalk biochar.

Fig. 1. Chemical compositions of the banana peel and banana stalk biochar.

3.1.2 Substrate Properties

Both types of biochar increased soil pH , thereby reducing acidity, with BSB having a more immediate effect on raising pH , which is particularly beneficial for acidic soils. BSB also significantly boosted potassium and phosphorus levels in the soil, enhancing nutrient uptake for plants [23]. BPB had a more immediate impact on nutrient release, whereas BSB contributed to long-term soil conditioning owing to its carbon content and porosity [24.]. In terms of cracking widths, the controls ranged from 0.26 cm to 0.35 cm . The BP3 (Figure 2) displayed slightly smaller cracking widths, between 0.23 cm and 0.30 cm . The BP5 and BP10 series showed progressively lower cracking widths, with the BP10 recording the smallest widths as low as 0.10 cm . The performance of the BP10 indicates superior resistance to shrinkage and cracking, suggesting enhanced durability and stability, which is particularly desirable in materials where controlling shrinkage is critical, such as in concrete or ceramics. The BP5 (Figure 2) moderately improved both shrinkage and cracking compared to the control and BP3, suggesting improved structural integrity relative to the controls. On the other hand, the control and BP3 had higher shrinkage and larger cracking widths, suggesting they are more susceptible to shrinkage-related issues, such as cracking or deformation under certain conditions. Recent studies, such as those by [25.], indicate that incorporating additives like fibre reinforcements, polymers, or pozzolanic materials can significantly reduce shrinkage and cracking. The observed improvements in the BP5 and BP10 (Figure 2) may be linked to such modifications, which help mitigate shrinkage by enhancing internal bonding or minimizing water loss during curing [21; 26.]. Furthermore, BP10's lowered shrinkage and cracking width align with contemporary advancements in sustainable material design, aiming to improve performance under environmental stresses. According to [27], there is a direct relationship between crack width in soil and its structural integrity-wider cracks often indicate weaker, more brittle soil. Therefore, the smaller crack widths observed in BP10 and BS10 samples suggest that these soils have better structural strength, greater
durability, and are more resistant to external forces such as weathering or mechanical stress. Figure 3, the C ( C 1 to C 5 ) shows shrinkage between and , indicating high shrinkage. The BS3 (BS3,1 to BS3,5) has linear shrinkage between and , with results similar to the C. The BS5 (BS5,1 to BS5,5) demonstrates a significant reduction in shrinkage, ranging from to 5.22%.

Fig. 2. Influence of the control, banana peels, and banana stalks biochar on the linear shrinkage.

Fig. 2. Influence of the control, banana peels, and banana stalks biochar on the linear shrinkage.

Fig. 3. Influence of the control, banana peels and banana stalks biochar on the average cracking width.

Fig. 3. Influence of the control, banana peels and banana stalks biochar on the average cracking width.

3.1.3 pH and Electrical Conductivity (EC)

The pH and electrical conductivity (EC) of the banana stalk biochar and the banana peel biochar were 9.50 and 9.05 (Figure 1). The higher pH of banana stalk biochar suggests a better potential as a liming agent, which can help reduce soil acidity and create a more conducive environment for root growth and microbial activity. In terms of electrical conductivity, banana stalk biochar exhibited higher values ( ) compared to banana peel biochar ( ). This indicates that banana stalk would better enhance nutrient retention and cation exchange. A higher EC and cation exchange capacity (CEC) imply that banana stalk biochar can retain more water, while banana peel biochar is better at holding nutrients. This is particularly advantageous in coarse-textured soils that are prone to nutrient leaching. Furthermore, a higher pH contributes to the stabilization of soil structure, enhancing both water infiltration and retention [6, 28.]

3.1.4 Moisture Retention Capacity

The addition of the banana stalk biochar in sandy soils enhanced the soil's water-holding capacity by , while banana peel biochar increased it by . The differences in water retention between the two types of biochar can be linked to their distinct physicochemical characteristics, such as surface area, porosity, and ash content (Table 1-7). The banana stalk biochar's superior performance could be attributed to its higher surface area and porosity, which facilitate the creation of a stable microenvironment within soil pores. Additionally, its higher cation exchange capacity (CEC) aids in water molecule adsorption, minimizing water loss through evaporation and drainage. As noted by [29.], biochars with greater CEC and larger surface areas are generally more effective at retaining water, thus reducing the need for frequent irrigation in sandy soils.

3.1.5 Impact on Soil Hydraulic Conductivity

The hydraulic conductivity of the sandy soil was measured using Constant Head Permeability Test. A soil sample was placed in a permeameter. Water was allowed to flow through the sample under a constant head. The volume of water ( Q ) collected over a known time ( t ), length of the soil sample ( L ), and cross-sectional area (A) was measured and calculated using Equation 9, below where is the constant head difference (cm). The addition of banana stalk biochar led to a reduction in hydraulic conductivity of sandy soil, while banana peel biochar resulted in a reduction (as shown in Tables 1-2). This decrease in hydraulic conductivity suggests a slower water movement through the soil profile, enhancing water retention—a trend noted by [11.]. The blocking of larger pores by biochar particles in sandy soils contributes to increased water-holding capacity by decelerating water infiltration rates. Such a reductihb. on in hydraulic conductivity is particularly advantageous for coarse-textured soils, which often have low water retention due to their larger pore spaces, supporting findings by [28.] Moreover, the ability of biochar- amended ,c loam soil to maintain constant saturated hydraulic conductivity after repeated wetting and drying indicates enhanced resistance to capillary stress. The banana stalk biochar's effectiveness in lowering hydraulic conductivity is related to its higher surface area and porosity, corroborating results from [23]. Who found that biochar amendments can improve water retention by altering soil pore architecture and decreasing water percolation rate.
Hydraulic conductivity ( k ) was calculated using Darcy's Law given as follows

Nutrient and Water Retention Synergy

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The nutrient retention capabilities of biochar significantly influence its effectiveness in enhancing water retention. Biochar derived from banana peels, characterized by its higher cation exchange capacity (CEC), excels at retaining vital nutrients such as nitrogen and phosphorus in the soil, thereby minimizing losses due to leaching. This synergistic relationship between nutrient and water retention highlights the value of banana peel biochar for enriching sandy soils while ensuring adequate moisture levels. By improving both water availability and nutrient content, this biochar type supports crop
growth in arid or nutrient-deficient environments. Research by [19], confirmed that biochars with high CEC enhance both nutrient and water availability, fostering a more sustainable soil ecosystem for agricultural production.

Table 1 Effect of banana peel control on gravimetric water content.

Samples (kPa) | Suction | Initial Soil Mass (g) | Water Drained (ml) | Dry Soil Mass (g) | Water Retained (g) | Gravimetric Water Content (%)
C1 | 0 | 150 | - | 100 | 50 | 50
C2 | -10 | 124 | 26 | 100 | 24 | 24
C3 | -33 | 114 | 10 | 100 | 14 | 14
C4 | -100 | 107 | 7 | 100 | 7 | 7
BP₃ | 0 | 150 | - | 100 | 50 | 50
BP₃ | -10 | 130 | 20 | 100 | 30 | 30
BP₃ | -33 | 120 | 10 | 100 | 20 | 20
BP₃ | -100 | 115 | 5 | 100 | 15 | 15
BP₅ | 0 | 150 | - | 100 | 50 | 50
BP₅ | -10 | 138 | 12 | 100 | 38 | 38
BP₅ | -33 | 128 | 10 | 100 | 28 | 28
BP₅ | -100 | 123 | 5 | 100 | 23 | 23
BP₁₀ | 0 | 150 | - | 100 | 50 | 50
BP₁₀ | -10 | 140 | 10 | 100 | 40 | 40
BP₁₀ | -33 | 132 | 8 | 100 | 32 | 32
BP₁₀ | -100 | 127 | 5 | 100 | 27 | 27

The gravimetric water content ( ) was at 0 kPa . Since the soil has water by mass, no suction is being used, and water fills every pore in the soil (Table 1). A considerable volume of water ( 26 g ) is lost by the soil when suction rises to -10 kPa . Larger pores have drained, leaving only water in the finer pores, as seen by the gravimetric water content dropping to . With a suction of -33 kPa (Table 1), the soil releases more water, resulting in a water content of . This represents the soil moving towards the wilting point, which is the stage where water is increasingly difficult for plants to extract. At - 100 kPa , only of the soil's mass is water, indicating that most of the water has been drained. According to Table 1, the gravimetric water content was at water content denotes significant moisture availability, which is characteristic of saturated soils. The soil has retained 38 g of water and lost 12 g at -10 kPa . The soil is probably at field capacity when the gravimetric water content falls to . This condition is the maximum amount of water that the soil can retain against gravity and is easily accessible to plants. The soil retains 28 g of water after draining an extra 10 g of water when the suction rises to -33 kPa (Table 1). With a gravimetric water content of as a result, the soil is getting close to its wilting point. At this stage, water becomes more difficult for plants to extract, suggesting that the soil moisture is starting to stress plants. At- 100 kPa , the soil loses 5 g of water and retains 23 g . Tension in the soil moisture, which indicates how firmly water is retained in the soil against gravity, is shown by the negative suction pressure values.
3). Dryer soil conditions are correlated with higher suction values (more negative). While the dried soil mass stays at 100 g , the beginning soil mass fluctuates significantly between samples. This suggests that variations in water content have a direct impact on the overall mass of soil. The soil's moisture content can be inferred from the difference between the original and dry soil masses. As suction increases (becomes more negative), less water is lost. This suggests that the soil is holding onto more liquid at greater suction pressures because less water is accessible to drain from the soil. Water retained decreases with increasing suction. Initially, at 0 kPa suction, the soil retains 50 g of
water (Table 1). As suction increases to -100 kPa , the retained water drops to 27 g . This trend indicates that as the soil dries, it holds onto less water due to the increased tension. The gravimetric water content percentage also decreases as suction increases. Starting from at 0 kPa , it decreases to at -100 kPa (Table 1).

Table 2 Effect of banana stalk biochar on gravimetric water content.

Samples | Suction (kPa) | Initial Soil Mass (g) | Water Drained (ml) | Dry Soil Mass (g) | Water Retained (g) | Gravimetric Water Content (%)
C1 | 0 | 150 | - | 100 | 50 | 50
C2 | -10 | 124 | 26 | 100 | 24 | 24
C3 | -33 | 114 | 10 | 100 | 14 | 14
C4 | -100 | 107 | 7 | 100 | 7 | 7
BS3 | 0 | 150 | - | 100 | 50 | 50
BS3 | -10 | 130 | 20 | 100 | 30 | 30
BS3 | -33 | 120 | 10 | 100 | 20 | 20
BS3 | -100 | 115 | 5 | 100 | 15 | 15
BS5 | 0 | 150 | - | 100 | 50 | 50
BS5 | -10 | 139 | 11 | 100 | 39 | 39
BS5 | -33 | 130 | 9 | 100 | 30 | 30
BS5 | -100 | 125 | 5 | 100 | 25 | 25
BS10 | 0 | 150 | - | 100 | 50 | 50
BS10 | -10 | 143 | 7 | 100 | 43 | 43
BS10 | -33 | 133 | 10 | 100 | 33 | 33
BS10 | -100 | 129 | 4 | 100 | 29 | 29

The initial soil mass decreases from 150 g at 0 kPa to 115 g at -100 kPa (Table 2). The dry soil mass remains constant at 100 g , suggesting that the variations in initial mass result from water loss as suction increases. The highest water drainage occurs at -10 kPa , with 20 g lost, showing significant moisture release at low suction. As suction rises, drainage reduces to 10 g at -33 kPa and 5 g at -100 kPa , indicating increased water retention as suction strengthens. Water retained decreases consistently with increasing suction, starting from 50 g at 0 kPa and falling to 15 g at -100 kPa (Table 4). This trend clearly indicates that as the soil dries out, its capacity to hold onto water diminishes. The gravimetric water content also decreases as suction increases, beginning at at 0 kPa and dropping to at -100 kPa (Table 2). The initial soil mass declines from 150 g at 0 kPa to 125 g at -100 kPa , while the dry mass remains 100 g , showing water loss.
Water drainage decreases as suction rises, from 0 g at 0 kPa to 11 g at at -33 kPa , and 5 g at -100 kPa . This pattern highlights how increasing suction reduces water movement, emphasizing the soil's ability to retain moisture under higher tension. This pattern suggests that higher suction leads to a greater retention of water, indicating that as the soil dries, it becomes increasingly difficult for water to be expelled. Water retained decreases with increasing suction, from 50 g at 0 kPa to 25 g at -100 kPa (Table 2). This decline highlights the effect of suction on water availability; as the soil becomes drier, it retains less water. The gravimetric water content also decreases with increasing suction, starting from at 0 kPa and falling to at -100 kPa (Table 2). The initial soil mass decreases slightly from 150 g at 0 kPa to 129 g at -100 kPa (Table 4). The dry soil mass remains constant at 100 g , indicating that the loss of mass is primarily due to water drainage. The difference in initial mass helps understand how much water was present initially. The amount of water drained increases as suction becomes more negative, from 0 g at 0 kPa to 10 g at -33 kPa (Table 4), and then decreases to 4 g at -100 kPa . This indicates that the soil drains more water initially, but as suction
increases further, less water can drain, possibly because the soil particles are holding onto water more tightly. The water retained by the soil decreases with increasing suction, starting at 50 g at 0 kPa and dropping to 29 g at -100 kPa (Table 4). The gravimetric water content also decreases as suction increases, beginning at and dropping to (Table 4). This is consistent with the principles of soil water retention, where higher suction results in lower moisture availability.

3.2 Morphological Properties of BPB and BSB

The morphological analysis of biochar derived from BPB and BSB reveals notable differences in surface area and porosity. Specifically, BSB have a larger surface area of , compared to 110 for BPB. Additionally, the biochar from banana stalks exhibited a more intricate porous structure, featuring both macro- and micropores, which are essential for effective water and nutrient retention. These findings align with [30.], who highlighted that biochars with greater surface areas and porosity enhance soil water retention due to improved capillary forces. The increased surface area and porosity of BSB suggest a possible superior ability to retain water within the soil matrix. The extensive pore network facilitates water adhesion through capillary action, thereby reducing water loss and improving moisture retention in coarse-textured soils.

3.2.1 EDX as a Function of Feedstock

Energy Dispersive X-ray Spectroscopy (EDX) was used to determine the elemental composition of biochar produced from banana peels and banana bunch stalks (Figure 4). After pyrolyzing these banana residues to form biochar, the samples were analysed using a Scanning Electron Microscope (SEM) coupled with EDX. The EDX spectrum confirms the presence of carbon and oxygen (Table 2.).

Fig. 4. Elemental composition of biochar produced from banana peels and banana bunch stalks.

Fig. 4. Elemental composition of biochar produced from banana peels and banana bunch stalks.

Table 3 Element and % composition of BPB and BSB.

Element Composition | BPB | BSB
Carbon (C) | 72.05 | 61.33
Oxygen (O) | 20.52 | 24.41
Potassium (K) | 4.28 | 1.15
Sulphur (S) | 0.01 | 3.27
Calcium (Ca) | 2.15 | 7.35
Phosphorous (P) | 0.82 | 0.03
Magnesium (Mg) | 0.15 | 0.01
Silicon | 0.02 | 0.02

3.3 Water Retention Curve

At a suction of 0 kPa (saturation), the soil reaches its maximum water-holding capacity, with all pores filled with water. As suction increases (e.g., -10 kPa or -33 kPa ), larger pores drain first, leaving only water retained in the smaller pores. Amendments with banana stalk biochar improved water retention in fine sand more effectively than banana peel biochar, even at the lowest application rates. However, the significance of this improvement varied depending on the type and rate of amendment (see Table 4). Generally, adding of any of the three biochar types did not significantly enhance total porosity. Nonetheless, total porosity (volumetric water content at 0 kPa ) increased significantly ( ). From Figures 5 and 6, among the treatments, the banana stalk biochar (BS10%) retained the most water, with an value of 0.7599 , followed by banana peel biochar (BP10%) with an of 0.745 , and banana stalk biochar (BS ) with an of 0.7309 . The banana peel biochar (BP5%) had an of 0.7188 , while both treatments (BS3% and BP3%) had an of 0.6243 . This indicates that increasing the biochar amendment rate correlates with greater water retention in the soil, aligning with findings by [24.]. From Figure 6, the residual water content ( ) is , meaning some moisture remains in the soil even under high suction. The saturation water content ( ) is , indicating the maximum water-holding capacity. The value ( 0.163 ) suggests moderate water retention before air entry. The n value (1.634) suggests that the soil has a relatively good balance between drainage and retention. From Figure 7, the residual water content ( ) suggests that about of the soil's weight remains as moisture even at high suction. The high n value (2.118) indicates good water retention and slow drainage, typical of finer-textured soils or soils treated with biochar. A being low ( 0.100 ) suggests that the soil retains water well before reaching air entry suction.

Table 4 Volume of water retained (%) as a function of the types and rates of amendments compared with the un-amended fine sand at the various matric potentials ( kPa ).

Suction (kPa) Ψ | C (%) | BP3% | BP5% | BP10% | BS3% | BS5% | BS10%
0 | 50 | 50 | 50 | 50 | 50 | 50 | 50
-10 | 24 | 30 | 38 | 40 | 30 | 39 | 43
-33 | 14 | 20 | 28 | 32 | 20 | 30 | 33
-100 | 7 | 15 | 23 | 27 | 15 | 25 | 29

Fig. 5. Result for banana peels biochar on gravimetric water content (control, , BP 10%).

Fig. 5. Result for banana peels biochar on gravimetric water content (control, , BP 10%).

Fig. 6. Result for banana stalk biochar on gravimetric water content (control, BS , BS , BS 10%).

Fig. 6. Result for banana stalk biochar on gravimetric water content (control, BS , BS , BS 10%).

Fig. 7. Water retention curve using banana peels and banana stalk biochar.

Fig. 7. Water retention curve using banana peels and banana stalk biochar.

Table 5 The ANOVA for the volume of water retained (%) as a function of the types and rates of amendments compared with the unamended fine sand at the various matric potentials ( kPa ).

Source of Variation | Sum of Squares (SS) | Degrees of Freedom (df) | Mean Square (MS) | F-Statistic | p-value
Between Groups | 371.21 | 5 | 74.24 | 4.12 | 0.046
Within Groups | 4206.25 | 18 | 233.68 | NaN | NaN
Total | 4577.46 | 23 | NaN | NaN | NaN

The sum of squares ( SS ) for between groups is 371.21 , with a mean square (MS) of 74.24. The SS for within groups is 4206.25 , and the mean square (MS) is 233.68 . The F-statistic of 4.12 suggests that there is little evidence to reject the null hypothesis that the means of the groups are equal. The pvalue of indicates a high probability that any observed differences are due to chance, suggesting that there is a slight statistically significant difference between the banana peel biochar (BP) and the banana stalk biochar (BS). Higher in BS10% suggests improved moisture retention (Figure 7). Lower values (BS10%) indicate stronger water retention in fine-textured soils. The n values indicate the pore size distribution, with higher values (BS10%) suggesting better water retention at low suctions.

3.3.1 Impact on Swell-Shrink Behaviour

The enhanced moisture retention of banana peel biochar effectively decreases the amplitude of the swell-shrink cycle, allowing soil to remain hydrated for extended periods, even in dry conditions. This buffering capacity reduces the contraction and expansion of clay particles, thereby minimizing soil cracking and structural damage. In contrast, banana stalk biochar, which has lower moisture retention, is less effective at moderating the shrink-swell behaviour of the soil. The differing behaviours of the two biochar types are influenced by their binding characteristics, which are also related to their chemical composition a [31.]. The interconnected pore network of banana peel biochar facilitates better water infiltration and retention, which buffers the typical shrink-swell dynamics observed in fine-textured soils. This is consistent with recent research by [30.], which found that biochars with greater porosity can reduce soil compaction and mitigate shrinkage effectively.

3.3.2 Swelling Index and Shrinkage Limit

The addition of banana peel biochar resulted in an reduction in the swelling index of finetextured soils, while banana stalk biochar contributed to a reduction. Additionally, banana peel biochar increased the shrinkage limit by , compared to a increase with banana stalk biochar. These findings indicate that banana peel biochar is more effective in addressing the adverse effects of swelling and shrinking in expansive soils due to its superior physicochemical properties. The lower shrinkage limit further illustrates the biochar's capacity to stabilize soil structure by preventing excessive contraction during drying. Similar findings were reported by [23], who noted that biochar amendments helped reduce swell-shrink cycles and improve soil stability in clayey soils.

3.3.3 Soil Aggregate Stability and Crack Formation

Soil aggregate stability is crucial for resilience against swell-shrink cycles. Soils treated with BPB demonstrated a improvement in aggregate stability compared to untreated soils, while those amended with BSB showed a improvement. The reduction in crack formation in banana peel biochar-amended soils indicates better preservation of soil structure, which is essential for minimizing water infiltration and protecting root systems. The addition of biochar alters the shape and orientation of soil aggregates, particularly as the application rate increases [11.] The enhanced stability offered by banana peel biochar helps maintain soil structure during wetting and drying cycles, thereby reducing crack formation typical in fine-textured soils. In contrast, the smoother surface and lower porosity of banana stalk biochar make it less effective in preventing soil degradation from swellshrink behaviour. These findings align with [23.], who reported that biochar amendments enhance aggregate stability and decrease crack formation in expansive soils.

Conclusion

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Biochar derived from banana peels and stalks shows significant potential as a soil amendment in agriculture. BPB enhances immediate nutrient availability, while BSB excels in moisture retention and long-term carbon storage. Both biochar types improve water retention, especially in coarsetextured soils. Notably, BSB outperformed its peel counterpart in terms of surface area, porosity, cation exchange capacity (CEC), and moisture retention, increasing the soil's water-holding capacity by up to . This effectiveness makes it particularly suitable for sandy soils prone to water and nutrient leaching. Additionally, BSB was more effective at mitigating swell-shrink behaviour in finetextured soils. Its superior characteristics contributed to enhanced soil aggregate stability and reduced fluctuations in soil volume. While amending fine sand with of any biochar type did not significantly increase total porosity, volumetric water content at 0 kPa saw a significant increase ( . The results revealed that retained the most water, with an value of 0.7599 , followed by BP10% at 0.745 , and so on, indicating that higher biochar application rates correlate with improved water retention. The study also highlighted a negative correlation between soil suction and water retention, suggesting that as suction levels increase, the soil's capacity to retain water diminishes. This relationship is crucial for agricultural applications, irrigation planning, and understanding soil hydrology. By utilizing these insights, farmers and land managers can enhance irrigation strategies, improve crop yields, and adapt to evolving environmental conditions.

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The original version of this paper is available on https://www.scientific.net/EI.17.1.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 19-40

doi: 10.4028/p-C4sOTt

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

Submitted:
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Accepted:
2025-09-18

A Review on Biochar in Agriculture: Production, Applications, and Impacts

The original version of this paper is available on https://www.scientific.net/EI.17.19.pdf

Badmus Alao Tayo , Ayodele Ebenezer Ajayi , Oluwaseun Temitope Faloye , Ayoola Olawole Jongbo

1Department of Agricultural and Bioresources Engineering, Faculty of Engineering and Technology, University of Calabar, Calabar PMB 1115, Nigeria

Department of Agricultural and Environmental Engineering, Federal University of Technology, Akure PMB 704, Nigeria

Programa de Pos-Graduacao em Zootecnia (PPZ), Universidade Tecnologica Federal do Parana, Campus Dois Vizinhos, 85660-000, Dois Vizinhos, Parana, Brazil

a*tayobadmus007@gmail.com, aeajayi@futa.edu.ng, dayoolajongbo@utfpr.edu.br

Keywords: biochar, pyrolysis, sustainable agriculture, climate, soil fertility, crop yield.

Abstract

Biochar, a carbon-rich material derived from biomass pyrolysis, is increasingly recognized for its potential in sustainable agriculture. Its unique physical and chemical properties enhance soil fertility, water retention, and nutrient availability, while also acting as a long-term carbon sink that mitigates greenhouse gas emissions. Despite these advantages, there remains a significant knowledge gap regarding its long-term agronomic impacts particularly on crop yield sustainability. Certain studies have observed a sustained increase in maize yield even ten years after a single biochar application, while others report no yield improvement after six years. This review examines biochar production processes, emphasizing how feedstock type and pyrolysis conditions influence its properties and agricultural performance. It synthesizes evidence on biochar's role in improving soil health, boosting crop productivity, supporting microbial activity, and enhancing resilience to climate variability. Furthermore, it critically assesses the environmental benefits, potential to reduce synthetic fertilizer dependency, and constraints related to cost, scalability, and adoption. By addressing the uncertainty surrounding long-term yield outcomes, his review clarifies biochar's role in climateresilient and sustainable farming systems and aims to guide future research and policy directions.

1. Introduction to Biochar and Its Agricultural Importance

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Maintaining agricultural productivity is a global challenge due to the rapid depletion of soil organic matter and nutrients. With the population exceeding 8 billion, modern agroecosystems must produce more food while protecting soil, the source of nearly of human food and vital ecosystem functions [140, 142, 141]. This challenge is intensified by climate change, evidenced by rising temperatures, increased atmospheric , and extreme events such as droughts [143, 144, 145]. In this context, agriculture must adapt to improve resource efficiency and productivity while minimizing environmental impacts. Biochar, with its broad application potential, emerges as a promising innovation to address soil degradation, climate challenges, and environmental concerns [146, 135]. Biochar is a permeable, carbon-rich material with high resistance to decomposition and a high degree of aromatization, produced by thermal decomposition of plant or animal biomass in low-oxygen conditions [88]. Its varied industrial and agricultural applications have drawn growing interest. Biochar, produced via thermal conversion of biomass under limited oxygen, is a carbon-rich material whose physicochemical properties are governed by its feedstock and production conditions and determine its diverse applications [8, 48, 115].

1.1 Relevance in Modern Agriculture

Biochar has gained attention as a soil amendment due to its cost-effectiveness and environmental benefits compared to chemical fertilizers, which can harm soil and plant health. It improves nutrient uptake, supports soil microbes, and enhances soil properties through nutrient cycling. Its diverse biomass origins provide essential nutrients and growth factors [67, 73, 52]. Figure 1 highlights biochar's role in modern agriculture.

Fig. 1. Applications of biochar in sustainable agriculture [40].

Fig. 1. Applications of biochar in sustainable agriculture [40].

1.1.1 Contribution to Soil Health

Biochar is a versatile material with high surface area and porosity, aiding carbon sequestration, reducing nutrient leaching, and mitigating soil acidity. It supports climate change mitigation by enhancing soil health and sustainability [20]. Its addition improves soil physical, chemical, and biological properties, promoting plant growth and nutrient uptake. Benefits include improved porosity, water infiltration, water holding capacity (WHC), aggregate stability, reduced bulk density, enhanced soil strength, balanced pH , better nutrient cycling, and higher cation exchange capacity [58, . Figure 2 shows biochar's impact on soil health.

Fig. 2. Comparison of the effects of adding biochar to soil on its physical, chemical, and biological characteristics [32].

Fig. 2. Comparison of the effects of adding biochar to soil on its physical, chemical, and biological characteristics [32].

1.1.2. Climate Resilience

Biochar is a climate-resilient amendment that supports sustainable agriculture and climate change mitigation. Its high carbon content and porous structure enhance soil properties and sequester , aiding environmental sustainability [93]. As a carbon sink, it reduces greenhouse gas emissions and improves soil structure, fertility, and water retention, thereby enhancing nutrient availability and
reducing leaching [92]. Its porosity supports microbial activity, promotes nutrient cycling, and suppresses disease. Biochar also improves drought resilience and reduces reliance on synthetic fertilizers, minimizing nitrogen runoff [103, 55, 86]. It further aids in restoring degraded soils and converting them into productive farmland.

1.1.3 Sustainable Agriculture Practices

To maintain groundwater quality and decrease nitrate leaching, many tactics have been proposed recently. These strategies include the implementation of drip irrigation systems [34], optimizing fertilization schedules [61], using fertilizers with a gradual release [124], and incorporating soil amendments like zeolite [78]. Among the various strategies, biochar, which is derived from organic materials through pyrolysis in low or no-oxygen conditions, is gaining recognition. Biochar offers substantial potential for use as a soil amendment, boosting sustainable farming methods, increasing the fertility of the soil and lessening the effects of abiotic and biotic stresses [5, 4].

2. Production, Characteristics, and Factors Influencing Biochar

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Biochar is widely produced from biomass to meet growing demand across various applications [104]. Common production involves thermochemical methods such as pyrolysis, hydrothermal carbonization (HTC), gasification, and torrefaction [49, 19]. Yield optimization depends on matching the method to the biomass type and controlling factors like temperature, residence time, and heating rate [38]. These parameters affect biochar's physicochemical properties. For agricultural residues, processing conditions shape biochar structure. [91] noted initial weight loss from water at , followed by cellulose, hemicellulose, and lignin breakdown around . Figure 3 illustrates biochar production methods.

Fig. 3. Methods of biochar production and the resulting yields [147].

Fig. 3. Methods of biochar production and the resulting yields [147].

2.1 Pyrolysis Conditions

Pyrolysis is a procedure wherein organic materials are thermally disintegrated without oxygen, generally at temperatures between 250 and 900 degrees Celsius [140]. With this method, biomass waste is efficiently transformed into useful products, including biochar, syngas, and bio-oil. Depolymerization, fragmentation, and recombination are processes that lignocellulosic materials such as hemicellulose, cellulose and lignin go through at various temperatures, resulting in gaseous, liquid, and solid products [112]. The solid byproduct of pyrolysis is biochar, and the liquid produced is bio-oil. The gaseous outputs include syngas, a blend of hydrocarbons, hydrogen , carbon monoxide and carbon dioxide [43].

2.2 Types of Pyrolysis

Fast pyrolysis is a rapid thermochemical process that produces high-energy liquid bio oil from carbon-rich materials [43]. Biomass is heated at rates over [28], with vapor residence times of 0.5 to 2 seconds at 400 to [105, 25]. Minimizing vapor residence time is key to maximizing bio oil quality, often achieved by rapid quenching [110, 13]. In contrast, slow pyrolysis involves a heating rate of 5 to and residence times over one hour, yielding more char than other methods [95].

2.3 Properties of Biochar

Biochar's various qualities are essential to its effectiveness in a range of applications, like agriculture and environmental restoration. This section delves into the chemical, physical and biological characteristics of biochar, emphasizing the significance of attributes like surface area, functional groups, and porosity in influencing its interactions and functions in various settings.

a. Physical Properties

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i. The surface area of biochar is key to its reactivity and nutrient absorption capacity. Higher pyrolysis temperatures generally produce biochar with greater surface area, enhancing its effectiveness in soil improvement and environmental applications [151]. This property supports nutrient retention and pollutant removal.
ii. Biochar's porosity, defined by its pore size distribution, affects soil aeration, water retention, and microbial activity. Macropores improve water flow, while micropores retain nutrients. A balanced pore structure is vital for enhancing soil quality and supporting plant growth [51], making biochar valuable for agricultural and environmental use.
iii. Biochar density influences nutrient retention and its persistence in soil. Low-density biochar decomposes faster, releasing nutrients, while high-density types last longer, offering sustained benefits. Optimizing density is key to achieving targeted agronomic and environmental outcomes [77, 150], supporting soil health and plant growth.

b. Chemical Properties

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i. Carbon Concentration: Biochar is rich in carbon, and its carbon content determines its stability and long-term carbon sequestration potential. High-temperature pyrolysis yields biochar with higher carbon content, making it more resistant to microbial breakdown [150]. This stability helps prevent greenhouse gas emissions and supports biochar's role in climate change mitigation.
ii. Functional Groups: Biochar's functional groups including hydroxyl, carboxyl, and phenolic enhance its chemical reactivity and nutrient binding. These groups improve cation exchange capacity, aiding nutrient retention and contaminant reduction. Their presence determines biochar's effectiveness in agriculture and environmental use [83].
iii. pH: Biochar's pH can influence soil acidity or alkalinity. Alkaline biochar, often from specific feedstocks, can neutralize acidic soils. However, its impact varies with production conditions like pyrolysis temperature and duration, affecting soil pH differently across environments [51].

c. Biological Properties

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i. Microbial Activity: By providing a favourable environment for advantageous microbes, biochar can have an impact on soil microbial communities. Its permeable structure provides refuge for microbes, which helps create a favourable environment for soil health and nutrient cycling. By supporting microbial activity, biochar contributes to maintaining soil fertility and promoting effective nutrient management [150].
ii. Plant Growth Promotion: By increasing nutrient availability, biochar encourages plant growth. Boosting water retention and boosting rhizosphere microbial activity. The presence of biochar can promote root development and increase nutrient uptake, fostering healthier and more productive plants [2]. Additionally, the capacity of biochar to influence soil microbial populations helps support plant growth through improved soil structure and nutrient cycling.

2.4 Factors Influencing Biochar Properties

The pyrolysis process's characteristics are crucial to the creation of biochar. The primary factors influencing the characteristics of biochar and significantly influencing its quality are feedstock type, particle size, pyrolysis temperature, and heating rate [11]. Additionally, the biomass type utilized and the particular pyrolysis conditions used affect the biochar output. Determining the applicability of biochar for different uses requires an understanding of its features. Natural materials, wood, municipal waste, and agricultural residues are only a few of the biomass sources that can be used to make biochar [94].

2.4.1 Feedstock

Biomass, a complex solid from organic, inorganic, or biological sources, is classified as woody or non-woody [123]. Woody biomass, like forestry residues, has low moisture and high calorific value [66, 117, 26], while non-woody biomass (e.g., agricultural waste, manure) has high moisture, low density, and low energy content [102]. Moisture reduces biochar yield and increases pyrolysis energy demands [81, 84]. Wood-based feedstocks produce carbon-rich, stable biochars [106], while grass and manure-based feedstocks yield nutrient-rich biochars ideal for N-P-K alternatives [10, 125, 41, 107]. Diverse sources include agricultural, industrial, municipal, and aquatic wastes [37, 59, 22, 23].

2.4.2 Resident Period

Increasing the residence time during pyrolysis at lower temperatures ( ) decreases biochar yield but enhances its ability to adsorb iodine and its pH . However, at higher temperatures ( ), increasing the residence period considerably decreases the iodine adsorption capacity while having little effect on pH or charcoal output [129].

2.4.3 Temperature

Pyrolysis temperature greatly affects biochar's elemental composition, pore structure, surface area, and functional groups [50]. Higher temperatures release volatiles, reducing Hydrogen-to-Carbon ratio (H/C) and Oxygen-to-Carbon ratio (O/C) ratios and increasing aromaticity and carbon content, thus enhancing stability [44]. At around , biochar becomes more hydrophobic with greater microporosity and surface area, ideal for removing organic pollutants [91]. Lower temperatures ( ) yield biochar with more oxygenated functional groups, better for capturing inorganic pollutants [94]. Higher temperatures also increase pH due to ash content [66, 84].

2.4.4 Pre-Treatment of Materials

Pre-pyrolysis treatment significantly influences biochar properties. Physical, chemical, or biological methods help prepare various feedstocks [114]. Techniques like particle size reduction and solvent soaking can boost yield [115]. For example, acid-treated pine wood enhanced biochar quality. Nitrogen or metal doping and vapor treatments also improve elemental composition and stability [129].

3. Biochar's Effects on the Health of Soils

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Biochar is a stable porous carbon from biomass that can improve soil structure and water retention, raise pH and cation exchange capacity, and support microbes, thereby enhancing soil fertility and reducing nutrient losses. Effects vary with feedstock, pyrolysis conditions, application rate, soil type, and time since application.

3.1 Impact on Water Retention/WHC

Biochar's large surface area and porosity improve soil water retention, acting like a sponge, absorbing excess water and slowly releasing it during dry periods [57]. This supports plant growth, enhances drought tolerance, and reduces irrigation needs, especially in arid regions. Its effectiveness depends on biochar type, application rate, soil properties, and environmental conditions, all of which must be considered for optimal use [57].

3.2 Impact on Nutrient Retention/Availability

Biochar enhances nutrient retention through its porous structure and large surface area, reducing leaching and ensuring long-term availability of nutrients like potassium, phosphorus, and nitrogen. It improves cation exchange capacity, supporting plant growth and minimizing nutrient loss [119, 120, 118, 35, 80, 31]. This reduces fertilizer demand and promotes efficient nutrient use in agriculture [126].

3.3 Impact on Soil Structure/Texture

Biochar improves soil structure by enhancing aggregation, porosity, and stability [126]. This boosts water infiltration, retention, and nutrient availability, creating favorable conditions for microbes and root growth. It also reduces compaction, especially in clay soils (Blanco-Canqui, 2021). Long-term use further enhances soil structure and supports sustainable farming [57].

3.4 Impact on the PH of Soil

Plant growth and health depend heavily on the soil pH . By limiting the availability of nutrients, making plants more susceptible to pests, and limiting growth, acidic soils impede plant development [33]. [33] state that biochar buffers pH variations and releases alkaline substances like potassium and calcium carbonate to help balance soil acidity and give plants more stable growing conditions. Biochar, however, may have a detrimental impact on pH in alkaline soils [21]. Soil pH is influenced by the type of feedstock, pyrolysis conditions, and soil composition [125].

3.5 Impact on Soil Porosity

Soil porosity is key to water retention, nutrient uptake, and plant growth [87]. Biochar enhances porosity by creating channels that improve air and water flow, boosting crop yield and aiding and contaminant retention [72, 15]. Biochar increased capillary and total porosity by and [127], and by in sandy loam soil at over two years [1]. Meta-analysis shows biochar raises porosity by and lowers bulk density by [15]. [85] also reported an increase.

3.6 Concentration of Organic Carbon in Soil

Soil organic carbon (SOC) is essential for soil fertility, structure, water retention, and carbon sequestration [42]. Biochar's impact on SOC depends on soil type, application rate, and biochar type. Biochar increased SOC from to over two years [99] and by over four years at 6-12 [128]. Rates from 1 to averaged a rise. In pot studies, raised SOC by in 3.5 years [42]. Short-term gains include a rise from to in five days [152] and from 3.1 to [125].

3.6.1 Biochar Effects on Soil Physical and Chemical Properties

To synthesize the literature, Table 1 compiles representative studies by feedstock, pyrolysis conditions, experiment type and duration, soil type, application rate and crop, the soil characteristics measured, and the observed effects with references. Overall, the studies most often report increases in soil pH , cation exchange capacity, aggregation, water holding capacity, and soil organic carbon, and decreases in bulk density. These effects tend to be stronger in acidic or coarse textured soils.

Table 1. Biochar effect on soil physical and chemical properties across different experiments.

SN | Biochar source | Biochar pH | Pyrolysis temperatures (°C) | No. of years | Soil type | Application rate | Crops grown | Soil characteristics measured | Effect on soil | References
1 | Wood chip, wheat straw, vineyard pruning | 8.3–9.7 | 400 (vineyard pruning) and 525 (wood chip, wheat straw, vineyard pruning) | (crop), 2 (fallow) | Chernozem and Cambisol (fine textured), Planosol (coarse textured) | 0, 3% by weight | Mustard (Sinapis alba L.), barley (Hordeum vulgare L. cv. Xanđú) and red clover (Trifolium pratense L.) | Bulk density, aggregate stability, plant available water | Coarse-textured soil > fine-textured soil (soil structure and aggregate stability); straw biochar > wood chip biochar (aggregate stability in coarse-textured soil) | [154]
2 | Forest shrub | 9.3 | 450–500 | 3 | Inceptisols (moderately acidic silty loam) | 0, 5, 10, 15, 25, 40 [t ha⁻¹] | Maize (Zea mays) – Mustard (Brassica spp.) | Plant available phosphorus (PAP) and water (PAW); soil pH, CEC, base saturation (BS), soil organic carbon (SOC) | PAP increased at 10 and 40 t ha⁻¹; PAW at 40 t ha⁻¹; increase in soil pH, EC and BS; additive effect on SOC | [155]
3 | Wheat straw biochar | 10.35 | 350–550 | 1 | Upland red soil | 0, 2.5, 5, 10, 20, 30 and 40 [t ha⁻¹] | Rapeseed and sweet potato | Soil particle size, aggregate stability, organic carbon, total N content and C:N ratio | Increase in soil aggregation, microaggregate content, SOC, total N and C:N ratio | [156]
SN | Biochar source | Biochar pH | Pyrolysis temperatures (°C) | No. of years | Soil type | Application rate | Crops grown | Soil characteristics measured | Effect on soil | References
4 | Cacao shell | 10.2 | 300–450 | 1 | Typic Kanhapl-udult (Ultisol; sandy loam) | 0, 22.5 [t ha⁻¹] | Maize (Zea mays) | pH, Al³⁺ concentration, macronutrients | Increase in soil P and K availability; raised soil pH; reduced Al³⁺ concentration | [157]
5 | Mixed woody feedstock | 10.1–10.5 | 620 | 10 days | Sandy and sandy loam | 0, 1, 5, 10% and 20% (by volume) | Soil incubation | Bulk density and water holding capacity | Biochar decreased soil BD and increased maximum WHC; stronger effect on sandy (coarse-textured) soil and with finer biochar | [158]
6 | Soft and hard wood | 9.7 | 600–700 (softwood), 900 (hardwood) | Multiple scales - including 6-year corn–tomato rotation | Sandy; silty clay | 0, 0.5, 1% [w/w] | Corn–tomato rotation | Soil water retention (SWR), wilting point (WP), field capacity (FC), plant available water (PAW) | High-surface-area biochar increased FC and SWR (sandy soil); no significant effects on WP or on silty-clay soil | [159]

3.7 Mechanisms of Interaction with Biochar

Biochar interacts with soil through processes that enhance porosity, water retention, and aeration, supporting plant and microbial activity [30, 7, 71]. Its high surface area aids in nutrient and contaminant retention, improving soil fertility. Cation exchange capacity, influenced by biochar's pH and surface charge, boosts nutrient availability and soil health [31]. Biochar also supports microbial activity, nutrient cycling, and aggregate formation, leading to lasting soil improvements [126]. Figure 4 illustrates these interactions.

Fig. 4. Mechanisms and effects of interactions between microbes and biochar in lowering pollutants and enhancing soil quality [130].

Fig. 4. Mechanisms and effects of interactions between microbes and biochar in lowering pollutants and enhancing soil quality [130].

4. Biochar Applications

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Biochar is gaining attention for its eco friendliness, wide biomass availability, and versatile thermochemical production [111]. Pyrolysis temperature affects how well biochar removes pollutants. Higher temperatures produce carbon rich biochar that is more effective at adsorbing organic pollutants [3,27,55], while lower temperatures are better for removing inorganic pollutants due to the presence of more oxygen containing functional groups [113]. Factors like pH and residence time also affect biochar's performance [38]. Biochar is used in soil amendment, energy, composting, catalysis, wastewater treatment, and carbon storage [9]. It offers a cost effective alternative to activated carbon for removing pollutants [100], with performance shaped by its surface area, porosity, ion exchange, and feedstock pyrolysis conditions [39, 79, 102].

4.1. Soil Amendment

Soil amendment with biochar is considered one of the important approaches to enhance soil nutrient and water retention, promote soil ecological functions, and serve as a significant tool to address climate change by storing in the soil [131, 132, 133]. Owing to its highly stable carbon content, high porosity, and large specific surface area, biochar application offers multiple benefits as a soil amendment [146, 135, 137]. Studies have shown that biochar improves the physical, chemical, and biological properties of soil [138, 139, 137], particularly with long term application [55], thereby enhancing agricultural productivity [136]. The effects of biochar on soil may persist for years due to its biochemical stability, and long term impacts on crop yield have been documented in some field experiments. For example, [149] reported a consistent increase in maize yield even a decade after a single biochar application, whereas [153] found that rice yield was no longer influenced by biochar six years after application. Such differences often arise from variations in environmental conditions and agricultural practices across field trials, resulting in diverse long term effects of biochar on crop yield. However, few meta-analyses have investigated these long term impacts, and the driving factors behind the spatial variation in biochar effects still need to be identified to optimize biochar soil amendment strategies.

5. Environmental Impact and Future Research

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Biochar has diverse applications, but its environmental impact must be assessed carefully. Stability is crucial and depends on the strength, condensation, and aromaticity of its carbon structure [99, 65, 98, 115, 148]. Biochars derived from wood and grasses can act as sinks for PAHs [60]. However, because their properties vary, they must be evaluated; if unstable, they can release dissolved organic compounds and pose environmental risks [14]. Figure 5 shows biochar's environmental and climate benefits.

Fig. 5. Biochar and carbon sequestration [24].

Fig. 5. Biochar and carbon sequestration [24].

i. Potential for Carbon Sequestration

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Growing climate concerns have increased focus on reducing atmospheric . Soil, a major carbon sink, plays a key role in climate regulation [29]. Biochar, with its stable aromatic structure, resists biodegradation and enhances soil carbon sequestration [6]. However, its effects on emissions vary, with studies reporting both increases and decreases [127, 102]. Wood-derived biochar reduces carbon mineralization [43,53], while combustion-based inputs may increase organic carbon turnover [76], suggesting that sequestration depends more on biochar's stability than fertility improvement. , a key greenhouse gas, reached 412 ppm in 2020 [36] and is projected to hit 54-56 Gt by 2030, surpassing the 42 Gt limit for a target [74]. Biochar's long-term stability makes it a promising carbon storage tool [116, 121]. Agricultural residue biochar can sequester up to year
[122], while manure-based types reach up to year [89], with other studies reporting year , supporting its role in climate mitigation.

ii. Reduction of Greenhouse Gas Emissions

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Applying stable carbon as biochar in agricultural soils is an effective strategy for enhancing carbon sequestration and mitigating climate change [64, 90]. Some biochars also reduce emissions of greenhouse gases like methane , carbon dioxide , and nitrous oxide . Meta-analyses show mixed results, reduced , and increased , depending on feedstock, pyrolysis conditions, application rate, and environment, mostly from lab studies [96, 46]. , a potent greenhouse gas with nearly 300 times the warming potential of over 100 years, mainly comes from fertilizer and manure use [47]. Biochar reduces by improving soil pH , reducing compaction, and enhancing microbial activity, particularly in sandy loam soils [82]. Biochar also helps cut emissions from landfills, rice paddies, and manure by adsorption and microbial oxidation. Spruce wood biochar at over seven years reduced by [56], while a metaanalysis of 43 studies showed an average reduction in East Asian rice fields [63].

5.1 Future Research

Future research on biochar in agriculture could delve into several critical areas to enhance its utility and evaluate its long-term implications:

a. Optimizing Biochar Production

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Explore how variations in feedstock types, pyrolysis temperatures, and residence times influence biochar properties suited for specific agricultural needs. Develop energy-efficient and advanced pyrolysis techniques to improve biochar yield and functionality.

b. Soil-Biochar Interactions

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Analyse how biochar affects soil microbial ecosystems and their function in nutrient cycling in the long term. Analyse how biochar affects the dynamics of organic matter in the soil and stabilising carbon storage.

c. Crop-Specific Applications

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Assess biochar's influence on diverse crops under different soil conditions and climates. Identify biochar formulations optimised for nutrient retention and water management, particularly in arid regions.

d. Greenhouse Gas Mitigation

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Evaluate the effectiveness of biochar in lowering emissions of and from agricultural soils. Address possible trade-offs, such as the release of during biochar production or application.

e. Sustainable Farming Integration

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Combine biochar with practices like composting, organic farming, or precision agriculture to explore synergistic effects. Study its role in regenerative agriculture to boost soil health and ecosystem resilience.

f. Economic and Policy Frameworks

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Analyse the economic viability of producing and using biochar for smallholder and large-scale farming. Examine policy measures to encourage biochar adoption, including subsidies, carbon credits, or sustainability incentives.

g. Reduction of Climate Change

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Assess the global potential of biochar in mitigating greenhouse gas emissions through carbon storage and reducing the need for fertilisers. Investigate its role in mitigating climate change over the long run strategy by integrating biochar into national and international carbon offset programs. This
comprehensive approach will ensure biochar is optimized as a sustainable solution for modern agricultural challenges.

5.2 Conclusion

Biochar stands at the intersection of agricultural innovation and environmental stewardship, offering a multifunctional tool for enhancing soil health, mitigating climate change, and building resilient farming systems. By addressing production challenges and promoting widespread adoption through research and policy initiatives, food security and ecological balance for future generations could be ensured by using biochar to make agriculture a more climate-adaptive and sustainable enterprise.

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 41-47

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Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

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Ojo O.M. , Abayomi O.F.

Department of Civil and Environmental Engineering, School of Infrastructure, Minerals and Manufacturing Engineering, Federal University of Technology, Akure, PMB 704, Nigeria

omojo@futa.edu.ng, drnaryojo@gmail.com

*Corresponding email: omojo@futa.edu.ng

Keywords: natural coagulants, orange seeds, peels, surface water, treatment.

Abstract

The increasing demand for alternative water treatment methods has led to growing interest in natural coagulants for purifying surface water particularly in low-resource communities. In this study, the coagulation performance of orange (Citrus sinensis) seeds and peels as natural alternatives to alum for surface water treatment was investigated. Coagulant dosages ranging from were evaluated for their effectiveness in reducing turbidity, total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD) and biological oxygen demand (BOD). The results showed that orange seeds achieved maximum reductions of turbidity, , and at an optimal dosage of , outperforming orange peels across all parameters. In comparison, alum at demonstrated higher treatment efficiency, with turbidity, COD and BOD reductions of , and , respectively. While reductions in turbidity and organic matter were substantial, TDS removal was limited thus suggesting the need for additional treatment stages to meet WHO drinking water standards. These findings suggest that orange seeds and peels are promising alternatives to conventional coagulants for surface water treatment and supports the application of natural and biodegradable alternatives for water treatment practices.

1. Introduction

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Access to safe and clean water is a fundamental human right, yet millions of people in developing regions continue to face challenges in obtaining potable water. The quality of surface water bodies such as rivers, lakes and streams has declined significantly due to increased urbanization, agricultural runoff, industrial discharges, and domestic wastewater [1,2]. These contaminants contribute to high levels of turbidity, organic load, and microbial contamination, making raw surface water unsafe for human consumption. Traditional water treatment systems, which commonly rely on chemical coagulants like alum and ferric chloride, though effective, are often unaffordable and inaccessible for rural and low-income communities. Furthermore, residual aluminum in treated water has been associated with neurological disorders and other health risks [3].
In recent years, natural coagulants have emerged as sustainable alternatives to chemical counterparts [4-6]. Plant-based coagulants such as Moringa oleifera seeds, cactus extracts and tannin-rich materials have shown considerable potential in water treatment due to their biodegradability, renewability, and low toxicity [5, 7-11]. Agricultural by-products such as fruit peels and seeds, which are typically discarded as waste, have also been explored for their coagulating properties. These natural materials contain active compounds including polysaccharides, proteins, and polyphenols that facilitate floc formation and sedimentation [12-14]. Orange (Citrus sinensis), a widely consumed fruit, generates significant amounts of waste in the form of peels and seeds, which are rich in pectin and limonoids-bioactive components known for their coagulating ability [15, 16].
This study investigated the effectiveness of orange seeds and peels as natural coagulants for the treatment of surface water. By examining their performance in reducing key water quality parameters such as turbidity, total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD) and pH , the study seeks to validate their potential as
low-cost and eco-friendly alternatives to alum. Studies have demonstrated the promising roles of fruit-derived coagulants, but comparative evaluations of different parts of the same plant, such as seeds versus peels, are limited [6, 17-20]. This research aims to fill that gap and contribute to the growing body of knowledge that promotes sustainable water treatment approaches suitable for use in low-resource settings.

2. Materials and Methods

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Surface water used in this study was collected from a tributary of the Ala River in Akure, South Western Nigeria, using clean 10-liter plastic containers. The collected samples were immediately transported to the laboratory for analysis and were characterized for turbidity, total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biological oxygen demand (BOD) and pH , following standard procedures outlined by the American Public Health Association [21]. TSS was determined by filtering a known volume of the water sample through a pre-weighed glass fiber filter, drying the filter at and calculating the mass of solids retained. TDS was measured by evaporating the filtrate from the TSS test and drying the residue at . COD was analyzed using the closed reflux method with dichromate digestion followed by colorimetric determination and BOD was determined by incubating the sample at for 5 days and measuring the oxygen consumed using a dissolved oxygen meter.
Natural coagulants were prepared from orange seeds and peels. These materials were thoroughly washed to remove dirt and adhering impurities, then air-dried under shade to prevent the loss of bioactive compounds. After drying, they were pulverized into fine powders using a laboratory grinder and stored in airtight containers until use. Coagulant solutions were prepared by dissolving known quantities of the powders in distilled water to achieve dosages of , and 10 . For comparison, alum (aluminum sulfate) was used as a control coagulant at a fixed dosage of 2 g/L.
Jar test experiments were conducted to simulate the coagulation-flocculation process in a batch mode. For each test, 500 mL of the raw water sample was poured into six beakers, and the respective coagulant doses were added. The contents were rapidly mixed at 120 rpm for 2 minutes to disperse the coagulants, followed by slow mixing at 40 rpm for 20 minutes to promote floc formation. The samples were then allowed to settle undisturbed for 30 minutes. After sedimentation, the supernatants were carefully collected and analyzed for residual turbidity, TSS, TDS, COD, BOD and pH using the same standard methods applied to the raw water.
The percentage reduction of contaminants was calculated using equation 1 as described in [19]

Where: is concentration of the parameter (turbidity, TSS, TDS, COD, BOD) before treatment ( or NTU)
is concentration of the parameter after treatment with the coagulant ( or NTU).

3. Results and Discussion

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3.1 Initial parameters

The quality of the initially collected surface water before treatment is presented in Table 1. It shows that the raw surface water sample exceeded the WHO recommended limit for turbidity, thus indicating significant presence of suspended particles that may harbor pathogens. Although TDS is within acceptable limits, the elevated levels of COD and BOD suggest high organic pollution, which could lead to oxygen depletion and microbial contamination [22]. The slightly acidic pH of 6.4 is just below the WHO guideline range, this indicates the need for pH adjustment for safe consumption.

Table 1. Surface water quality before treatment

Parameter | Raw Water Value | WHO (2017) Standard for Potable Water | Unit
Turbidity | 52.7 | ≤ 5 | NTU
TSS | 5.6 | Not specified (should be as low as possible) | mg/L
TDS | 162 | ≤ 1000 | mg/L
COD | 368.7 | Not specified (used as pollution indicator) | mg/L
BOD | 42.1 | Not specified (used as pollution indicator) | mg/L
pH | 6.4 | 6.5–8.5 | —

3.2 Percentage Reduction of Contaminants

Figures 1 and 2 show a consistent trend of increasing treatment efficiency with higher dosages of orange seed and peel, up to , after which a slight decline is observed-indicating optimal performance at this dosage. Orange seeds outperformed orange peels across all parameters with maximum turbidity, TSS, TDS, COD, and BOD reductions of , and , respectively, at . In contrast, orange peels achieved peak reductions of , , and , also at , suggesting comparatively lower coagulation efficiency due to fewer active phytochemicals [23]. The pH of the treated water remained within the acceptable WHO range (6.5-8.5) across all dosages. There was a slight but consistent increase with higher coagulant concentrations, indicating that the natural coagulants had a mild alkalizing effect [24]. When compared to alum treatment (Figure 3), which at achieved superior reductions- in turbidity, in TSS, in TDS, in COD, and in BOD-both natural coagulants showed moderate performance, with orange seeds being a closer alternative. This aligns with findings by [25] who noted that while alum often demonstrates higher removal efficiency, natural coagulants offer safer and more sustainable options. TDS reductions were modest for both coagulants, but remained well within the WHO limit of . The COD and BOD were considerably lowered by both coagulants, indicating their effectiveness in reducing organic pollutants [27]. The orange-based coagulants significantly reduce contaminants, they may require further polishing treatment to meet potable standards [28]. TDS reductions were the least among all parameters for all treatments, with orange seed showing a maximum of compared to alum's , reinforcing findings by [7] that natural coagulants have limited effect on dissolved solids. The optimal performance at for both seed and peel followed by a drop at could be attributed to particle restabilization due to overdosing, a phenomenon previously reported by [29]. The results suggest that orange seeds are better suited as a sustainable coagulant for surface water treatment, particularly in rural or decentralized water systems. While not as efficient as alum, orange-based coagulants could provide a viable, low-cost and eco-friendly alternative that can be further optimized and integrated with complementary treatment steps [29].

Fig. 1. Orange Seed Treatment

Fig. 1. Orange Seed Treatment

Fig. 2. Orange Peel Treatment

Fig. 2. Orange Peel Treatment

Fig. 3. Performance of Alum ( ) as a Control Coagulant in Surface Water Treatment

Fig. 3. Performance of Alum ( ) as a Control Coagulant in Surface Water Treatment

4. Conclusion

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Orange seeds showed slightly better performance than peels across all the parameters. The effectiveness peaked at , beyond which a slight decline was observed; this indicates potential overdosing. While alum outperformed both natural coagulants in terms of absolute reductions, the gap was not substantial at optimal natural dosages. These findings affirm the potential of orangebased coagulants as environmentally friendly alternatives in household and community-level water treatment.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/EI.17.41.pdf

The authors acknowledge the staff of Water Resources and Environmental Engineering laboratory of the Department of Civil and Environmental Engineering, Federal University of Technology, Akure for creating a conducive environment to carry out some of the experiments described in this study.

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The original version of this paper is available on https://www.scientific.net/EI.17.41.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 49-56

doi: 10.4028/p-AqG7RP

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

Submitted:
2024-11-24

Accepted:
2025-10-10

Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

The original version of this paper is available on https://www.scientific.net/EI.17.49.pdf

Pawenary , Hari Purnomo and Winda Nur Cahyo

Industrial Engineering, Islamic University of Indonesia, JI. Kaliurang Km 14.5, Yogyakarta 55584, Indonesia

haripurnomo@uii.ac.id, winda.nurcahyo@uii.ac.id

Keywords: safety hazards detection, electrical industry, safety behavior, literature review.

Abstract

The electrical sector drives economic growth and poses significant occupational health and safety challenges. The dynamic nature of electrical infrastructure and the expansion of power plants and distribution networks, particularly in Indonesia, heightens the risk of electricity-related workplace accidents. However, Indonesia's limited research focuses on hazard detection within the electrical industry. This study aims to map the factors contributing to hazards and methods to detect hazards in the electrical industry with several contributing factors, particularly human factors, in identifying potential hazards and mitigating accident risks. The search engines utilized in this study included Science Direct, Google Scholar, Taylor & Francis Online, MDPI, and the Institute of Electrical and Electronics Engineers (IEEE). A total of 2,446 articles were initially reviewed, and after thorough screening, 15 articles were selected for further analysis. The results show some factors contributing to safety hazard detection, including human, environmental, organizational, technological, and physical factors. The factors with the most substantial evidence demonstrated a significant level of . Subsequently, some methods were used for the detection of hazards, such as surveys, interviews, machine learning, deep learning, eye tracking, and virtual reality, which are applied in power plants, transmission, and distribution electrical sectors. Future research could focus on IoT for hazard detection, work culture, and enhancing the human-machine interface.

Introduction

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In a global context, the electrical sector is vital for economic growth and presents significant occupational health and safety challenges [1]. This sector includes power generation, distribution and sales, involving public and private entities for Indonesian conditions. In 2022, Indonesia's electricity supply substantially increased up to especially in electricity used in households, industry, and services[2]. Despite its critical role in energy provision, the industry requires rigorous attention to its workforce's health and safety. The dynamic conditions of electrical infrastructure and the expansion of power plants and distribution networks in the electrical industry, especially in Indonesia, increase the risk of electrical workplace accidents. Electrical hazards in the sector include electric shock, current surges, and fire risks from electrical faults. The key factors are operational procedures, as identified by the Occupational Safety and Health Administration (OSHA) scenario analysis [3]. Addressing these complex hazards requires consideration of factors such as worker skills and experience [4].

Electrical workplace accidents significantly impact productivity in the electrical industry and affect workers. According to OSHA data, electrical issues account for of production, transmission, and distribution losses, leading to 12,976 lost workdays annually [5], [6]. The National Institute for Occupational Safety and Health (NIOSH) reports 244 accidents involving scenarios such as electrical network equipment ( ) and contact with power lines ( ) [7]. Several quantitative and qualitative methods exist for identifying workplace hazards. Typically, risk identification uses two parameters: severity and probability. Methods include a safety checklist, Job Safety Analysis (JSA), Hazard and Operability Study (HAZOP), Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA) [8]. These methods can be adapted to specific job types or regional
conditions. Additionally, effective risk measurement requires regulatory approaches to occupational health and safety in the electrical industry [7].

Other previous research studies have been to understand the unique features of the transmission and distribution sector of the electrical industry with detection hazards. The methods were used for quantifying the safety risk, safety risk mitigation, and safety hazard detection [9], [10]. One of the most effective ways to reduce casualties and property damage in electrical sites was to provide safety hazard detection models. The model that existed used a visual attention mechanism for detecting situations as hazardous or not based on a dataset for processing the results[10]. Research about occupational health and safety in the electrical industry has recently been limited, especially regarding safety hazard detection in Indonesia. This systematic literature review focuses on safety hazard detection factors that contributed to accidents in distribution and transmission activity in the electrical industry over the past four years (2021-2024). The review explores recent advancements aimed at safety hazard detection factors that can reduce the frequency of near-miss and accidents for electrical industry workers. By examining conditions and personal and environmental factors contributing to accidents, this review highlights potential areas for improvement and innovations that could enhance occupational health and safety in the electrical industry.

Methodology

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The methodology of this study was a systematic literature review (SLR). This study started by searching for relevant articles about safety hazard detection in the electrical industry and observing the condition of the electrical Indonesian sector. After identifying the real issue, we conducted SLR using a systematic rule of review in the University of Edinburgh and several other articles through the review literature research. This literature review consists of three stages: literature search and data management, included and excluded criteria, screening process, and evaluation process[11], [12], [13], [14].

Literature Search and Data Management

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The systematic literature review uses five types of search databases from 2020 to 2024, using the same keyword and advanced search features. Article databases that were used were Science Direct, Google Scholar, Taylor & Francis Online, MDPI, and the Institute of Electrical and Electronics Engineers (IEEE). The keywords in this review are safety hazard detection in the electrical industry, and the process review uses Mendeley Reference Management and Microsoft Excel software.

Included and Excluded Criteria

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In this systematic literature review, safety hazard detection in the electrical industry focuses on power plants, distribution and transmission. These are the criteria for the chosen article:

It is connected to safety hazard detection in the electrical industry.

Year of article publication: 2021-2024.

Report on several factors contributing to safety hazard detection in the electrical industry.

One of the investigation tools or methods for examining the safety hazard detection process is interviewing, modelling, direct observation, and analyzing institutions' hazard databases.

Screening Process

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An initial paper search was conducted using the advanced search feature with specified keywords. Once relevant papers were identified, a verification process ensured no duplicates were present. The screening process involved reviewing the abstracts as they summarize the research problem and conclusions. This examination of abstracts helped determine if a paper met the criteria for inclusion in the systematic literature review on safety hazard detection in the electrical industry.

Evaluation Process

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In this stage, the evaluation process of factors contributing to safety hazard detection in the electrical industry focuses on the study's significance. P-value identifies a string relationship. The classification of factors in safety hazard detection is as follows:

Factors with solid evidence ( P -value )

Factors with moderate evidence ( P -value )

Factors with insufficient evidence ( P -value )

Results and Discussion

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The findings of this process were the importance of a structured data management approach, which was adopted to ensure the accuracy and reliability of reference sources. In the first stage of the literature search, there was a broad screening process of 2,475 articles from various source databases. Upon closer examination, 2,472 articles remained after identifying and removing three duplicate entries. This preliminary filtering process allowed for the elimination of redundancies and ensured that unique sources were retained for further analysis. Through subsequent stages of refinement, additional exclusion criteria were added. Furthermore, 15 studies were relevant and selected for inclusion in this research. A detailed overview of the systematic literature review process is depicted in Fig. 1, illustrating the filtering stages and decision points that guided the final selection of sources.

Fig. 1. Stage Of Systematic Literature Review of Studies Safety Hazard Detection in Electrical Industry.

Fig. 1. Stage Of Systematic Literature Review of Studies Safety Hazard Detection in Electrical Industry.

Investigation of Tools and Methods for Safety Hazard Detection in Electrical Industry

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There are a few ways to investigate safety hazard detection in the electrical industry, such as using subjective perspectives, data analysis, model development, and applied technology [4], [15], [16], [17]. Each of these methods has its strengths and weaknesses regarding research. Previous research has extensively explored the application of various techniques that spread over several scopes of electrical industries and countries. The results of classification tools and methods used are shown in Fig. 2.

Fig. 2. Classification Tools and Methods Based on Systematic Literature Review.

Fig. 2. Classification Tools and Methods Based on Systematic Literature Review.

The valuable insights into personal experiences and how people perceive safety hazards are found by collecting several subjective perspectives from stakeholders at different levels. The collected data used questionnaires, interviews, and focus group discussions. For data processing, fuzzy AHP, performance map analysis and necessary condition analysis (NCA) were used for measuring the probability of hazards, exposure and consequence [4], [7], [10], [18], [19], [20], [21], [22], [23], [24].

On the other hand, data analysis provides a more objective and quantifiable way to spot patterns and potential risks. However, it might miss out on details that aren't easily captured in numbers. Developing models can help us simulate and predict potential safety issues, which is a proactive way to address hazards. Still, these models depend on accurate data and assumptions, which can take time. The dataset used for detecting safety hazards comes from the accident analysis and the safety database department [4], [15], [16], [25].

Eye-tracking technology is another helpful tool because it shows how people interact with their surroundings and can highlight hazards that might be overlooked, giving us a detailed and real-time view of safety awareness. However, eye-tracking can be expensive and complicated to set up. Each method has its role in improving safety in the electrical industry, and using a combination of these approaches could provide the most thorough understanding of safety hazards [17].

Virtual Reality (VR) is being explored as a powerful tool to help construction workers stay alert to electrical hazards, often overlooked despite regular safety training. By immersing workers in a VR environment where they repeatedly encounter these hazards, they can safely experience what happens when accidents occur, like getting electrocuted. Using eye-tracking technology, researchers found that this immersive, simulated experience could sharpen workers' attention and make them more vigilant. This approach shows promise as a more engaging and effective way to reduce the risk of electrocution on construction sites [26].

Safety Hazards Detection Factors in Electrical Industry

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Detecting safety hazards in the electrical industry involves a wide range of factors influencing workplace safety and accident rates. Studies have demonstrated that hazard control and prevention are crucial in mitigating unsafe actions, particularly in high-risk environments such as coal-fired power plants and electrical construction projects[10], [19], [22]. Research conducted in Malaysia and Saudi Arabia revealed that safety planning, management, verification, strong leadership, and employee motivation are vital in improving safety compliance and participation[22], [23]. Structured safety protocols have proven effective in reducing occupational accidents and fatalities, with a clear link between these measures and improved safety outcomes.

In addition to organizational and leadership factors, personal attributes of workers, such as age, job role, and work experience, significantly influence safety behaviour in the electrical industry[23]. A study conducted in Kerala, India, found that personal stress, job-related fatigue, and safety climate factors were strongly correlated with occupational accident rates[20]. This underscores the importance of providing comprehensive safety training, enhancing safety knowledge, and addressing personal issues such as stress to create a safer working environment. Effective leadership in
motivating workers and fostering a strong safety culture can bridge the gap between safety knowledge and field compliance[4], [23].

Technological approaches are also becoming increasingly important in detecting and mitigating safety hazards. Virtual reality (VR) simulations, wearable eye-tracking systems, and machine learning algorithms such as the Gaussian Process Classifier (GPC) have shown great potential in improving workers' vigilance toward electrical hazards and increasing the accuracy of hazard detection[15], [17], [26]. These innovations enable more effective safety training and early detection of electrical panel fires, providing practical solutions to the persistent issue of electrical accidents. The integration of these technologies, along with continued education and regulatory enhancements, holds significant potential for improving safety in the electrical industry.

Factors Considered in This Study

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In this study, the factors considered for detecting safety hazards in the electrical industry focus on human, environmental, and operational dimensions, as highlighted in the reviewed literature[7], [20]. This study focuses on developing a hazard detection model that incorporates working culture and is centred on a Human-Machine Interface (HMI) to minimize workplace accidents in the electrical industry. The reviewed literature emphasizes the critical role of human, environmental, and operational factors in workplace safety; however, many existing models need to integrate these dimensions comprehensively[4], [10], [20]. Current research often addresses these factors in isolation, requiring a holistic view of how they interact in real-world scenarios. By emphasizing working culture, this research seeks to fill that gap, recognizing that safety behaviours and perceptions are heavily influenced by cultural norms and organizational practices, which are crucial to addressing hazards effectively.

One of the critical gaps in existing models is the need for more understanding of how human factors interact with environmental conditions, especially in high-risk industries like electrical utilities[4]. For instance, extreme weather or poor lighting conditions can significantly affect human performance, yet these factors are not consistently accounted for in hazard detection models. This research will explore how such environmental conditions, combined with human factors like fatigue or stress, increase the likelihood of accidents. Furthermore, by integrating emerging technologies such as the Internet of Things (IoT) and machine learning, the proposed model will enhance the precision and speed of hazard detection, offering real-time monitoring and adaptive responses to safety threats.

The core innovation of this research lies in integrating Human-Machine Interface (HMI) technology as a crucial output of the hazard detection model. HMI will serve as the communication bridge between workers and machines, providing intuitive, real-time alerts that can be customized to reflect the organization's working culture and safety priorities [27]. This user-centric approach will improve hazard recognition by making it easier for workers to interact with complex systems, ensuring that safety interventions are timely, easy to understand, and implemented. By aligning HMI with both the operational environment and the workers' cultural context, the model aims to reduce human error and improve safety compliance.

The findings of this research have the potential to contribute significantly to the academic field by offering a comprehensive, culture-sensitive approach to workplace safety. This integrated model addresses the technological aspects of hazard detection and emphasizes the importance of human and cultural factors in shaping safety outcomes. By bridging the gap between existing safety frameworks and the realities of human-machine interaction in culturally diverse work environments, this study offers a novel contribution to reducing workplace accidents in the electrical industry, providing practical solutions that can be adapted across various high-risk sectors.

Future Research Stages

The original version of this paper is available on https://www.scientific.net/EI.17.49.pdf

The future stages of research based on the abstract would involve a comprehensive, multi-phase process to enhance the understanding and implementation of hazard detection models and safety behaviour frameworks in the electrical utility industry. In the first phase, a detailed assessment of existing safety measures and hazard detection models will be conducted. This phase will collect realtime data from multiple power plants using advanced data collection tools such as IoT devices and machine learning algorithms. The goal is to identify gaps in the current system, especially in integrating personal and organizational factors that influence safety behaviours. This phase will also assess the relevance and effectiveness of safety compliance frameworks and their direct impact on reducing workplace accidents.

The second phase of the research will focus on developing an enhanced hazard detection model that emphasizes the Human-Machine Interface (HMI) and considers the working culture within the industry. A crucial part of this phase will involve conducting pilot tests using virtual reality (VR) simulations to expose workers to various safety scenarios and measure their reactions to hazards in a controlled environment. The inclusion of eye-tracking technology, as well as AI-driven analysis, will help evaluate worker responses, providing insights into improving hazard perception and vigilance. Additionally, this phase will explore how working culture impacts safety compliance, participation, and overall hazard recognition, allowing for developing a more comprehensive and adaptive safety model.

The final stage will aim to validate the proposed hazard detection model through extensive field testing across different industrial sites. The research will measure the model's efficacy in minimising workplace accidents by comparing accident rates, safety compliance, and employee safety behaviour before and after implementing the new model. This phase will also involve using predictive analytics to forecast future trends in safety performance and offer recommendations for continuous improvement. Ultimately, this research aims to contribute significantly to advancing safety practices in high-risk industries by providing an integrated approach that bridges technological innovation and cultural considerations.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.49.pdf

In conclusion, this systematic literature review highlights the critical factors influencing safety hazard detection in the electrical industry, particularly emphasizing human, environmental, and organizational components. As the electrical infrastructure in countries like Indonesia expands, the risk of workplace accidents grows, necessitating more robust hazard detection mechanisms. Despite the limited focus on this issue within Indonesian research, this review demonstrates that combining technological advancements, such as IoT integration and enhanced human-machine interfaces, alongside a deeper understanding of human factors can significantly mitigate risks. By analyzing critical findings from a broad selection of scholarly sources, this study provides a comprehensive overview of the current state of safety hazard detection in the electrical industry. It underscores the need for future research to refine and implement these solutions effectively across diverse cultural and operational contexts.

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The original version of this paper is available on https://www.scientific.net/EI.17.49.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 57-74

doi: 10.4028/p-X0oD4Y

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

Submitted:
2025-02-17

Accepted:
2025-10-10

A Study on Environmental Sensors for Low-Cost Weather Stations

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Irvan Budiawan , Naufal Ananda , Haryas Subyantara Wicaksana , Azka Muji Burohman , Faqihza Mukhlish , Estiyanti Ekawati and Deddy Kurniadi

¹Doctoral Student, Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung

Instrumentation and Control Research Group, Faculty of Industrial Technology Bandung Institute of Technology, West Java Province, Indonesia

Instrumentation, Calibration and Engineering Center of Meteorologi, Climatology and Geophysics Agency of Indonesia

Engineering Physics Research Group, Faculty of Industrial Technology Bandung Institute of Technology, West Java Province, Indonesia

a*budiawan.irvan@gmail, naufal.ananda17@gmail.com, charyas.wicaksana@bmkg.go.id, azka.m.burohman@itb.ac.id, efaqihza.m@itb.ac.id, esti@itb.ac.id, kurniadi@itb.ac.id

Keywords: weather station, sensor BME 280, low-cost sensor, calibration.

Abstract

Designing a low-cost weather station requires careful consideration of the selection of sensors used. The use of inappropriate or poorly calibrated sensors can result in inaccurate data acquisition, which ultimately affects the quality of weather observations. This paper conducts a literature study related to sensors that are often used in low-cost weather stations. Based on the study results, the most frequently monitored quantities are temperature, humidity, and atmospheric pressure. To demonstrate the calibration analysis of weather sensors, tests were conducted on the BME280 sensor, which is widely used to measure these three parameters. This test was conducted at a meteorological calibration laboratory that has been certified by the National Accreditation Committee (KAN). The test was carried out by building a sensor node equipped with a reference sensor, so that the test could be carried out simultaneously and at the same time. This aims to ensure that the BME280 sensor readings can be compared directly with the calibrated reference sensor, so that the accuracy and precision of the sensor can be evaluated more comprehensively. In this test, the BME280 sensor was tested before and after adjustment. The initial calibration results showed a deviation in pressure and temperature readings that exceeded the specified tolerance limits. After the adjustment process, the BME280 sensor showed a significant improvement in accuracy, with readings that were more consistent and closer to recognized measurement standards.

Introduction

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An automatic weather station (AWS) is a device designed to measure various physical parameters of the environment, such as temperature, humidity, atmospheric pressure, wind speed, wind direction, light intensity, and precipitation[1]. These seven parameters are generally considered the standard elements that an AWS must have. The data obtained from these sensors is essential for monitoring weather conditions in real-time, which is useful in various applications.

However, in application, not all AWSs need to be equipped with all these parameters. Sometimes, a weather monitoring system only requires a few sensors depending on its specific needs. For example, weather stations in agricultural areas may focus more on rainfall and soil moisture parameters[2], [3], [4], [5], [6] while stations in urban areas may pay more attention to air pollution[7] and sunlight intensity parameters[8], [9]. This flexibility in sensor selection enables the development of more cost-efficient AWS systems, without compromising on key functionality.

Research also shows that many small-scale AWS system developments are designed with budget constraints in mind. Researchers often design weather stations using only those sensors that are relevant to their research objectives or focus, thus keeping costs down without compromising the
accuracy of the data generated. With this modular approach, AWS can be customized for various specific applications, enabling more economical yet reliable weather monitoring.

Modern weather station applications play a vital role in various sectors of life, especially in the face of the challenges of climate change and extreme weather. With the ability to provide real-time and accurate weather data, these systems offer invaluable solutions to industries such as aviation, agriculture, shipping, and road transportation. In the energy sector, weather data is used to optimize electricity distribution and management of renewable energy sources. In addition, disaster management[10], [11], defense, tourism, and outdoor activities such as mountaineering[12] and sports also rely heavily on reliable weather information. Thus, weather station applications not only improve operational efficiency, but also contribute to public safety and better decision-making in various fields.

Many researchers and journals that focus on developing low-cost weather sensors often do not conduct adequate laboratory testing, especially regarding the sensor calibration process. This is a serious concern as poorly calibrated sensors have the potential to produce inaccurate data, which in turn can affect the quality of weather monitoring results. Although the use of affordable sensors offers an economical and efficient solution, without proper calibration testing, the reliability of the resulting data remains in doubt. Calibration conducted in a laboratory environment allows these sensors to be tested under controlled and precisely measurable conditions, thus ensuring that they meet certain accuracy standards. Calibration testing is therefore a very important step to guarantee sensor performance, especially when used in applications that require a high level of precision, such as in meteorology, agriculture and disaster management.

Methodology

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Literature Study

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Table 1 presents the results of a literature review on the use of different types and types of sensors applied in research on low-cost weather stations. Although this table provides a comprehensive overview of the sensor options used to measure weather parameters such as temperature, humidity, air pressure, and wind speed and other quantities.

Table 1. literature study on the use of sensors in the creation of low-cost weather stations.

Environmental sensors
Sensor components
Papers

Temperatur
LM35
(Radhi and Al-Naima, 2022)[13], (Heng et al., 2022)[14]

BME280
(Shaw, 2024)[1], (Bernardes et al., 2023)[10], (Suparta et al., 2021)[15]

DHT11
(Qasim et al., 2024)[8], (Gaikwad et al., 2023)[16], (Sakthi et al., 2023)[17], (Khuzairi et al., 2023)[18], (Paulraj et al., 2023)[19], (Chakraborty et al., 2023)[20], (Anik et al., 2022)[21], (Akilan et al., 2022)[4], (Wulandari et al., 2021)[22], (A. Gupta et al., 2021)[9], (Asanza et al., 2021)[23], (Mayurappriyan et al., 2021)[24], (Shevchenko et al., 2020)[25], (Bin Shahadat et al., 2020)[26]

DS18B20
(Ali et al., 2024)[3], (Helal et al., 2022)[27]

DHT22
(Stoyanov et al., 2024)[28], (Zhang et al., 2023)[29], (Tandan et al., 2022)[30], (Leon et al., 2021)[12]

AHT20
(Haris et al., 2023)[7]

HDC1080
(Priambodo and Nugroho, 2021)[31]

SHT10
(Ioannou et al., 2021)[32]

other sensors
(Khan et al., 2024)[2]

Humidity
BME280
(Shaw, 2024)[1], (Ali et al., 2024)[3], (Bernardes et al., 2023)[10], (Suparta et al., 2021)[15]

DHT22
(Stoyanov et al., 2024), (Zhang et al., 2023), (Tandan et al., 2022), (Ioannou et al., 2021), (Leon et al., 2021)[12]

DHT11
(Qasim et al., 2024)[8], (Gaikwad et al., 2023)[16], (Sakthi et al., 2023), (Khuzairi et al., 2023), (Paulraj et al., 2023), (Chakraborty et al., 2023), (Radhi and Al-Naima, 2022), (Anik et al., 2022)[21],

In the results of table 1, the literature study on physical quantities used in the construction of lowcost weather stations shows that in addition to commonly measured quantities such as temperature, humidity, air pressure, wind speed, wind direction, rainfall, and light intensity, there are also developments that involve the measurement of other quantities that are not always standard in conventional weather stations. Some studies integrate sensors to measure additional parameters such as air quality, which includes measuring the concentration of particulates (PM2.5, PM10) or gases such as CO 2 and ozone, to monitor air pollution that can affect environmental and health conditions. UV sensors are also added to some systems to measure the intensity of ultraviolet light, which is important in the context of public health related to sun exposure. In addition, there are weather stations equipped with soil sensors, such as soil moisture and temperature sensors, which are especially useful in agricultural applications or environmental studies. Thus, the literature review shows that although low-cost weather stations usually monitor standard weather parameters, many developments try to add other physical quantities according to the specific needs of the application or research, thus expanding the scope and benefits of the monitoring system.

However, there are significant shortcomings in many of these studies, with most not conducting an in-depth analysis of sensor accuracy and precision according to standards set by the World Meteorological Organization (WMO). Meeting these standards is important to ensure the quality and reliability of the weather data produced. Without verification against these accuracy standards, there is the potential that the data generated by these low-cost weather stations is not fully valid, especially for applications where high accuracy is required, such as in weather prediction or climate change monitoring.

Design of Experimental Kits for Calibration

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The BME280 sensor is becoming one of the most widely used components in the creation of lowcost weather stations due to its multifunctional capabilities, measuring temperature, humidity, and atmospheric pressure in a single device. Not only does this sensor offer an affordable price, but it is also easy to integrate with popular microcontrollers such as Arduino, Raspberry Pi, and ESP8266, making it a top choice in various DIY and research projects. Its low power efficiency and compact size further strengthen its position as a key component for cost-effective weather stations. However, it is important to emphasize that while these sensors offer good accuracy, a calibration process is necessary to ensure the reliability of the data generated. Without proper calibration, the measurement results from these sensors can suffer from deviations, especially over longer periods of time or under extreme environmental conditions. Therefore, regular calibration is necessary to keep the weather data generated accurate and reliable, especially when used for more sensitive applications such as precision agriculture or environmental monitoring. Good calibration will ensure that the use of the BME280 sensor, despite its low cost, still provides optimal and accurate performance in accordance with weather monitoring standards.

In figure 1 is a sensor node block diagram that illustrates how the system works. There is an ESP32 V4 as a microcontroller, a voltage source of 12 volt, a DC to DC converter to reduce the voltage from 12 volt to 5 volt and from 5 volt to 3.3 volts. There are BME280 sensors for temperature sensors, humidity sensors and atmospheric pressure sensors. Before the voltage enters the sensor on the sensor node, it will pass first, namely the voltage selector, choosing what voltage will be used for the sensors, either 5 volt or 3.3 volt voltage. The sensor uses a digital data pin using the I2C communication protocol. All input from the sensor will be processed by the EP32 microcontroller to read and process the data received by the data readings from the sensors which will then be sent to data processing using a local access point network.

Fig. 1. (a) Diagram sensor node (b) Skematik sensor node.

Fig. 1. (a) Diagram sensor node (b) Skematik sensor node.

Figure 2 displays the data acquisition architecture designed to operate on a local computer as a central server. In this system, the installed applications include an MQTT server, which serves as a broker to manage and host data sent from sensor nodes. This MQTT server is in charge of receiving messages from various sensors scattered in the field and ensuring that the data can be processed properly without any loss or delay. In the next layer, the API built using the Node-Red platform acts as a link between the data received by the MQTT broker and the InfluxDB database, which is used to store the acquired data in an efficient and structured manner. Node-Red simplifies data flow through visual integration that enables real-time data processing, while allowing users to monitor the data flow directly.

Fig. 2. Data acquisition architecture for calibration.

Fig. 2. Data acquisition architecture for calibration.

Result and Discussion

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Meteorological equipment condition requirements follow the regulations set by World Meteorological Organization (WMO) (WMO, 2018)[35]. The tolerances of measurements that meet the requirements are presented in table 2 . The table shows the tolerances of 3 physical quantities that will be tested using the BME280 sensor.

Table 2. Condition requirements for meteorological equipment by WMO.

No.
Parameters
Measurement range
Desired tolerance
Tolerance allowed

1
Air temperature
-80 ~ 60 °C
0.3 °C for ≤ -40 °C
0.1 °C for > -40 °C and ≤ 40 °C
0.3 °C for > 40 °C
0.2 °C

2
Relative humidity
0 ~ 100%
1%
3%

3
Atmospheric pressure
500 ~ 1080 hPa
0.1 hPa
0.15 hPa

Five repetitions of calibration were performed for each quantity. The results of the analysis are shown in each subsection below.

BME280 Temperature Sensor Calibration.

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Table 3 shows the accuracy and precision of the BME280 temperature sensor at each test point before adjustments are made. The lowest precision value is , while the highest reaches , with a median value of . For accuracy, the lowest value is and the highest is , with a median value of . In addition, precision and accuracy calculations were also performed for five calibration data at each test point. As a result, the lowest precision value is , the highest value is , and the median value is . Meanwhile, the lowest accuracy is , the highest value is , and the median value is .

Table 3. Accuracy and Precision of BME280 temperature sensor at each test point before adjustment.

Measurement point (°C) | 1st Calibration | 2nd calibration | 3rd calibration | 4th calibration | 5th calibration | All five calibration data
PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%)
20 | 99.7 | 99.5 | 99.1 | 97.3 | 99 | 97.3 | 98.8 | 96.9 | 99.2 | 97.9 | 98.6 | 97.3
25 | 99.5 | 99.4 | 99.3 | 98.4 | 99.3 | 98.4 | 98.8 | 97.8 | 99.1 | 98.3 | 99.1 | 98.3
30 | 99.7 | 99.5 | 99.2 | 98.8 | 99.6 | 99 | 99.8 | 99 | 99.1 | 98.5 | 99.3 | 98.8
35 | 99.7 | 99.6 | 99.4 | 99 | 99.5 | 99.4 | 99.1 | 98.7 | 99.5 | 99 | 99.3 | 99
40 | 99.6 | 99.6 | 99.3 | 99.2 | 99.3 | 99.1 | 99.5 | 99.5 | 99.4 | 97.9 | 98 | 97.7

note: PR (Precision), ACC (Accuracy)
Although it has the best accuracy value of , the result of the bias value still exceeds the tolerance value allowed by WMO. Table 4 shows the bias value of the calibration data. The average correction value is 0.14 where the allowable tolerance is 0.2 . This shows that the correction value obtained has met the requirements. However, adjustments will still be made to improve its accuracy.

Table 4. Bias of BME280 Temperature Sensor at each test point before adjustment.

Measurement point (°C) | 1st calibration bias | 2nd calibration bias | 3rd calibration bias | 4th calibration bias | 5th calibration bias | Overall bias of the measurement range
20 | 0.04 | 0.37 | 0.32 | 0.37 | 0.27 | 0.27
25 | 0.02 | 0.23 | 0.25 | 0.24 | 0.20 | 0.19
30 | 0.06 | 0.13 | 0.18 | 0.22 | 0.19 | 0.16
35 | 0.02 | 0.12 | 0.03 | 0.13 | 0.16 | 0.09
40 | 0.01 | 0.04 | 0.08 | 0.02 | 0.61 | 0.11

To determine the range of values around a measurement that includes variability or uncertainty in the data, an error band graph is created. In the context of calibration and measurement, the error band provides information on how far the measurement result can vary from the true or reference value. Figure 3 shows the error band of the BME 280 pressure sensor. The error band value is obtained from the largest deviation between the measurement values of the standard measuring instrument and the test measuring instrument. In the temperature sensor calibration, the largest deviation is .

Error band before adjustment

Fig. 3. Error Band on BME 280 temperature sensor before adjustment.

To improve the bias and accuracy values, a linear graph was found between the measurement values of the reference unit and the tested unit. Figure 4 shows the linear correlation between the reference unit measurement and the test unit measurement for the first calibration data. The same process was performed to obtain the linear correlation of the second to fifth calibration data, as well as for the combination of the five calibration data. In table 5, we can see the linear function and the coefficient of determination obtained. A coefficient of determination of 0.99 was achieved for the fifth calibration, while in the other calibrations it reached 1 , indicating that the linear regression model obtained was able to explain the entire variation in the data very well.

Table 5. Linear correlation of reference unit temperature measurement with test unit.

Calibration data to | Linear function | R²
1 | y = 0.9982x + 0.1151 | 1.00
2 | y = 0.9816x + 0.7443 | 1.00
3 | y = 0.9857x + 0.6298 | 1.00
4 | y = 0.9833x + 0.7249 | 1.00
5 | y = 0.9642x + 1.1489 | 0.99
1,2,3,4,5 | y = 0.9825x + 0.6742 | 0.99

Fig. 4. Linear correlation of temperature measurement between reference unit and test unit.

The precision and accuracy results after tuning are shown in table 6, where the smallest precision value is , the largest is , and the median value is . Meanwhile, the smallest accuracy value was , the largest was , and the median value was .

Table 6. Accuracy and precision of the BME280 temperature sensor at each test point after adjustment.

Measurement Point (°C) | 1st Calibration | 2nd calibration | 3rd calibration | 4th calibration | 5th calibration | All five Calibration data
PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%)
20 | 99.7 | 99.5 | 99.1 | 99.1 | 98.9 | 98.8 | 98.7 | 98.6 | 99.2 | 98.3 | 98.7 | 98.5
25 | 99.5 | 99.3 | 99.3 | 99 | 99.3 | 99.2 | 99.7 | 99.6 | 99.1 | 98.9 | 99.1 | 98.9
30 | 99.7 | 99.7 | 99.2 | 99 | 99.6 | 99.5 | 99.8 | 99.7 | 99.1 | 98.7 | 99.1 | 99
35 | 99.7 | 99.6 | 99.4 | 99.3 | 99.5 | 99.2 | 99.1 | 99 | 99.4 | 98.6 | 98.7 | 98.7
40 | 99.6 | 99.5 | 99.3 | 99.2 | 99.3 | 99.2 | 99.5 | 99.4 | 99.3 | 98.5 | 98.9 | 98.7

note: PR (Precision), ACC (Accuracy)

The correction values after tuning show improvement in each calibration data. Table 7 displays the correction values obtained. The average correction value after tuning is 0.03 , which meets the tolerance allowed by the World Meteorological Organization (WMO). Thus, it can be stated that the tuning results successfully improved the accuracy values. The error band value has improved after adjusting the sensor. The band error obtained after tuning is .

Table 7. Bias of BME280 Temperature Sensor at each test point after adjustment.

Measurement
point
(°C)
1st
calibration
bias
2nd
calibration
bias
3rd
calibration
bias
4th
calibration
bias
5th
calibration
bias
Overall bias
of the
measurement
range

20
0.04
0.01
0.02
0.02
0.17
0.05

25
0.05
0.06
0.03
0.03
0.05
0.04

30
0.00
0.06
0.02
0.01
-0.12
-0.01

35
0.03
-0.03
0.10
0.01
-0.28
-0.03

40
0.04
0.05
-0.02
0.04
0.33
0.09

BME280 Humidity Sensor Calibration

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Table 8 shows the accuracy and precision of the BME280 humidity sensor at each test point before adjustment. The smallest precision value is and the largest is , with a center value of . Meanwhile, the smallest accuracy was and the largest was , with a median value of . Precision and accuracy calculations were also performed from a combination of five calibration data at each point. As a result, the smallest precision value is , the largest is , with a median value of . Meanwhile, the smallest accuracy is , the largest is , and the median value is . Although the best accuracy value reaches , the correction results still exceed the tolerance limit allowed by the WMO.

Table 8. Accuracy and Precision of BME280 humidity sensor at each test point before adjustment.

Measurement Point (RH%) | 1st Calibration | 2nd calibration | 3rd calibration | 4th calibration | 5th calibration | All five Calibration data
PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%)
40 | 99.2 | 98.7 | 99.42 | 98.48 | 99.19 | 97.74 | 97.69 | 95.42 | 99.68 | 98.34 | 97.92 | 96.81
50 | 99.08 | 96.48 | 97.9 | 96.19 | 99.32 | 97.84 | 99.09 | 97.76 | 99.8 | 98.28 | 98.03 | 96.32
60 | 99.71 | 94.88 | 99.32 | 95.25 | 99.34 | 95.76 | 99.49 | 96.11 | 99.27 | 95.59 | 98.06 | 94.21
70 | 99.87 | 93.59 | 99.62 | 93.81 | 99.82 | 94.35 | 99.61 | 94.45 | 99.57 | 94.09 | 98.43 | 92.86
80 | 99.85 | 92.27 | 99.83 | 92.6 | 99.53 | 92.5 | 99.28 | 92.6 | 99.82 | 93.17 | 98.63 | 91.86
90 | 99.98 | 88.58 | 99.75 | 91.26 | 99.9 | 91.77 | 99.42 | 91.77 | 99.81 | 91.87 | 95.66 | 87.3

note: PR (Precision), ACC (Accuracy)
Table 9 shows the bias values of the calibration data. The average bias value is , where the allowed bias tolerance is . Based on the average bias, the bias value has met the allowed tolerance. However, if reviewed further, the band error has not met the expected tolerance, so further adjustment is required.

Table 9. Bias of BME280 humidity sensor at each test point before adjustment.

Measurement point (RH%) | 1st calibration bias | 2nd calibration bias | 3rd calibration bias | 4th calibration bias | 5th calibration bias | Overall bias of the measurement range
40 | 0.18 | 0.34 | 0.52 | 0.81 | 0.49 | 0.40
50 | 1.22 | 0.81 | 0.70 | 0.62 | 0.71 | 0.81
60 | 2.73 | 2.32 | 2.04 | 1.92 | 2.10 | 2.22
70 | 4.20 | 3.89 | 3.66 | 3.46 | 3.68 | 3.78
80 | 5.85 | 5.57 | 5.45 | 5.18 | 5.14 | 5.15
90 | 10.02 | 7.46 | 7.15 | 6.74 | 6.98 | 7.67

To determine the range of values around a measurement that includes the variability or uncertainty in the data, an error band graph is created. In the context of calibration and measurement, error bands provide information on how far the measurement results can vary from the true or reference value. Figure 5 shows the error band of the BME280 humidity sensor. The error band value is obtained from the largest deviation between the measurement value of the standard measuring instrument and the test measuring instrument. In the temperature sensor calibration, the largest deviation of was obtained.

error band before adjustment

Fig. 5. Error Band on BME 280 humidity sensor before adjustment.

To improve the bias and accuracy values, a graphical analysis of the correlation between the reference unit value and the test unit was performed. Figure 6 shows the linear correlation between the traced gauge and the test gauge for the first calibration data. The same process was carried out to obtain a linear correlation for the second to fifth calibration data, as well as the combination of the five calibration data. The results of these graphs produced a linear function for each calibration data, which is shown in table 10. The coefficient of determination obtained was 1 for each calibration test, indicating that the linear regression model was able to explain all the variation in the data.

Table 10. Linear correlation of reference unit humidity measurement with test unit.

Parameters | Accuracy | Error band before adjustment
Temperature | ± 0.5 °C | ± 1.1 °C
Pressure | ± 1 hPa | ± 1.2 hPa

Fig. 6. Linear correlation of humidity measurement between reference unit and test unit.

The precision and accuracy results after tuning are shown in table 11, where the smallest precision value is , the largest is , with a median value of . Meanwhile, the smallest accuracy value was , the largest was , and the median value was .

Table 11. Accuracy and Precision of BME280 humidity sensor at each test point after adjustment.

Measurement
Point (RH%)
1st
Calibration
2nd
calibration
3rd
calibration
4th
calibration
5th
calibration
All five
Calibration
data

PR
(%)
ACC
(%)
PR
(%)
ACC
(%)
PR
(%)
ACC
(%)
PR
(%)
ACC
(%)
PR
(%)
ACC
(%)
PR
(%)
ACC
(%)

40
99.09
94.83
99.37
93.49
99.11
93.22
97.47
90.95
99.65
93.7
98.13
92.39

50
98.99
94.34
97.75
92.85
99.27
94.58
99.02
94.49
99.79
95.11
98.5
93.79

60
99.68
95.8
99.28
95.72
99.3
95.6
99.46
95.92
99.23
95.72
99.3
95.65

70
99.87
96.34
99.6
97.08
99.81
97.3
99.59
97.1
99.55
97.19
98.76
96.05

80
99.84
96.84
99.82
98.2
99.51
98.05
99.24
97.79
99.82
98.12
98.12
96.12

90
99.98
99.26
99.73
98.98
99.9
99.08
99.4
98.49
99.8
99.02
98.3
97.79

note: PR (Precision), ACC (Accuracy)

The bias value after tuning shows improvement in each calibration data. Table 12 shows the bias values obtained. The average bias value after tuning is 1.65 , which meets the tolerance allowed by the World Meteorological Organization (WMO). Thus, it can be stated that the tuning is effective for improving the accuracy value. The error band value has improved after adjusting the sensor. The band error obtained after tuning is , and the value meets the allowable tolerance.

Table 12. Bias of BME280 Humidity Sensor at each test point after adjustment.

Measurement point (RH%) | 1st calibration bias | 2nd calibration bias | 3rd calibration bias | 4th calibration bias | 5th calibration bias | Overall bias of the measurement range
40 | 1.55 | 2.14 | 2.13 | 2.32 | 2.17 | 2.06
50 | 2.14 | 2.21 | 2.17 | 2.08 | 2.18 | 2.16
60 | 2.19 | 2.00 | 2.08 | 1.99 | 1.98 | 2.05
70 | 2.34 | 1.67 | 1.67 | 1.66 | 1.57 | 1.78
80 | 2.31 | 1.25 | 1.12 | 1.11 | 1.31 | 1.47
90 | 0.63 | 0.66 | 0.72 | 0.79 | 0.68 | 0.44

BME280 Barometric Pressure Sensor Calibration.

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Table 13 shows the accuracy and precision of the BME280 pressure sensor at each test point before adjustment. The smallest precision value is , the largest is , with a center value of . Meanwhile, the smallest accuracy is , the largest is , with a median value of . In addition, precision and accuracy calculations were also carried out directly for five calibration data at each point. As a result, the smallest precision value is , the largest is , with a median value of .

Table 13. Accuracy and Precision of BME280 barometric pressure sensor at each test point before adjustment.

Measurement Point (hPa) | 1st Calibration | 2nd calibration | 3rd calibration | 4th calibration | 5th calibration | All five Calibration data
PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%)
850 | 99.92 | 99.71 | 99.95 | 99.74 | 99.88 | 99.66 | 99.88 | 99.69 | 99.92 | 99.71 | 99.58 | 99.37
900 | 99.97 | 99.76 | 99.98 | 99.82 | 99.69 | 99.55 | 99.92 | 99.71 | 99.97 | 99.95 | 99.72 | 99.57
950 | 99.98 | 99.78 | 99.99 | 99.79 | 99.98 | 99.78 | 99.96 | 99.76 | 99.98 | 99.86 | 99.87 | 98.62
1000 | 99.94 | 99.76 | 99.97 | 99.79 | 99.98 | 99.8 | 99.99 | 99.81 | 99.98 | 99.85 | 99.91 | 99.74
1050 | 99.96 | 99.79 | 99.99 | 99.82 | 99.93 | 99.76 | 99.97 | 99.79 | 99.97 | 99.81 | 99.94 | 99.77

Despite having the best accuracy value of 99.95%, the correction results still exceed the tolerance allowed by the WMO. Table 14 shows the bias value of the calibration data performed. The average bias value is 1.68 , while the allowable tolerance is 0.15 . Therefore, additional tuning is required to correct the bias value to match the expected tolerance.

Table 14. Bias of BME280 barometric pressure sensor at each test point before adjustment.

Measurement
point
(hPa)
1st
calibration
bias
2nd
calibration
bias
3rd
calibration
bias
4th
calibration
bias
5th
calibration
bias
Overall bias
of the
measurement
range

850
1.83
1.80
1.86
1.65
1.80
1.79

900
1.84
1.46
1.30
1.88
0.21
1.34

950
1.88
1.85
1.85
1.89
1.16
1.62

1000
1.77
1.81
1.87
1.87
1.33
1.73

1050
1.82
1.80
1.84
1.86
1.67
1.80

To determine the range of values around a measurement that includes the variability or uncertainty in the data, an error band graph is created. In the context of calibration and measurement, the error band provides information on how far the measurement result can vary from the true or reference value. Figure 7 shows the error band of the BME280 pressure sensor. The error band value is obtained from the largest deviation between the measurement of the standard measuring instrument and the test measuring instrument. In the temperature sensor calibration, the largest deviation found was .

Fig. 7. Error Band on BME 280 barometric pressure sensor before adjustment.

Fig. 7. Error Band on BME 280 barometric pressure sensor before adjustment.

To correct the bias value, a graphical analysis of the correlation between the reference unit and test unit values was performed. Figure 8 shows the linear correlation between reference units and test units for the first calibration data. The same process was carried out to obtain a linear correlation for the second to fifth calibration data, as well as the combination of all five calibration data. The results of these graphs produced a linear function for each calibration data, which is shown in table 15. The coefficient of determination obtained is 0.99 for the fifth calibration, while in the other calibrations it reaches 1 , indicating that the linear regression model obtained is able to explain all the variations in the data.

Table 15. Linear correlation of reference unit barometric pressure measurement with test unit.

Calibration Data to | Linear function | R²
1 | y = 0.9998x + 1.9912 | 1
2 | y = 1.0006x + 1.1275 | 1
3 | y = 1.0011x + 0.72 | 1
4 | y = 1.0008x + 1.0642 | 1
5 | y = 1.0017x - 0.3391 | 0.99
1,2,3,4,5 | y = 1.0008x + 0.9088 | 1

Fig. 8. Linear correlation of barometric pressure measurement between reference unit and test unit.

The precision and accuracy results after tuning are shown in table 16, where the precision values before and after adjusting did not change. However, the accuracy value has changed, with the lowest accuracy of , the highest of , and the median value of .

Table 16. Accuracy and Precision of BME280 barometric pressure sensor at each test point after adjustment.

Measurement Point (hPa) | 1st Calibration | 2nd calibration | 3rd calibration | 4th calibration | 5th calibration | All five Calibration data
PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%) | PR (%) | ACC (%)
850 | 99.92 | 99.92 | 99.95 | 99.93 | 99.88 | 99.86 | 99.88 | 99.87 | 99.92 | 99.92 | 99.59 | 99.59
900 | 99.97 | 99.96 | 99.98 | 99.96 | 99.69 | 99.65 | 99.92 | 99.91 | 99.97 | 99.95 | 99.69 | 99.69
950 | 99.98 | 99.97 | 99.99 | 99.97 | 99.98 | 99.97 | 99.96 | 99.95 | 99.98 | 99.9 | 99.88 | 98.82
1000 | 99.94 | 99.94 | 99.97 | 99.96 | 99.98 | 99.98 | 99.99 | 99.99 | 99.98 | 99.91 | 99.91 | 99.9
1050 | 99.96 | 99.96 | 99.99 | 99.99 | 99.93 | 99.93 | 99.97 | 99.97 | 99.97 | 99.93 | 99.92 | 99.91

note: PR (Precision), ACC (Accuracy)

The bias values after tuning showed improvement for each calibration data. Table 17 displays the bias values obtained, with an average bias of 0.06 after tuning. This value meets the bias tolerance allowed by the WMO. Thus, it can be stated that the tuning successfully improved the accuracy of the sensor. In addition, the band error value also improved after adjusting, with the obtained band error of .

Table 17. Bias of BME280 barometric pressure sensor at each test point after adjustment.

Measurement point (hPa) | 1st calibration bias | 2nd calibration bias | 3rd calibration bias | 4th calibration bias | 5th calibration bias | Overall bias of the measurement range
850 | 0.01 | 0.17 | 0.20 | 0.10 | 0.02 | 0.06
900 | 0.03 | 0.21 | 0.41 | 0.10 | 0.21 | 0.06
950 | 0.08 | 0.15 | 0.08 | 0.06 | 0.80 | 0.18
1000 | 0.02 | 0.08 | 0.04 | 0.00 | 0.71 | 0.12
1050 | 0.04 | 0.04 | 0.04 | 0.05 | 0.45 | 0.09

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Many sensors are available for low-cost weather station construction, but their use must be considered before they are implemented in the system. It is important to make adjustments to the sensor so that the precision and accuracy values increase, resulting in a bias that complies with the standards allowed by the WMO. In this study, adjustment and calibration of the sensor were carried out. After adjustment, the calibration results on the BME280 sensor show a temperature sensor accuracy of , humidity sensor accuracy of , and pressure sensor accuracy of . This improvement increases the bias value, thus meeting the tolerance allowed by WMO. The next phase of this research involves the development of a wireless sensor system for blind calibration.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

Thanks to regional center IV MKG for facilitating the calibration laboratory for testing and thanks also to the Directorate of Research, Technology and Community Service (DRTPM), Indonesian Ministry of Education, Culture, Research and Technology for funding under the Fundamental Research 2024 scheme.

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The original version of this paper is available on https://www.scientific.net/EI.17.57.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 75-88

doi: 10.4028/p-Br9R7i

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

Submitted:
2025-04-08

Revised:
2025-07-09

Accepted:
2025-07-12

Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

Henry Osorto , Javier Enrique Delcid Carrasco

Universidad Nacional Autónoma de Honduras, Honduras

Facultad de Postgrado, Universidad Tecnológica Centroamericana, UNITEC, Honduras

henry.osorto@unah.edu.hn, henry.osorto@unitec.edu.hn, javier.delcid@unitec.edu.hn

Keywords: Economic APIs, Open Data, Time Series Forecasting, Machine Learning in Economics, R Programming Language

Abstract

The automation of economic analysis through open data interfaces has become a strategic component in the development of modern data infrastructures. This study evaluates the integration and analytical potential of the public Web API of the Central Bank of Honduras (BCH), focusing on its applicability within the R programming environment. Through a bibliometric and systematic literature review, global trends in the use of APIs for economic, computational, and predictive modeling purposes are identified. A case study is presented using the year-on-year variation of the Consumer Price Index (CPI) as a proxy for inflation, extracted directly from the BCH API. Six time series forecasting models were implemented-ARIMA, Simple and Linear Exponential Smoothing, Prophet, Random Forest, and XGBoost-comparing their performance using MAE, RMSE, and MAPE metrics. The results show that machine learning models outperform traditional methods in capturing nonlinear patterns in inflation dynamics. This research highlights the relevance of R as a comprehensive analytical environment for data acquisition, processing, modeling, and visualization. Additionally, it underscores the strategic value of programmable access to official statistics and proposes the development of a dedicated R package for the BCH API as a means to enhance reproducibility, efficiency, and transparency in economic research and public policy design.

Introduction

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

In the digital age, access to open data has gained increasing relevance across various sectors, driving decision-making processes based on real-time information. Within this context, Application Programming Interfaces (APIs) have transformed the way users interact with databases by facilitating automated access to structured information. APIs have been widely implemented in applications for mapping, e-commerce, and financial services, with emblematic examples such as Google Maps, which enables the integration of geospatial data into multiple platforms, and Amazon, which facilitates the automation of sales processes and product recommendations [1].

The implementation of APIs has also revolutionized access to economic and financial data, promoting transparency and efficiency in the retrieval of key indicators. Institutions such as the World Bank, the International Monetary Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), and the Economic Commission for Latin America and the Caribbean (ECLAC), among others, have developed APIs to provide real-time access to fundamental macroeconomic variables such as GDP, inflation, exchange rates, and interest rates [2]. These tools have become essential for academic research, public policy analysis, and strategic decision-making in both business and government sectors.

The role of APIs in data science and the digital economy has enabled the development of advanced modeling applications and predictive analysis. Recent studies have shown that automated access to data through APIs improves the accuracy and efficiency of econometric and machine learning models, facilitating the forecasting of macroeconomic trends and the monitoring of financial markets [3]. Furthermore, the use of APIs in the financial sector has contributed to the development of open bank-
ing platforms, where users can access and share banking information more easily and securely, as evidenced by the SEPA scheme in Europe [4].

Within this framework, the Central Bank of Honduras (BCH) has taken a significant step by implementing its own public API, allowing for automated access to national economic data. This initiative represents a key opportunity to enhance transparency and accessibility of macroeconomic information in the country, aligning with international standards in open data. However, taking full advantage of this API requires specialized computational tools that facilitate data extraction, transformation, and analysis.

The R ecosystem, one of the most widely used programming languages in data science, has integrated multiple packages to interact with economic and financial APIs. Tools such as wbstats [?] for World Bank data, imf .data [6] for IMF statistics, OECD [7] for OECD indicators, FAOSTAT [8] for FAO data, and CepalStatR [9] for ECLAC statistics, have proven essential for the research community and economic policy formulation [10]. Nevertheless, no R package has yet been developed specifically to interact with the BCH API, which represents a gap in the availability of tools for analyzing Honduran economic data.

This article aims to analyze the role of APIs in the provision of macroeconomic data, review the existing R tools for accessing economic information, and assess the importance of developing a dedicated package for the BCH API. The study also examines the availability of open data from international institutions and explores the potential impact of an R-based solution to improve accessibility and analysis of macroeconomic data in Honduras. Finally, a practical demonstration is presented on how to integrate economic data from APIs in R, highlighting the usefulness of these tools for economic research and analysis.

Literature Review.

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

The literature review was carried out using the Scopus database, employing search terms associated with application programming interfaces as well as macroeconomic data. The search equation used was: (TITLE-ABS-KEY("API" OR "Application Programming Interface") AND TITLE-ABSKEY("macroeconomic data" OR "economic indicators" OR "financial data" OR "open data")). The query returned a dataset of 497 documents published between 1995 and 2025, indicating an annual growth rate of . In addition, over 3,313 Keywords Plus and 1,584 Author's Keywords were analyzed, identifying the most frequent terms as open data, API, financial, machine learning, programming, and macroeconomic data.

To identify the thematic structure underlying the literature, a correspondence analysis was performed to build a conceptual map based on keyword co-occurrence [11] (see Fig. 1). The resulting clusters revealed that the scientific literature on APIs and macrodata is organized around three main themes: (1) APIs and open data in economics and finance, characterized by terms such as "financial data processing," "open data," "big data," and "application programming interfaces API"; (2) APIs in data analysis and computational science, indicated by terms such as "data handling," "web services," "query processing," "data integration," and "interoperability"; and (3) Applications of APIs in machine learning and predictive models, supported by keywords such as "data mining," "semantics," and "ontology," which are fundamental in predictive modeling with API-driven data.
APIs and open data in economics and finance. The implementation of APIs in the economic and financial sectors has transformed the way data is accessed, shared, and analyzed. Their use has been driven by the rise of the open data movement, the digitalization of the financial sector, and the growing demand from users, researchers, and businesses for automated and interoperable solutions.

One of the most notable developments in the literature is the role of APIs in democratizing access to financial data and empowering users. In [12], this phenomenon is addressed from a behavioral and empirical perspective, showing that the use of open financial technologies significantly improves the performance of individual investors in stock markets. This finding is supported in [13], where

Fig. 1: Conceptual structure map based on multiple correspondence analysis of API-related literature

Fig. 1. Conceptual structure map based on multiple correspondence analysis of API-related literature

a reference architecture for APIs in financial systems is proposed, optimizing both the security and traceability of information flows between banks, fintech companies, and users.

Likewise, [14] emphasize the value of APIs in open data systems as enablers of distributed analysis, where multiple sources of economic data-such as e-commerce platforms, social networks, and institutional records-can be dynamically integrated. Their work highlights the need for decentralized computational infrastructures in which APIs act as connectors between heterogeneous data sources.

A central element in this transformation is user empowerment and secure, standardized access to personal financial data. According to [15], the application of security frameworks such as the Open Web Application Security Project (OWASP) within open API architectures has enabled consumers to exert greater control over their data, allowing for more personalized services and active participation in digital financial ecosystems. This phenomenon is closely tied to the rise of open banking and the decentralization of information flows.

This process has also been accompanied by an increasing availability of analytics and predictive modeling services. As noted in [16], the authors developed an API-based system for detecting financial bubbles. Their model integrates diverse, real-time operational data sources that would be unfeasible to analyze without a programmable and open data architecture. This ability to connect dispersed sources, automate analysis, and generate alerts represents a new frontier for financial risk management and public policy design.

Beyond operational use, APIs are also being conceptualized as structural components of computational scientific infrastructure in economics. In this regard, [17] propose a research paradigm supported by digital ecosystems enabled by APIs, allowing the integration of economic models with heterogeneous data sources-from economic and social sensors to financial transaction platforms. Their proposal implies a reconfiguration of economic research processes, increasingly oriented toward replicable, automated, and real-time analysis.

Additionally, the API revolution has extended into the governmental and institutional realms. [18] document how governments and multilateral organizations have promoted open data policies to foster fiscal accountability and administrative efficiency, primarily enabled through the use of APIs. Similarly, [19] present an integrated architecture that combines data mining techniques and computational modeling powered by public APIs, enabling the early detection of macroeconomic risks in European countries. These experiences reaffirm the importance of an interoperable financial data infrastructure supported by programmable services.

On another front, [20] underscores the role of open data as a catalyst for sustainable development and innovation. Their work, in collaboration with institutions such as the World Bank and the United Nations, argues that APIs not only improve data accessibility but also enable the construction of more dynamic and context-sensitive indicators, especially in developing countries.

This ecosystem of open data and APIs has also stimulated the development of software libraries specifically designed for economic analysis. Among the most prominent are the wbstats, imf.data, OECD, and CepalStatR packages, which connect directly to APIs provided by multilateral institutions. These packages have been instrumental in accelerating data collection, cleaning, updating, and exploratory analysis processes, facilitating the production of comparative and replicable studies at the international level.

In this context, the recent launch of the API of the Central Bank of Honduras (BCH) represents a valuable opportunity to integrate Central America into this global network of open data. However, there is still a lack of tools that allow for effective utilization of this API within statistical environments such as R or Python, which justifies the development of new solutions aimed at facilitating access, processing, and modeling of national economic data.
APIs in data analysis and computational science. At the intersection between data science and distributed access technologies, APIs have emerged as a fundamental pillar in the design of computational architectures aimed at automating the processing, integration, and analysis of large volumes of heterogeneous information. Their implementation has enabled the deployment of reproducible scientific environments with real-time data flows and an increasing ability to respond to complex, multidimensional problems.

One of the most prominent applications is the use of APIs in geospatial processing platforms, where data from sensors, satellites, and institutional records are managed. In [1], a standardized architecture is proposed for delivering environmental monitoring data through APIs that allow direct integration with analytical applications and web-based visualizations. Similarly, [21] present a scalable platform for satellite Earth observation data, based on microservices and RESTful APIs, enhancing interoperability between different sources and distributed algorithms.

From an urban and citizen-centered perspective, [22] describe a digital twin model that integrates urban, environmental, and mobility data through open data APIs. This proposal is complemented by [23], who explore the creation of accessibility links using heterogeneous public data sources connected via APIs.

In the field of computational neuroscience, [24] developed a web interface called NeuronBridge, which allows researchers to compare three-dimensional neuronal maps through a specialized API. This system optimizes the search and correlation of complex neural structures, contributing to open and collaborative science. The three-dimensional visualization of dynamic phenomena has also been explored using APIs. In [25], a 3D heat mapping system is proposed, integrating data from sensors, social networks, and simulations through APIs to represent correlations among multiple factors in urban spaces. In the infrastructure sector, [26] document the implementation of an urban inspection system supported by open data APIs, enabling the automation of risk detection in bridges and roads using image flows and structural records.

Finally, [27] explore the use of APIs for sentiment analysis on Twitter, integrating data through a bio-inspired algorithm (Social Spider Optimization) that processes real-time text streams. These developments reflect the transformative potential of APIs as mediators between data sources, algorithms, and users, enabling the construction of intelligent, scalable, and scientifically robust infrastructures for computational analysis.
Applications of APIs in machine learning and predictive models. The integration of Application Programming Interfaces (APIs) into machine learning environments has created a more efficient, automated, and scalable computational ecosystem for the development of predictive models. APIs enable seamless connections between heterogeneous data sources, analytical platforms, and intelligent services, reducing latency in data-driven decision-making and optimizing artificial intelligence workflows.

One of the most notable applications is the development of decision support systems. In [28], the authors describe SIGAMER, a demand management tool that ingests heterogeneous sources through

APIs to provide energy demand forecasts using artificial intelligence. This kind of integration enables adaptive responses to dynamic consumption scenarios with real-time data infrastructures.

Semantic interoperability has also been addressed through APIs. In [10], a platform of semantically interoperable services is proposed to facilitate access to multidisciplinary data via APIs, allowing its application in predictive models in Earth sciences. This type of metadata-driven architecture enables the reuse of models and their integration across multiple analytical domains.
[29] argue that the opening of institutional data acts as a catalyst for expanding predictive models in social and economic domains. Their work, focused on the library sector, demonstrates how APIs exposing structured data sets can be leveraged by recommendation systems and behavioral analysis tools.

In the urban domain, [23] explore how APIs connected to public accessibility data can feed spatial analysis and inclusivity models, improving predictions about urban connectivity. Through advanced data integration methods, these applications enhance infrastructure diagnostics from a territorial perspective.

Similar applications have been tested in automated environments. In [30], an application based on Robotic Process Automation (RPA) is developed, integrating data via APIs to simulate administrative and production processes. The study shows how predictive systems can be fed with public and semistructured data.

In the infrastructure maintenance sector, [26] use APIs to feed deterioration detection models for civil works, drawing on data from automated visual inspections. This reinforces the role of APIs as vehicles for transforming physical data into real-time structural forecasts.

Smart cities have also been explored in this context. In [31], an ontology is developed for Internet of Things (IoT) devices, mapping NGSI-LD contextual models to RDF using APIs, thereby optimizing predictive capabilities in urban platforms. Semantic standardization plays a key role here in ensuring model interoperability.

From a technical perspective, [32] propose an architecture for publishing semantic vocabularies under any schema, allowing query-based predictive engines to access format-agnostic API sources. This capability enhances the scalability of models requiring multistructured information.
[33] address the issue of data anonymization in predictive systems, proposing a tool that applies anonymization rules directly on open APIs, thus preserving privacy without sacrificing analytical utility for model training.

Lastly, [34] investigate entity disambiguation in short texts through semantic knowledge models powered by APIs. Their findings are applicable to intelligent systems for pattern recognition in unstructured text, with direct impact on financial, legal, and scientific analytics.

All these studies converge in recognizing APIs as enablers of intelligent and modular architectures, supporting the training, tuning, and deployment of predictive models across various disciplinary domains.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

This section describes the procedures implemented to carry out the demonstration of the use of data from the BCH Web API.
Data retrieval and processing. For data acquisition, the API of the Central Bank of Honduras (BCH) was used, which provides access to macroeconomic time series through JSON-formatted requests. The year-on-year variation of the Consumer Price Index (CPI) was selected as the study variable, representing the annual inflation rate. The data extraction was performed using the following API query:
https://bchapi-am.azure-api.net/api/v1/indicadores/609/cifras?formato= Json&clave=XXXXXXX

The data were structured into a chronologically ordered set as follows:

where represents the date and the corresponding year-on-year inflation rate. The analysis period spans from January 2000 to February 2025.

While the CPI series used in this study did not present missing values, the reliability and completeness of other indicators available through the BCH API may vary. Some datasets might show inconsistencies in historical coverage or update frequency, which should be carefully considered by researchers aiming to conduct multivariate or long-term economic analyses.
Predictive models. Six time series forecasting models were implemented. For each model, the data were divided into a training set comprising of the observations, and a test set comprising the remaining 20%:

The models used are described below.
ARIMA. The Autoregressive Integrated Moving Average (ARIMA) model is represented by:

where is the autoregressive order, is the moving average order, is the number of differences, and is white noise.
Simple Exponential Smoothing (SES). This model calculates the forecast as:

where is the smoothing parameter.
Linear Eeponential Smoothing (LES - Holt-Winters). This model incorporates a trend component:

where is the level, the slope or trend, and the forecast horizon.
Prophet. Prophet is a modular additive regression model developed by Facebook, designed for largescale time series forecasting. It includes configurable components that allow the modeling of trends, seasonality, and holiday effects. The model decomposes the time series as follows: For this study, Prophet was implemented using monthly frequency (freq = "month") and enabled yearly seasonality (yearly.seasonality = TRUE) to capture recurring annual patterns in inflation. These settings were chosen to reflect the typical seasonality structure of macroeconomic indicators and ensure comparability with other models.

where represents the trend, the seasonality, the external effects, and the error term. Random Forest. This ensemble machine learning model uses multiple decision trees. The forecast is calculated as:

where denotes the -th tree in the forest.

XGBoost. XGBoost is a gradient boosting model that minimizes the following loss function:

where is a regularization parameter and are the model weights.
Implementation of machine learning models. Both machine learning models-Random Forest and XGBoost-were implemented as univariate autoregressive predictors. Specifically, each model was trained using a single lagged input variable (lag1) to forecast inflation. This approach was adopted to ensure consistency in input dimensionality across models and to facilitate a controlled comparison based solely on the forecasting algorithms employed. Random Forest was trained using 100 trees (ntree ), while XGBoost was configured with 100 boosting rounds (nrounds ) and the objective function reg: squarederror. No additional hyperparameter tuning was applied, as the purpose was to demonstrate baseline integration of machine learning models in economic forecasting through the API-generated data.
Model evaluation. To evaluate the predictive performance of the models, three metrics were used:

Mean Absolute Error (MAE):

Root Mean Squared Error (RMSE):

Mean Absolute Percentage Error (MAPE):

R packages used for implementation. The statistical programming language [35] was used for the development of this procedure, along with a set of specialized packages that facilitated the access, transformation, modeling, and visualization of the data.

The jsonlite package [36] was used to read and convert data in JSON format provided by the API. For data manipulation, cleaning, and structuring, the dplyr package [37] was employed, while tibble [38] and reshape2 [39] were used for table formatting and data reshaping. For the visualization of results and the comparison of model performance, the ggplot2 package [40] was used, widely recognized for its ability to generate elegant and customizable statistical graphics under the grammar of graphics paradigm.

Regarding predictive modeling, the following R packages were used: forecast [41] for the implementation of ARIMA, Simple Exponential Smoothing (SES), and Holt-Winters (LES) models; prophet [42] for the implementation of a modular additive regression model; randomForest [43] for the training of random forest ensembles; and xgboost [44] for the gradient boosting algorithm.

Results

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

The empirical demonstration aimed to evaluate the practical utility of the Web API of the Central Bank of Honduras ( BCH ), integrating it into an automated workflow in the R programming language. This choice was not arbitrary: R has consolidated its position as one of the most robust and versatile
environments for statistical analysis and data science, allowing in this case a seamless integration between data retrieval, processing, visualization, and predictive modeling.

By consuming data directly from the API in JSON format, it was possible to structure and transform the historical series of year-on-year inflation without the need for manual intervention. Figure 2 presents the complete series from the year 2000 to 2025, showing both actual data and the forecast values from each model starting at the cutoff point of the test set.

Fig. 2: Time series of inflation and forecasts from six predictive models.

Fig. 2. Time series of inflation and forecasts from six predictive models.

Note: For better visibility in grayscale, different line types and colors were used to distinguish actual vs. predicted values.

The strength of R lies in its ecosystem of specialized packages, which facilitated the fitting and implementation of six predictive models: ARIMA, Simple Exponential Smoothing (SES), Linear Exponential Smoothing (LES), Prophet, Random Forest, and XGBoost. This ability to compare multiple approaches within the same analytical platform represents a considerable advantage for researchers and analysts, as it allows for accurate evaluation of the performance of different algorithms on the same time series.

As shown in Table 1, the machine learning models (Random Forest and XGBoost) achieved the best performance, outperforming all traditional approaches across all metrics. In particular, Random Forest obtained a MAE of 0.41 and a MAPE of just , in contrast to ARIMA, which yielded a MAPE of . Prophet, for its part, showed suboptimal performance in this specific context, which could be attributed to the low seasonality and structural simplicity of the selected series.

Table 1: Performance comparison of predictive models for year-on-year inflation (2000-2025)

Model | MAE | RMSE | MAPE (%)
ARIMA | 2.0532 | 2.9558 | 29.39
SES | 1.9208 | 2.8311 | 27.44
3pt LES | 3.9751 | 4.7465 | 64.54
Prophet | 3.4464 | 4.1780 | 54.39
Random Forest | 0.4139 | 0.5342 | 7.74
XGBoost | 0.4774 | 0.6099 | 8.57

These results demonstrate not only the effectiveness of machine learning models in capturing nonlinear patterns, but also the role of R as a comprehensive environment for managing the entire analytical cycle-from automated data acquisition to model validation and visualization.

Moreover, the fact that this workflow can be consistently reproduced through programmatic scripts reinforces the importance of having open APIs and programming languages like R that can efficiently consume them. The exercise conducted highlights how the BCH API, when integrated into environments like R, becomes a highly valuable tool for academic institutions, economic analysis centers, and public entities that require continuous monitoring and predictive modeling based on local data.

This approach not only reduces data collection and cleaning times but also enables new analytical possibilities, including dashboards, alert systems, and econometric models fed directly from the official source. In this sense, R functions not only as a statistical language but also as an operational interface between public data and informed decision-making.

Discussion

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

The results obtained in this study highlight the transformative potential represented by the opening of the Web API of the Central Bank of Honduras (BCH) for automated economic analysis. The ability to access, process, and model time series directly from an official source marks a turning point in the open data ecosystem in Central America, aligning with international standards promoted by organizations such as the IMF, the World Bank, and the OECD.

One of the main contributions of this research lies in demonstrating that statistical languages such as R not only enable the consumption of information exposed by the BCH API but also facilitate its full integration into reproducible workflows for the fitting, comparison, and evaluation of predictive models. The capacity to execute-from a single environment-procedures encompassing automated data collection, statistical validation, and visualization represents a significant advantage for researchers, academic institutions, and public policy designers.

This approach reinforces the argument presented in [17], who propose a new scientific infrastructure for the economic sciences based on real-time data flows and system connectivity. Similarly, [45] designed a national fiscal monitoring system based on open indicators and API architectures, validating the applicability of the approach proposed here to the Central American context. In both cases, the use of programmable interfaces enhances automation, transparency, and traceability in analytical processes.

In terms of predictive performance, machine learning models such as Random Forest and XGBoost demonstrated superior ability to capture the dynamics of year-on-year inflation compared to traditional methods such as ARIMA, Prophet, or Exponential Smoothing. These findings align with [16], who used open architectures and real-time flows to detect financial bubbles through machine learning models. Similarly, studies such as [23] and [46] highlight how the construction of interoperable platforms based on APIs enables the application of advanced algorithms in urban, economic, or scientific contexts, reinforcing the proposal to integrate national economic analysis into open and modular infrastructures.

The utility of R as a statistical environment was key to the development of this demonstration. Thanks to its package ecosystem, R allows for the implementation of classical statistical models, machine learning algorithms, and interactive visualization methods, all within a single reproducible script. This advantage has also been documented by [47], who propose a scientific platform for neuroscience based on APIs, emphasizing the role of programmatic tools in building open science. In the present study, this reproducibility translates into the ability to replicate the predictive exercise month by month, with data automatically updated from the BCH without human intervention.

Nonetheless, the study presents some limitations. First, the exercise focused exclusively on a univariate time series (year-on-year inflation), without incorporating explanatory variables or other data sources. While this approach effectively demonstrates the viability of the API, it limits the complexity of the economic analysis. Additionally, model validation was carried out over a limited temporal
horizon, so it would be advisable to evaluate their robustness in contexts of high volatility or structural change.

From a methodological standpoint, although R is positioned as an accessible and powerful tool for this type of integration, similar solutions could also be developed in other languages such as Python, which would broaden the reach of the proposal. Libraries such as pandas, requests, and scikitlearn allow the replication of this workflow in other computational environments, promoting interoperability between technical and academic communities.

Finally, this research opens the door to future lines of work, including the development of a specialized R package for the consumption of the BCH API . Such a tool could incorporate functionalities for series search, bulk downloads, automatic date and format processing, and even integration with statistical modeling functions and report generation. This proposal would be consistent with similar initiatives such as wbstats for the World Bank, imf . data for the IMF, OECD for the OECD, FAOSTAT for the FAO, and CepalStatR for ECLAC, which have been key to democratizing the use of international data within the scientific community. The development of an official package would contribute to positioning the BCH as a regional pioneer in the programmable opening of economic data.

In addition to the structural and technological contributions, the empirical results from the forecasting exercise reveal interesting insights about model performance. A notable outcome from the comparative evaluation is the superior performance of Random Forest and XGBoost, despite both being implemented as simple autoregressive models using only a single lagged variable (lag1).

This result may be attributed to several technical considerations. First, machine learning algorithms such as Random Forest and XGBoost can capture complex nonlinear relationships and decision boundaries that traditional linear models like ARIMA, SES, and LES may overlook. Second, the inflation series used in this study-particularly during the test period-exhibited relatively smooth and stable behavior, favoring forecasting approaches based on recent observations. Additionally, XGBoost optimizes directly for loss functions such as RMSE and incorporates regularization mechanisms to reduce overfitting.

This suggests that, under stable macroeconomic dynamics and when forecasting precision is prioritized over interpretability, machine learning models-despite their structural simplicity-can outperform more traditional econometric approaches.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

This study demonstrates the successful integration of the Central Bank of Honduras (BCH) Web API into the R programming environment for economic forecasting. Through a comparative evaluation of six predictive models applied to official CPI data, the methodology highlights the potential of programmatic access to public statistics for reproducible, scalable, and transparent research workflows.

The ability to retrieve, transform, and model macroeconomic data directly from the BCH API-without manual intervention-positions this approach as a valuable resource for researchers, policymakers, and developers working in applied economics and data science. The use of R, with its ecosystem of packages for statistical modeling and data visualization, further reinforces the value of open-source tools for technical and academic innovation.

Future lines of work may include the extension to multivariate time series forecasting, the incorporation of external explanatory variables, and the development of a dedicated R package to streamline access and analysis of BCH data, similar to tools developed for the World Bank, IMF, FAO, and ECLAC.

To foster reproducibility and collaborative development, the complete R code used in this study is publicly available at: https://github.com/Henry-Osorto/BCH-API-Integration.

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The original version of this paper is available on https://www.scientific.net/EI.17.75.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 89-96

doi: 10.4028/p-6gRxcp

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

Submitted:
2025-05-18

Revised:
2025-07-26

Accepted:
2025-07-28

Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

José Francisco Deras Pérez , Alicia María Reyes-Duke and Héctor Fernando Villatoro

Universidad Tecnológica Centroamericana, Honduras

Keywords: net present cost; DC capacity optimization; self-consumption; self-sufficiency; net metering; residential photovoltaic systems

Abstract

Proper sizing of a photovoltaic (PV) system is essential to reduce lifecycle costs. This research presents a LabVIEW-based tool developed to optimize the DC nominal capacity of a selfconsumption PV system without storage, connected to the electrical grid. The tool minimizes the system's net present cost (NPC) using two main inputs: an annual hourly load profile and solar irradiance data. It plots NPC as a function of DC capacity and selects the capacity corresponding to the curve's minimum value. To demonstrate its functionality, the tool was applied using data from San Pedro Sula, Honduras. Three scenarios were evaluated: (1) surplus PV energy injected into the grid was discarded; (2) a net billing scheme was implemented; and (3) a net metering scheme was considered. The latter two aimed to improve economic performance by compensating excess generation. Results show that reducing the NPC leads to an increase in the optimal DC capacity. Therefore, the tool effectively optimizes system sizing based on financial criteria. Additionally, selfconsumption and self-sufficiency indexes were calculated for each scenario. The self-consumption index decreases as PV capacity increases, since more energy is generated than used. In contrast, the self-sufficiency index increases with PV capacity, up to a point where additional generation no longer matches consumption without energy storage. The proposed tool provides an accessible and effective approach for the techno-economic design of residential PV systems in regions lacking clear regulatory frameworks for managing surplus energy.

Introduction

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For a grid-connected PV system to be financially attractive, its net present cost (NPC) needs to be minimized. One of the ways to achieve this is by reducing the costs of purchasing electricity from the distribution network, by maximizing the consumption of photovoltaic energy by the load (also known as self-consumption), as shown in Fig. 1.

Fig. 1 Self-consumption of photovoltaic energy

Fig. 1. Self-consumption of photovoltaic energy

This is because, in general, the tariffs paid for surplus energy injected into the grid by selfconsumers are lower than the electricity tariffs charged by the distribution network. In other words, it is more cost-effective for the load to consume one kilowatt-hour produced by the PV system than to export it to the grid.

In fact, in some countries-such as Honduras-there is still no well-established legal or regulatory framework that defines how surplus PV energy injected into the grid should be managed [1,2]. Recent studies emphasize that the lack of clear policies reduces the profitability of self-consumption systems [3, 4].

Self-consumption of photovoltaic energy can also be increased through the use of energy storage systems, such as batteries. Instead of exporting surplus energy when it is not immediately needed, the energy is stored and made available later when demand arises. However, this solution increases the project's capital cost due to the high price of batteries and associated components, and therefore may counteract the goal of making the PV system financially attractive.

Context

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Increasing economies of scale and technological advancements are expected to continue reducing the installed cost of solar photovoltaic (PV) systems. The Levelized Cost of Electricity (LCOE) for solar PV is already competitive with all fossil fuel-based generation sources and is projected to decline further as installation costs decrease and system performance improves [5]. Global analyses confirm this trend, especially in high-irradiation regions like Central America [6].

In many countries, residential PV systems have become a competitive alternative for electricity generation [4]. Between 2016 and 2021, the installed PV capacity increased rapidly in several regions-for example, in Poland [7]. However, sizing optimization remains a key challenge to maximize economic returns [8].

However, in most cases, battery storage is considered to supply electricity during nighttime hours when the PV generator is not available.

Furthermore, the anticipated rise in electricity supply costs from the local grid, combined with the continued decline in PV technology costs, is likely to encourage monetary incentives for increasing PV self-consumption in both the residential [9] and commercial sectors [10]. Additionally, the installation of PV generators helps reduce the thermal load on buildings [11], which is another relevant benefit to consider.

Methodology

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A. Optimization Model

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A mathematical optimization model consists of an objective function and a set of constraints in the form of a system of equations or inequalities. Optimization models are used in areas like engineering design and financial portfolios [12]. Recent work demonstrates that NPC-based models are critical for evaluating storage-free PV systems [13].

The proposed methodology solves an optimization problem whose objective function minimizes the net present cost of a self-consumption photovoltaic system, as shown in Eq. 1.

Subject to the following conditions:

Where:

\(I_{0}\) : Capital Cost of PV System [\$]
O\&M \({ }_{t}\) : Annual operating and maintenance costs [\$]
\(S_{t}\) : Annual insurance costs [\$]
\(R_{t}\) : Annual replacement costs [\$]
\(\rho_{\text {Red }_{t}}: \operatorname{LCOE}\left[\frac{\$}{\mathrm{kWh}}\right]\)
\(\rho_{\text {Exc }_{t}}\) :Price of surplus energy injected into the grid \(\left[\frac{\$}{\mathrm{kWh}}\right]\)
\(E_{\text {Red }_{t}}\) :Annual energy supplied by the distribution network [kWh/year]
\(E_{E_{x c_{t}}}\) : Surplus energy injected into the distribution network [kWh/year]
\(r \rho_{\text {Red }}\) : Annual rate of increase in the price of electricity [\%]
\(r \rho_{\text {Exc } F V}\) : Annual rate of increase in the price of surplus energy injected into the
distribution network [\%]
i: Annual real discount rate [\%]
N:Project life [years]

B. Application developed with LabVIEW

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

To calculate the net present cost of a self-consumption photovoltaic system based on its DC nominal capacity, an application was developed using LabVIEW. The NPC of a PV system is calculated by summing the total discounted cash flows over each year of the project's useful life [14].

To visualize the data graphically on the front panel of the LabVIEW application, a waveform graph was added, allowing multiple curves to be plotted on a single graph. Self-consumption and selfsufficiency indices are calculated for each evaluated value of the DC nominal capacity in each simulation performed by the tool [15].

C. Simulations

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A series of simulations were carried out using the developed program tool to illustrate its proper operation. Three scenarios were considered:

Scenario 1: Surplus energy injected into the distribution network is not remunerated; that is, there is no established legal or regulatory framework governing the management of such surpluses.

Scenario 2: Surplus energy injected into the distribution network is compensated by the grid operator based on wholesale market prices (net billing).

Scenario 3: Surplus energy injected into the distribution network is recognized by the grid operator as energy credits in favor of the self-producing user (net balance). These credits remain available until they are fully offset against the user's consumption.
Each scenario was evaluated using electricity consumption data from two homes located in San Pedro Sula, Honduras.

The first house is occupied by a family of four, with an average daily consumption of 37.14 kWh . Consumption occurs mostly at night, as shown in its load or demand curve in Fig. 2.

Fig. 2 Weekly load profile of House 1

Fig. 2. Weekly load profile of House 1

The second house is occupied by a family of six, with an average daily consumption of 52.4 kWh . From Monday to Friday, the demand curve shown in Fig. 3 indicates very similar consumption patterns. This is because the house includes an office that operates during regular working hours.

Fig. 3 Weekly load profile of House 2

Fig. 3. Weekly load profile of House 2

Electricity consumption was measured at 5 -minute intervals over a full week. However, since the solar irradiance data are recorded hourly, the demand values were adjusted to hourly averages.

Table 1 summarizes the common parameter values for the three scenarios, while Table 2 presents the parameters specific to each scenario.

Table 1. Common parameter values across the three scenarios

Parameter | Value | Unit
Nominal power of the photovoltaic module | 1 | W
Performance ratio (PR) | 0.75 | -
DC/AC ratio | 1.2 | -
Annual degradation rate of the photovoltaic module | 0.5 | %
Annual increase rate of consumed energy | 0.6 | %
Electricity price | 0.2111 | $/kWh
Specific capital cost of the photovoltaic module | 0.35 | $/W
Specific capital cost of the inverter | 0.3 | $/W
Specific capital cost of balance of system (BOS) | 1.05 | $/W
Annual O&M costs | 12 | $/kW
Annual insurance costs (% of capital cost) | 0.6 | %
Nominal discount rate | 10 | %
Annual inflation rate | 2.3 | %
Annual increase in electricity price | 6 | %

Table 2. Scenario-specific parameter values

Parameter | 1 | 2 | 3
Price of surplus energy injected into the grid [$/kWh] | 0 | 0.0056 | 0.211
Annual real discount rate [%] | 0 | 0 | 6

Results

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

The program was developed using NI LabVIEW software and takes two text files as main inputs: a load profile and global irradiance data of the site, both provided at one-hour intervals over a full year. Each text file must contain a single column with 8,760 values, expressed in kilowatts and kilowatts per square meter, respectively. Fig. 4 shows the flow diagram of the algorithm.

Fig. 4. Flow diagram of the program algorithm developed with LabVIEW

Fig. 4. Flow diagram of the program algorithm developed with LabVIEW

The number of iterations performed by the program in each simulation depends on the PV module power rating entered by the user.

The data obtained from the simulations for each scenario are presented below, along with an interpretation of the results. The self-consumption ( ), self-sufficiency ( ) and net present cost (NPC) curves were plotted as a function of the nominal DC capacity for each scenario. Each graph shows a single minimum value of the net present cost (NPCmin), which corresponds to the optimal DC rated capacity.

A. Scenario 1: Waste of surplus PV

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Figs. 5 and 6 show the results obtained in Scenario 1.

Fig. 5 Scenario 1 - House 1

Fig. 5. Scenario 1 - House 1

Fig. 6 Scenario 1 - House 2

Fig. 6. Scenario 1 - House 2

B. Scenario 2: Net Turnover

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Figs. 7 and 8 show the results obtained in Scenario 2.

Fig. 7 Scenario 2 - House 1

Fig. 7. Scenario 2 - House 1

Fig. 8 Scenario 2 - House 2

Fig. 8. Scenario 2 - House 2

C. Scenario 3: Net Balance

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Figs. 9 and 10 show the results obtained in Scenario 3.

Fig. 9 Scenario 3 - House 1

Fig. 9. Scenario 3 - House 1

Fig. 10 Scenario 3 - House 2

Fig. 10. Scenario 3 - House 2

Table 3 was prepared to present the simulation results in an organized manner, summarizing the most important findings discussed in this section.

Table 3 Scenario-specific parameter values

Parameter | Housing 1 | Housing 2
| Scenario 1 | Scenario 2 | Scenario 3 | Scenario 1 | Scenario 2 | Scenario 3
Nom. Cap. DC [W] | 3,312 | 4,763 | 10,000 | 7,420 | 8,469 | 10,000
NPC min PV [$] | 53,735 | 52,604 | 25,302 | 70,760 | 68,760 | 50,060
NPC local grid [$] | 60,310 | 60,310 | 30,310 | 85,068 | 85,068 | 85,068
φSC [-] | 0.7267 | 0.5996 | 0.3572 | 0.7164 | 0.665 | 0.5949
φSS [-] | 0.2195 | 0.2604 | 0.3258 | 0.3437 | 0.3641 | 0.3846

As shown in Table 3, when surplus energy injected into the electricity grid is remunerated, the optimal DC nominal capacity of the PV system increases. This is because higher DC capacities generate more photovoltaic power and, consequently, greater energy surpluses. As a result, the income from selling electricity to the distribution network increases, leading to a reduction in the net present cost of the PV system.

Regarding the self-consumption and self-sufficiency curves, the former decreases as the nominal DC capacity increases, while the latter increases under the same condition.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

This research successfully developed a software tool that optimizes the nominal DC capacity of a grid-connected, self-consumption photovoltaic (PV) system without storage, based on minimizing its net present cost (NPC).

The results show that a decrease in the NPC leads to an increase in the optimal DC capacity calculated by the tool. Therefore, it can be concluded that the program effectively optimizes the DC rated capacity of a PV system according to economic performance criteria.

Future Work

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

The present project was developed using monthly average values of hourly global irradiance incident on the solar array at the optimal tilt angle. In other words, hourly averages were used for each month of the year to construct an annual profile containing 8,760 data points.

As a next step, the integration of a solar radiation model into the program is proposed. This model would allow the calculation of global irradiance on an optimally tilted surface based on global horizontal irradiance data. Incorporating such a model would improve the accuracy of the photovoltaic energy production and consumption estimates.

References

The original version of this paper is available on https://www.scientific.net/EI.17.89.pdf

J.L.O. Fernandez, J.L.O. Avila, R.A. Ordoñez, Potential effect on the energetic matrix of Honduras with the installation of residential photovoltaic generators for self-consumption, 2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX), Guatemala City, Guatemala, (2019) 1-6. https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976994

A.M. Reyes Duke, E.S. Osorio Hernández, Implementación de un sistema híbrido solar fotovoltaico - diésel en la municipalidad de Puerto Lempira, Gracias a Dios, Honduras, Innovare: Rev. Cienc. Tecnol. 7(2) (2018) 14-30.

R. Luthander, J. Widén, D. Nilsson, J. Palm, Photovoltaic self-consumption in buildings: A review, Appl. Energy 142 (2015) 80-94.

International Energy Agency (IEA), Trends in Photovoltaic Applications 2022, IEA PVPS, Paris, (2022).

IRENA, Future of Solar Photovoltaic: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation: paper), International Renewable Energy Agency, Abu Dhabi, (2019).

Fraunhofer ISE, Levelized Cost of Electricity Renewable Energy Technologies, Fraunhofer Institute, Freiburg, (2021).

K.J. Cieślak, Multivariant analysis of photovoltaic performance with consideration of selfconsumption, Energies 15 (2022) 6732.

C.D. Rodríguez-Gallegos, H. Liu, O. Gandhi, J.P. Singh, A deeper analysis into the economic viability of grid-connected PV systems with self-consumption, Energy 193 (2020) 116727.

G. Jiménez-Castillo, F. Muñoz-Rodriguez, C. Rus-Casas, D. Talavera, A new approach based on economic profitability to sizing the photovoltaic generator in self-consumption systems without storage, Renew. Energy (2019).

C. Lara Monge, L. Vásquez Márquez, A.M. Reyes Duke, Case study: Optimization of gridconnected photovoltaic self-consumption systems for the commercial sector in Honduras, IOP Conf. Ser.: Earth Environ. Sci. 801 (2021) 012025.

I. Ochoa, M. Ávila, H. Villatoro, Efectos de módulos fotovoltaicos en la carga térmica de un edificio industrial, Innovare: Rev. Cienc. Tecnol. 9(2) (2020) 63-70.

G. Merei, J. Moshövel, D. Magnor, D.U. Sauer, Optimization of self-consumption and technoeconomic analysis of PV-battery systems in commercial applications, Appl. Energy 168 (2016) 171-178.

J. Hoppmann, J. Volland, T.S. Schmidt, V.H. Hoffmann, The economic viability of battery storage for residential solar photovoltaic systems, Energy Policy 65 (2014) 609-620.

F.J. Ramos-Webb, L.M. Fernández-Ramírez, C.R. Sánchez-Sánchez, Techno-economic analysis of photovoltaic self-consumption systems: A review of modeling approaches and operational strategies, Renew. Sustain. Energy Rev. 145 (2021) 111496.

P.L. Cruz, M.J. Ortega, G.M. Pérez-Díaz, Self-consumption and self-sufficiency indices for photovoltaic systems: Standardization and case studies, Appl. Energy 278 (2020) 115872.

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-17/978-3-0364-3042-3

Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 97-108

doi: 10.4028/p-3MzqP4

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

Submitted:
2025-02-19

Revised:
2025-09-10

Accepted:
2025-09-29

Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

The original version of this paper is available on https://www.scientific.net/EI.17.97.pdf

R.B. Seno Wulung , Aida Nur Mashito Putri and Justinus Sigit Purwoko

¹Department of Rubber and Plastic Technology, Politeknik ATK Yogyakarta, JI.ATEKA, Bangunharjo, Sewon, Bantul, D.I. Yogyakarta, Indonesia.

PT. Rapindo Plastama, JI. Raya Trawas Km 7, Ds. Mojorejo, Kec. Pungging, Mojokerto, Indonesia

seno_wulung@atk.ac.id, putriaida866@gmail.com, cjustinesigit@gmail.com

Keywords: MRP, Inventory Management, Plastic Blown Film, Min-Max, ROP, EOQ.

Abstract

The inventory costs of raw materials and finished goods for manufacturing companies can reach half of the company's assets. Proficient inventory management is needed to accelerate the production process and prevent overstocking or stock-outs. Raw material inventory planning at the company often experiences overstock of raw materials, which results in material damage and also increases operational storage costs. Therefore, this study aims to implement material requirement planning (MRP) in managing raw material inventories. The Min-Max, Reorder Point (ROP), and Economy Order Quantity (EOQ) methods are used in this research. These three methods are implemented on item code CLKG-15000294 from the PT Sidoraya Utama (PT.SRU) order. The result indicates that the most suitable methods to be implemented according to company conditions are Min-Max and Reorder Point (ROP). The Min-Max method is applied to determine the minimum stock of raw materials, the maximum stock of raw materials, and also the quantity that must be ordered. Meanwhile, the Reorder Point (ROP) method is used as a benchmark for repurchasing after reaching that point. Moreover, the study results also showed that the Min-Max method saved the cost of purchasing PE 1 raw materials by , PE 2 raw materials by , and PE 3 raw materials by .

Introduction

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The plastic packaging industry has grown rapidly over the past decade. In 2023, the demand for plastic products is recorded as 8.227 million tons, while 7.1 million tons are produced in Indonesia [1]. High demand for plastic products creates tough competition. That situation led each company to conduct the production process efficiently and exceed customer expectations. The plastic blown film company produces many types of products, consisting of T-shirt bags, sheet on roll, bag on roll, flat bag, tubing on roll, soft loop, hand gloves, garbage bag, and others. With a wide product, the plastic blown film company has abundant customers and needs a large supply of raw materials. The company investigated in this study is located in Mojokerto, East Java, and manufactures plastic bags for both export and domestic markets. It serves 165 active customers and 37 passive customers. Active customers are those who place regular orders, while passive customers only order for specific events. To fulfill customer demand, the company operates six blown film machines, four automatic bagcutting machines, one punching machine, and two mixers. Therefore, proficient inventory management is needed by the company to ensure customer satisfaction at product delivery time and, on the other hand, avoiding the overstock of raw materials. Overstocking of materials tends to lead to material deterioration and increase inventory costs. Currently, the company does not apply a systematic method to calculate raw material requirements. Instead, staff rely solely on personal experience, leading to a high risk of either overstocking or stockouts. The largest overstock case was identified in the plastic bag product coded CLKG-15000294, ordered by PT Sidoraya Utama (PT. SRU). The main issue lies in the fact that product CLKG-15000294 is a make-to-order (MTO) item with a very large volume, making it difficult for the company to determine the required amount of raw material accurately. Therefore, an appropriate method is needed to estimate raw material
requirements for this product. The raw materials used for manufacturing product CLKG-15000294 consist of three variants of low-density polyethylene (LDPE) and recycled LDPE. The use of recycled LDPE not only reduces production costs but also helps minimize environmental impact [2]. The utilization of recycled materials has gained considerable attention among researchers. For example, a previous study conducted a case study in a plastic manufacturing company [3], focusing on the allocation of recycled material orders and determining the optimal order allocation.
That problem can be solved by utilizing Material Requirement Planning (MRP). MRP is known as a method to determine the amount of materials in the production process [4]. As the most popular method in material management, the material needed can be planned to be received on time with the correct amount and avoid overstocking or shortage of supplies [5]. Therefore, many of the previous studies conducted research for utilizing and developing the method. The closed-loop MRP and MRP II have been proposed in the later period that focused on various aspects of its operation and impact on the firm [3]. Enterprise resource planning (ERP) was developed in the same direction as MRP [6, 7]. Furthermore, for incorporating market demand, demand-driven MRP (DDMRP) was developed. The main feature of this method is incorporating dynamic demand from the customer. MRP and the variations are called push method material management planning. On the other hand, the pull flow system tends to control work-in-process items to the final demand of customers. Several methods of the pull system have been developed. One of the most important methods of a pull system is a kanban system that has a similar approach to just-in-time (JIT) philosophy [8,9].
Since plastic bag products are manufactured based on production volume forecasts related to customer orders, and use several similar materials with variations, the MRP method is considered more suitable for addressing the problem compared to JIT. Specifically, this applies to the plastic bag product with code CLKG-15000294. In general, JIT offers the advantage of reducing holding costs and becomes a more cost-effective system as demand levels increase [10]. However, JIT carries a risk of stockouts [9,11]. Moreover, because several products share similar materials, a stockout situation would not only affect product CLKG-15000294 but also many other products. Given this condition, the MRP method is more appropriate to implement in the company, as it minimizes the risk of stockouts and ensures raw material availability [12].
Several implementations have been applied using MRP. The previous research dealt with the order release mechanism [13]. The research compares and simulates the methods for handling highly varied flow and job shops with bottlenecks. Furthermore, the research compares the Drum-Buffer-Rope (DBR) method and Workload Control in terms of order release to the production floor. The improvement of production planning through finite-capacity MRP is also proposed in another research [14]. The research proposed a capacity-oriented MRP that combines the traditional MRP procedure with linear programming. Unfortunately, the previous research did not conduct empirical research and compare the efficiency of several methods in a blown plastic company under uncertainty orders in MTO circumstances.
In a plastic injection molding company, a hybrid MRP-JIT system was proposed [15]. The study applied the MRP system to manage polymer plastic pellets as raw materials and developed a conceptual design of the MRP-JIT system to address production challenges. However, the research did not discuss the practical implementation of MRP methods, nor did it compare alternative methods to determine the most suitable approach for the plastic industry. In another study on the plastic industry, several MRP methods were applied in a blow molding company to produce plastic bottles [16]. Unfortunately, this research did not present data on existing material stock or purchasing costs. Moreover, the study did not provide a comparison between the current practice and the proposed method. A further case study in a plastic company also employed MRP [17]. The motivation for this research stemmed from the fact that company staff managed raw materials largely based on personal experience and observation, without systematic calculation. Although the study conducted empirical research grounded in the company's actual problems, it did not explore multiple MRP methods or provide a comparative analysis of the results.

Our research has the same spirit as the others research in utilizing the MRP method. Similar to [15], this study examines a plastic product company and addresses a real problem in a plastic bag packaging company. Furthermore, continuing and improving the previous research [17], by considering the very large amount of the raw material order and the make-to-order product of the plastic bag product with code CLKG-15000294, our research intends to compare several methods of MRP to find the most suitable method. Therefore, this research utilizes min-max, reorder point (ROP) and Economic Order Quantity (EOQ) methods for solving the problems. This research contributes to the company choosing the most appropriate method for managing the material and inventory planning. By using the most suitable method, the plastic blown film company can manage material requirements efficiently, and avoid overstock of raw materials. The remainder of this paper is organized as follows. Section 2 illustrates the model development and analytical models. Section 3 presents the results and discussion. Section 5 summarizes present conclusions and provides recommendations for future work.

Methodology

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The company used some variations of raw material when producing plastic-blown film using six different machines. For producing a plastic bag product with the code CLKG-15000294 that was ordered by PT Sidoraya Utama (PT.SRU), the company uses LDPE with three variations. The product is consumed at LDPE variant LDPE variant LDPE variant 3 , and recycled LDPE. The Bill of Material (BOM) of product CLKG-15000294 is shown in Fig. 1.

Fig. 1 Bill of Material of Product CLKG-15000294

Fig. 1. Bill of Material of Product CLKG-15000294

Based on the Bill of Materials (BOM), the production of 1 kg of product CLKG-15000294 requires 0.75 kg of LDPE Variant of LDPE Variant 2, 0.10 kg of LDPE Variant 3, and 1.00 kg of clear recycled LDPE. To address the problem, three different methods are applied: the Min-Max method, the Reorder Point (ROP), and the Economic Order Quantity (EOQ). After performing the calculations and establishing the MRP using these methods, the results are compared to identify the most suitable approach. The research framework is illustrated in Fig. 2.

Fig.2. Method for Solving the problem

Fig.2. Method for Solving the problem

Firstly, we identified the problem for the company, and found that there was overstock and also stock out material for producing plastic bag CLKG-15000294. Then, we explore the suitable method for solving the problem. Furthermore, based on the collected data, we establish the Bill of Material (BOM). Secondly, utilizing the BOM, we calculate material requirements using three different approaches. In the last stage, based on the results, we conduct an analysis, make a comparison between the approaches, and make a conclusion and recommendation to the company.

Result and Discussion

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For overcoming overconsumption of the raw materials, this research utilizes the MRP method for controlling and regulating the supply of raw materials to the production. In this research, we conduct empirical experiments in the company. The data was collected from the production period 2023 at the company.

Master Production Schedule

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Based on the data of the company, Master Production Schedule of product plastic bag CLKG15000294 is shown in Table 1.

Table 1. Master Production Schedule and demand CLKG-15000294 period 2023

Month
Date
Demand
(kg)

7
15
16
17
23
24
26
27
28
29
30
31

January

1,000

1,000

February

625

625

March

350

350

April

325

325

May

600
600

June

0

July

625

500
625

50
1,800

August

1,175

1,175

September

0

October
625

625

1,250

November

0

December

1,175

1,175

Average
691,67

Table 1 shows that the demand for product CLKG-15000294 averages 691.67 kg per month. The Master Production Schedule (MPS) in Table 1 covers the production plan for the year 2023, from January to December. This dataset was selected for the present study because it represents the most recent company data. The results indicate that the highest demand occurred in July, with fluctuations throughout the year. The product is a type of plastic packaging used for chicken meat; therefore, its demand is closely influenced by fluctuations in chicken meat consumption. This circumstance leads to an increase in product demand whenever the demand for chicken meat rises.

Purchased Material

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Furthermore, we also found the company data of purchasing data of raw material that shown in Table 2.

Table 2. Amount of Purchasing Raw Material

Materials | Month | Amount (Kg)
LDPE 1 | January | 24,750
March | 24,750
May | 24,750
June | 24,750
July | 24,750
August | 24,750
October | 24,750
November | 24,750
December | 24,750
LDPE 2 | January | 15,000
March | 15,000
June | 15,000
July | 15,000
August | 15,000
November | 15,000
December | 15,000
LDPE 3 | January | 15,000
April | 15,000
May | 15,000
July | 15,000
August | 15,000
December | 15,000

Table 2 indicates that the company purchases fixed-lot quantities of LDPE 1, LDPE 2, and LDPE 3 periodically throughout 2023, from January to December, while recycled LDPE is produced internally from recycled materials. The table further shows that the purchasing volume for LDPE 1 is , whereas LDPE 2 and LDPE 3 are each purchased at . The purchasing quantities of these raw materials remain fixed each month, as company staff rely solely on experience rather than systematic calculation in determining procurement needs.

Safety Stock

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Based on the company data, we calculate the safety stock for the company. Safety stock is very important for a manufacturing company for ensuring the company can fulfill the customer order on time. The safety stock is calculated based on eq.1.

Where:

Materials | Month | Amount (Kg)
LDPE 1 | January | 24,750
March | 24,750
May | 24,750
June | 24,750
July | 24,750
August | 24,750
October | 24,750
November | 24,750
December | 24,750
LDPE 2 | January | 15,000
March | 15,000
June | 15,000
July | 15,000
August | 15,000
November | 15,000
December | 15,000
LDPE 3 | January | 15,000
April | 15,000
May | 15,000
July | 15,000
August | 15,000
December | 15,000

The calculation result is described in Table 3.

Table 3. Safety stock calculation

Material | Maximum uses (Kg) | Maximum Lead time (days) | Average uses daily (Kg) | Average Lead time (days) | Safety Stock (kg)
LDPE 1 | 881 | 7 | 17.05 | 4.5 | 6,092
LDPE 2 | 59 | 7 | 1.14 | 4.5 | 406
LDPE 3 | 118 | 7 | 2.27 | 4.5 | 812

Re-Order Point (ROP)

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The Reorder Point (ROP) indicates the time at which the company should purchase raw materials to avoid stockouts. At the same time, ROP helps minimize the risk of overstocking. In this study, the ROP method is applied because it enables the company to prevent stockouts, reduce excess inventory, and maintain sufficient stock to meet customer demand. The calculation of ROP takes into account the lead time of material orders to determine the appropriate ordering period, as expressed in Eq. (2).

Where:
ROP = Reorder point
Average uses yearly
LT = Lead time
SS = Safety stock
The calculation result of ROP is shown in Table 4.

Table 4. Re-Order Point

Materials
Average uses yearly (Kg)
a
Lead time (days)
b
Safety Stock (kg)
c
ROP (kg)
c+(axb)

LDPE 1
518.75
4
6,092
8,167

LDPE 2
34.58
4
406
544

LDPE 3
69.17
4
812
1,089

Min-Max Method

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The min-max method is used to determine the minimum and maximum stock of raw material in the warehouse. Furthermore, the min-max method can be used to decide the amount of material ordered by the company. This research uses min-max as one method in the research since simpler and triggers reorders based on set levels and triggering an order when stock falls to the MIN level to replenish it up to the MAX level. The method consists of:

a. Determine minimum stock

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Defined as the minimum amount of raw material that should be sufficient for the production process. The amount is calculated using eq. 2.
The calculation result is described un Table 5.

Table 5. Minimum Stock for CLKG-15000294

Materials
Average uses yearly (Kg)
a
Lead time (days)
b
Safety Stock (kg)
c
Minimum stock (kg)
c+(a×b)

LDPE 1
518.75
4
6,092
8,167

LDPE 2
3.58
4
406
544

LDPE 3
69.17
4
812
1,089

As shown in the table, minimum stock is equal to ROP in Table 4.
b. Determine Maximum Stocks

After calculate the minimum stocks, the method uses eq. 3 for determining the maximum stock.

Where:
MaxS = Maximum Stock
AvgU = Average uses yearly
LT = Lead time
SS = Safety Stock
The result of the maximum stock calculation is described in the Table 6.

Table 6. Maximum Stock for CLKG-15000294.

Materials
Average uses yearly (Kg)
a
Lead time (days)
b
Safety Stock (kg)
c
Max stock (Kg)
2(axb)+c

LDPE 1
518.75
4
6,092
10,242

LDPE 2
34.58
4
406
683

LDPE 3
69.17
4
812
1,366

After calculating the minimum and maximum amount of raw materials needed for producing CLKG15000294, we intend to determine the order quantity based on the min-max method. The calculation follows eq. 4.

Economic Order Quantity (EOQ)

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In this paper, we also establish MRP by utilizing EOQ. Economic Order Quantity is known as a method for searching for the optimum number of orders to minimize the purchasing cost. The EOQ is calculated by using eq.5.

Where:
EOQ = Economic Order Quantity
D = Yearly uses
S = Price/Unit product
H = Holding cost
The calculation result is shown in Table7.

Table 7. Economy Order Quantity for CLKG-15000294

Materials
Holding cost (Rp.)
H
Yearly uses (Kg)
D
Price/unit product (Kg)
S
EOQ (kg)
√(2DS/H)
EOQ (Rp.)

LDPE 1
182.50
6,225
33,030
1.501
49,580,120.92

LDPE 2
182.50
415
42,086
437
18,412,556.31

LDPE 3
182.50
830
49,121
668
32,833,866.62

Discussion

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In this paper, we use three kinds of MRP methods, which consist of the min-max method, ROP and EOQ. In this section, we discuss the result and compare the three methods by considering the situation on the company.
a. Number of Inventory

The comparison of the number inventory is described in Table 8.

Table 8. Comparison the number of inventory Min-Max, Reorder Point (ROP), and Economy Order Quantity (EOQ)

Materials | Existing Condition (Kg) | Min stock (Kg) | Order quantity Min-max (Kg) | ROP (kg) | EOQ(kg)
LDPE 1 | 21,500 | 8,167 | 4,150 | 8,167 | 1,501
LDPE 2 | 15,000 | 544 | 277 | 544 | 437
LDPE 3 | 15,000 | 1,089 | 553 | 1,089 | 668

Furthermore, we also compare the cost between the methods that described in Table.9.

Table 9. Total Cost Comparison between Min-Max, Reorder Point (ROP), dan Economy Order Quantity (EOQ)

Materials | Existing Condition (Rp.) | Min stock (Rp.) | Order quantity (Rp.) | ROP (Rp.) | EOQ (Rp.)
LDPE 1 | 710,135,712 | 269,752,595 | 137,072,.707 | 269,752.,595 | 49,580,121
LDPE 2 | 631,290,000 | 22,914,434 | 11,643,793 | 22,914,434 | 18,412,556
LDPE 3 | 73,.813,440 | 53,489,403 | 27,180,229 | 53,489,403 | 32,833,867

Based on Table 8 and Table 9, for LDPE 1 and LDPE 2, the minimum amount of inventory and total cost are achieved by using EOQ. The use of EOQ has economic advantages compared to the existing quantity order. Furthermore, the min-max method and ROP, even though not superior to the EOQ method, are still more economical compared to the existing quantity order. In the use of EOQ, the material cost per unit depends on the number of orders, in which more ordered materials tend to decrease the cost [18]. However, based on the master scheduling in Table 1, the demand in July is and the uses of LDPE 1 are equal to , while the economic order using EOQ is only
. That situation will lead to stocking out raw materials when considering the material lead time. Therefore, good material requirement planning should consider not only the total cost but also lead time and safety stock.
The min-max method has a goal to arrange material requirement planning, minimize the cost, and avoid stockouts [19]. The min-max method also considers the inventory continuously, meaning that when raw material stock reaches the reorder point, the company will issue the reorder [20]. Moreover, the min-max method is most suitable for the raw material purchasing process under uncertainty and fluctuation [21]. Therefore, to overcome the fluctuating demand of the CLKG 15000294 product, the use of the min-max method is more appropriate. Even if the cost is higher compared to the EOQ method, the min-max method is selected to solve the problem, since the method provides more economical material planning. The comparison is provided in Table 10.

Table 10. Cost Comparison

Materials | Existing Cost (Rp.) | Cost Min-max method (Rp.) | Savings Cost (Rp.)
LDPE 1 | 710,135,712 | 137,072,707.20 | 680,409,100.80
LDPE 2 | 631,290,000 | 11,643,793.33 | 619,646,206.67
LDPE 3 | 736,813,440 | 27,180,229.12 | 709,633,210.88

The most advantages aspect of the min-max method is that the company can determine the minimum inventory precisely. Furthermore, the company can respond to the demand fluctuations efficiently and deal with the rapid change in demand. Even though the min-max method is considered the most appropriate method for solving the problem considering the circumstances of the CLKG 15000294 product, the ROP and EOQ methods have several advantages with some constraints. The ROP method is utilized in order to determine the time to issue the purchased order. However, the drawback of only considering the ROP method is that the company has difficulty ensuring the lower limit and upper limit of raw material inventory. On the other hand, the EOQ method is the best method in the scenario when the need for raw materials is certain, the lead time can be determined precisely, the cost structure can be fixed, there is sufficient capacity inventory, and there is enough money to purchase the lot of material.

Conclusion

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In this paper, we propose the most appropriate method for implementing Material Requirement Planning for managing raw materials in a plastic blown film company. For numerical experiment purposes, we assessed bag plastic code CLKG 15000294 in the plastic blow molding company. Furthermore, we compared the results using three methods consisting of the min-max method, ROP, EOQ, and existing conditions in the company. The result indicates that the company has purchased raw materials excessively and leads to inventory overstock. Moreover, the overstocking also causes inefficient costs in the company. All of the MRP methods show a better result compared to the existing material planning in the company. However, dealing with the fluctuation and uncertainty of product CLKG 15000294, the most appropriate method is the min-max method. The company can implement the EOQ method if the need for material is certain, the lead time can be determined precisely, and the company has no constraints on inventory and sufficient funds to purchase the lot of material.

References

The original version of this paper is available on https://www.scientific.net/EI.17.97.pdf

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Engineering Innovations, ISSN: 2813-1002, Vol. 17, pp 109-122

doi: 10.4028/p-R2QkXI

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

Submitted:
2025-02-20

Revised:
2025-09-11

Accepted:
2025-09-29

The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

The original version of this paper is available on https://www.scientific.net/EI.17.109.pdf

Mohamad Fajar Natawijaya , Tota Pirdo Kasih

JI. Kyai H. Syahdan No. 9, Kemanggisan, Kec. Palmerah, Kota Jakarta Barat, DKI Jakarta, 11480

mohamad.natawijaya@binus.ac.id, tota.kasih@binus.ac.id

Keywords: Lean manufacturing, pareto chart, process cycle efficiency, manufacturing lead time, value stream mapping.

Abstract

This study investigates the implementation of lean tools to enhance process cycle efficiency and reduce production lead time by at least in a case study of pipe sleeve fabrication at one of manufacturing company in Indonesia. Improving production efficiency is a critical investment for manufacture sustainability and competitive advantages. The application of lean tools, such as value stream mapping and pareto analysis, can help reduce process inefficiencies by eliminating non-value-added activities, such as machine breakdowns, waiting, and material handling. The results demonstrate that the implementation of lean tools can increase Process Cycle Efficiency (PCE) from to and reduce manufacturing lead time (MLT) by . This case study provides insights into how lean tools can be applied to improve productivity in manufacturing processes. Lean tools can be implemented across various manufacturing industries to enhance their productivity. Broader implications of implementing lean method suggest that companies can enhance productivity, reduce cost of production, improve quality, and ultimately increase their competitive advantage in the market.

Introduction

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Economic globalization has had a profound impact on the global economy, both in terms of domestic and international markets. One of the most notable effects of globalization is the increased competition in the manufacturing sector [1]. In today's globalized economy, manufacturing industries face increasing competition. In order to succeed, companies must focus on providing high quality products and services that meet the needs of their customers. Lean manufacturing is a production strategy that emphasizes waste reduction and efficiency, and it is becoming increasingly popular in global manufacturing [2]. Waste is any activity that does not add value to the product or service being produced it can be reduced through lean manufacturing methods, which involve everyone in the organization working together to identify and eliminate waste [3]. This strategy is led by automotive manufactures and their equipment suppliers, who have seen significant benefits from adopting lean manufacturing. According to a recent survey, approximately of manufacturing companies in the United State have adopted lean or are in the process of doing so. [4]. The implementation of lean manufacturing concepts is full of flexible tools and techniques such as just in-time inventory (JIT), kanban, kaizen, 5S, single minute exchange of dies (SMED), one point lesson (OPL), and value stream mapping (VSM). Lean manufacturing is a soft technology that accommodates qualitative and quantitative techniques as well as computing to demonstrate a commitment to continuous improvement. Therefore, lean manufacturing systems are flexible for implementation in manufacturing and service industries [5].

The process cycle efficiency (PCE) is a measure of the efficiency of a production process. It is calculated by dividing the value-added time by the total cycle time. A higher PCE indicates a more efficient production process. In a study conducted by Md Abu Sayid, he was able to improve the PCE of shoes production in Bangladesh from to [6]. This improvement was achieved through the implementation of lean manufacturing principles and practices. Besides, in other cases a researcher in Bangladesh, has successfully improved the efficiency of mango juice production in the
food industry. He did this by implementing lean tools, which are a set of principles and practices that focus on eliminating waste and improving efficiency. As a result of his research, the process cycle efficiency of mango juice production increased from to [7]. This represents a significant improvement in efficiency, and it has the potential to lead to a number of benefits for the food, including reduced costs, improved quality, and increased customer satisfaction. In this study investigated the use of lean tools to improve the efficiency and lead time of pipe sleeve fabrication at a leading manufacturing company in Indonesia. The study found that lean tools led to significant improvements in both metrics.

Methodology

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The research methodology was designed to systematically examine the pipe sleeve fabrication process and identify sources of inefficiency. A case study approach was employed, focusing on direct observations, process documentation, and semi-structured interviews with operators and supervisors. Lean manufacturing tools - specifically value stream mapping (VSM), pareto analysis, and fishbone diagrams were applied to map current processes, quantify waste, analyze root cause. Activities were classified into value-added (VA), necessary but non-value-added (NNVA), and non-value-added (NVA), enabling a structured assessment of process performance.

This methodological framework provides a comprehensive basis for analyzing the current state of fabrication, diagnosing inefficiencies, and formulating targeted improvements. The subsequent section presents the results of this analysis and discusses their implications for manufacturing performance.

Fig 1. Methodology

Fig 1. Methodology

Result and Discussion

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The application of lean tools yielded valuable insights into the inefficiencies present within the pipe sleeve fabrication process. The current state Value Stream Mapping (VSM) revealed that considerable proportion of production time was consumed by non-value-added and necessary but non-value-added activities, including prolonged waiting times, redundant inspections, and inefficient material handling. Pareto analysis further emphasized that human related factors were the dominant contributors to waste, while machine related factors played a comparatively smaller role. Fishbone diagrams were then employed to explore the underlying causes of these inefficiencies across the dimensions of man, method, machine, and materials.

The following discussion elaborates on these findings, highlighting how each category of waste emerged, how it aligns with prior studies on lean implementation, and what strategic measures can be applied to reduce lead time and enhance process cycle efficiency.

Observed Process Fabrication. The pipe sleeve fabrication process is shown in Fig. 1, The raw materials used in this process were not a major concern in this study. Instead, the focus was on identifying and eliminating waste and downtime. Production delays were caused by a variety of factors, and downtime was measured in terms of non-value-added time.

Fig 2. Stage processing of fabrication pipe sleeve

Fig 2. Stage processing of fabrication pipe sleeve

Value Stream Mapping (VSM). Value Stream Mapping (VSM) is a lean manufacturing tool that can be used to identify and eliminate waste in a production process [8]. Studies have shown that value stream mapping (VSM) is effective in improving production speed, reducing costs, saving time, and minimizing environmental impact. value stream mapping (VSM) is also considered flexible and adaptable for redesigning production systems with different flow paths [9]. In value stream mapping (VSM), activities are classified into three types: value-added (VA), non-value-added (NVA), and necessary but non-value-added (NNVA). VA activities are those that increase the value of a product from the customer's perspective. NVA activities do not create value and are considered waste, such as waiting, rework, or unnecessary movement. NNVA activities do not directly add value but are still required under current process or regulatory conditions, such as inspections or equipment setup. This classification helps identify which parts of the process should be improved or eliminated to increase efficiency.

Current state VSM of Fabrication Pipe Sleeve. The current state value stream mapping (VSM) of a pipe sleeve fabrication system is shown in Fig. 2, value stream mapping (VSM) shows that production begins upon receiving customer request for specified quantity. The required raw materials are procured from suppliers, stored in the warehouse, and subsequently transferred to the production floor for cutting, inspection, bevel and press/roll processing, and final inspection. The finished pipe sleeve is then packaged and shipped.

Value stream mapping (VSM) also reveals the presence of 31 bottlenecks in the process, which hinder the smooth flow of materials and information. The analysis indicates that 2,919 seconds,
equivalent to approximately of the total production time, were classified as non-value-added (NVA) activities. This represents a substantial portion of time that could potentially be eliminated. In addition, 6,977 seconds, or about of the total production time, were categorized as necessary but non-value-added (NNVA) activities, which could be reduced through process streamlining and the elimination of inefficiencies.

Overall, the study concludes that the implementation of lean tools has the potential to significantly enhance the efficiency of pipe sleeve fabrication by eliminating NVA activities and reducing VA time. These improvements are expected to increase productivity, minimize downtime and mitigate the risk of production delays.

Table 1 Current stage NVA, NNVA, NVA

Note: VA time is an activity that provides added value;
NVA time is an activity that does not provide added value;
NNVA time is an activity that does not provide added value, but is still necessary

Value Stream Mapping
Process Name: Fabrikasi Pipe Sleeve

Fig 3. Current state value stream mapping

Fig 3. Current state value stream mapping

Current state Manufacturing Lead time of Fabrication Pipe Sleeve. Manufacturing lead time is the time it takes to produce a product from start to finish, including all the steps in between, whether they are necessary or not [10]. The data required to calculate manufacturing lead time (MLT) includes the time of value added (VA), non-value-added but necessary (NNVA), and non-value added (NVA) activities that have been carried out in the production process. The formula for calculating MLT is as follows:

The fabrication process is carried out for shifts per day, each shift lasting 28,800 seconds, excluding lunch time and planned downtime.

Current state Process Cycle Efficiency of Fabrication Pipe Sleeve. PCE is how efficiently a manufacturing process produces value-added products. It is calculated by dividing the time required to produce value-added (VA) by the manufacturing lead time (MLT) and expressing the result as a percentage. The formula PCE can be shown in formula (2) below:

The study found that the value-added (VA) time for pipe sleeve fabrication was 6,534 second and the MLT was 16,430 seconds. The resulted in a PCE value of , which is above the international competitiveness level of [11]. The study sought to improve process cycle efficiency (PCE) to mitigate delays in meeting customer demand by applying lean tools such as value stream mapping (VSM), pareto analysis, fishbone diagram, pareto charts.

Fishbone Diagram. Delays can be caused by a large amount of waste during the production process. Waste analysis can be done when the material is already on the production floor. Waste is any activity or action that does not add value to the product or service being produced. Lean manufacturing identifies seven types of waste that must be eliminated in production systems [12]:

Motion: unnecessary movement of people or equipment.

Defect: products or services that do not meet customer requirements.

A fishbone diagram is used to identify the root cause of a waste problem. The analysis considers four factors: man, machine, methods, materials. Figure 4 shows one of the wastes, namely defects in production.

Fig 4. Fishbone diagram waste defect

Fig 4. Fishbone diagram waste defect

Man factors such as fatigue, environmental disturbances, and poor physical health can lead to a loss of concentration, which in turn increases the likelihood of production defects and subsequent rejections. Similarly, rushing and a lack of focus may contribute to errors as workers are more prone to overlooking details and misinterpret instruction. Machine factors including wear and tear on tools and components due to inadequate preventive maintenance, as well as calibration errors arising from irregular maintenance schedules, are also common causes of product defects. Method factors such as insufficient spacing and inadequate packaging, can further result in product damage. For example, the absence of pallets and the use of simple block separators may exacerbate these issues. Materials factor on defects are frequently associated with prolonged storage, exposure to fluctuating environmental conditions, and improper storage or handling practices. Refer with: Fig. 5, illustrates one of the identified wastes - transportation, which is caused by man, method, machine and materials factors.

Fig 5. Fishbone diagram waste transportation

Fig 5. Fishbone diagram waste transportation

The Overproduction factors are primarily associated with a lack of coordination caused by inefficient processes, frequent changes in delivery allocation, incorrect material placement due to misidentification, and delays in material handling resulting from overloaded trucks. Machine factors contributing to transportation waste include production downtime caused delayed material handling and interruptions in processes from other plants or vendors due to inadequate machine capacity within the workshop area. Method factors include congested factory traffic caused by excessive material transport trucks and inefficient facility layouts arising from the simultaneous presence of various products in the ready for delivery (RFD) area. Material factors include the generator of excessive scrap material due to high product tonnage, and frequent changes in fabrication location caused by long distances between workshops. Refer with: Fig. 6, illustrates one of the identified wastes, excess processing, which is caused by man, method, machine and materials factors.

Fig 6. Fishbone diagram waste excess processing

Fig 6. Fishbone diagram waste excess processing

Man factors contributing excess processing waste include repeated checks on quantities, cutting plans, and drawings due to sudden changes, as well as redundant inspections driven by fear of errors and lack of operator confidence. Errors during processing also lead unnecessary rework and
additional activities. Method factor include the application of procedures that differ from previously established practices, particularly when tight tolerance is required. Material factors involve poor material quality caused by defects or corrosion, which necessitate special treatment to continue the fabrication process, along with additional steps for materials that do not meet raw material specifications. Machine factors include poor machine condition resulting from a lack of awareness regarding the importance of predictive maintenance, and limited machine capacity to meet production demands. Refer with Fig. 7, illustrates one of the identified wastes, namely motion, which is caused by the man, machine and method factor.

Fig 7. Fishbone diagram waste motion

Fig 7. Fishbone diagram waste motion

Man factors contributing to motion waste include operators spending excessive time retrieving materials or tools due to inadequate tool preparation, as well as inefficient movements when handling materials caused by uncomfortable work areas and unnecessary variations in motion. Machine factors include poor machine condition, such as excessive friction and vibration, which lead to additional non-value-added movements by operators. Furthermore, inefficient facility layouts, particularly when machines are positioned far apart, exacerbate this type of waste. Method factors include inefficient work methods employed by operators and suboptimal fabrication sequences resulting from inadequate planning. Refer with: Fig. 8, illustrates one of the identified wastes - inventory, which arises from man, method, machine and materials factor.

Fig 8. Fishbone diagram waste inventory

Fig 8. Fishbone diagram waste inventory

Man factors contributing to inventory waste include errors in ordering caused by poor communication and insufficient planning of material requests and supply, often resulting from late work orders. Method factors involve inefficient data recording due to the absence of an ERP/MRP system, improper storage and handling of materials caused by excess deadstock, and suboptimal inventory management stemming from high inventory levels in logistics. Material factors include a high frequency of product repairs due to substandard production outputs and poor material quality resulting from prolonged storage. Machine factors contributing to inventory waste consist of downtime and production delays due to machine conditions, delayed material handling that leaves materials waiting to be transferred to the ready for delivery (RFD) area, and material accumulation near machines caused by limited machine capacity relative to high demand. Refer with Fig. 9, illustrates one of the identified wastes, namely waiting, which arises from man, method, materials, and machines factors.

Fig 9. Fishbone diagram waste waiting

Fig 9. Fishbone diagram waste waiting

Man factors contributing to waiting waste include delays in QC inspection due to the remote location of the QC team from the work area, operators waiting for cranes and forklifts to move products and materials as a result of high utilization, and lack of coordination among teams that creates dependencies and prolongs waiting times between processes. Method factors involve delays in shipment preparation caused by unmet production schedules. Material factors include limited material availability due to pending material arrivals as well as delays associated with forklift and cranes that are either under maintenance or insufficient in number. Machine factors contributing to waiting waste consist of downtime resulting from capacity constraints, delays caused by dependence on preceding processes, and reduced machine speeds due to declining reliability.

Pareto Analysis. This statistical technique is a powerful tool for decision-making that significantly contributes to quality improvement. It is based on the Pareto principle, which states that approximately of the causes are responsible for of the problems [7]. In the words, the majority of issues arise from a relatively small number of root causes. The pareto chart follows the rule, with the threshold included to highlight the critical factors that require the greatest attention, as they lie below this line. In this study, the Pareto chart was applied to identify the of factors responsible for of non-value-added (NVA) activities.

Table 2 Cumulative percentage of frequency from waste factor

Factors | Frequency | Cumulative Frequency | Cumulative percentage
Man | 7 | 7 | 44%
Method | 4 | 11 | 69%
Materials | 3 | 14 | 88%
Machine | 2 | 16 | 100%

The Pareto chart was employed to analyze the negative impacts within the project. This tool facilitated the construction of a Pareto diagram that displays the factors contributing to waste, with the X -axis representing the types of waste and the Y -axis representing the cumulative percentage frequency. The most significant contributing factor was identified as the man factor, where human error frequently occurred throughout the process, while machine - related damage accounted for the lowest contribution. The factors associated with waste and NVA activities across different stages of the fabrication process are presented in Table 2, along with their corresponding cumulative frequency percentages. Refer with Fig. 10, the man factor was the most dominant contributor, accounting for of waste in the fabrication of pipe sleeves, whereas the machine factor represented the lowest contribution at . The waste factors are arranged in descending order according to their frequency. Through this diagram, waste - causing factors can be easily identified, allowing for the development of targeted and effective measures to mitigate and ultimately eliminate them.

Fig 10. Pareto chart

Fig 10. Pareto chart

Continuous Improvement. Continuous improvement, commonly referred to as kaizen, is one of the most fundamental and effective principles in lean manufacturing. Kaizen represent a philosophy of incremental and continuous improvement that can be applied across various context, including domestic, societal, and organizational settings. Within the workplace, kaizen emphasizes the active involvement of all personnel, from top management to top shop-floor workers in identifying and implementing small, gradual improvements [14]. This philosophy adopts a long-term perspective, focusing on systematic efforts to achieve incremental goals that enhance both quality and efficiency. The principle of kaizen supports simple yet meaningful improvements to end-to-end business processes as part of a broader continuous improvement strategy. Globally, many companies have adopted kaizen to increase productivity, improve quality, and enhance profitability, while minimizing cost, time, and effort [14]. Refer with Fig. 11 illustrates a kaizen-based strategy for sustaining longterm lean manufacturing implementation.

Fig 11. Kaizen implementation for strategic planning

Fig 11. Kaizen implementation for strategic planning

Future Value Stream Mapping of Fabrication Pipe Sleeve. Refer with Table 3 shows that the future process activity mapping (PAM) consists of only 19 production activities out of 31 . The first eliminated activities were material inspection processes, which were unnecessary upon material arrival but required when materials were transported from the supplier to the plant for fabrication. Operator inspections were also eliminated; inspections were conducted solely by Quality control, allowing operators to focus on their processes and accelerate production. Waiting for cranes, forklifts, and the Quality control team was also eliminated as these activities did not add value and could lead to delays if repeated. To avoid waiting, time schedules were implemented in the workshop for the Quality control team and crane usage, minimizing repetitive processes and waste, and preventing unnecessary movements. Additionally, post-process material arrangement was eliminated. To minimize this activity, clear planning was established regarding layout and inventory deadlines, preventing materials from being left in one place for extended periods.

Table 3 Future stage NVA, NNVA, NVA

Value Stream Mapping
Process Name: Fabrikasi Pipe Sleeve

Fig 12. Future Value Stream Mapping (VSM) of Pipe Sleeve Fabrication

Fig 12. Future Value Stream Mapping (VSM) of Pipe Sleeve Fabrication

Expected Manufacturing Lead Time (MLT) of Fabrication Pipe Sleeve. Manufacturing lead time (MLT) refers to the total time required to produce a single unit of output. A shorter lead time indicates a more efficient production process. In this study, the implementation of lean tools demonstrated the potential to reduce the manufacturing lead time in pipe sleeve fabrication. In the current state, the manufacturing lead time (MLT) was recorded at 16.43 seconds, whereas the future state - after applying lean improvement initiatives - is expected a manufacturing lead time (MLT) of 12,711 seconds.

Future state PCE of Fabrication Pipe Sleeve. In the future state, the effective application of lean tools is projected to reduce NNVA time from 6,977 seconds to 6,177 seconds, while eliminating NVA activities entirely, as non-value-added fabrication processes are removed. As previously mentioned, the process cycle efficiency (PCE) is expressed as the ratio of VA time to manufacturing lead time (MLT). Based on this calculation, the future state process cycle efficiency (PCE) is expected to reach which exceeds the international competitive benchmark of .

Fig 13. Comparison of PCE and MLT currently and after improvement

Fig 13. Comparison of PCE and MLT currently and after improvement

Process cycle efficiency (PCE) significantly improved from to following the evaluation and optimization of the pipe sleeve fabrication process. The primary driver of this improvement was the elimination of non-value-added (NVA) activities, which led to a substantial reduction in the manufacturing lead time (MLT). Refer with: Table 4, MLT decreased from 16,430 seconds to 12,711 seconds.

Table 4 Factors Contributing to the Improvement of PCE and MLT

PCE
(%)
MLT
(s)
Factor

Current
Condition
39.76
16,430
The MLT value was determined based on VA, NVA, and NNVA components. In the initial condition, the NVA category encompassed activities classified as waste, including redundant inspections, idle or waiting periods, and unnecessary movements.

Future
Condition
51.40
12,711
Following the improvement initiatives, the PCE value increased as each activity was systematically evaluated, with non-value-added activities being eliminated and sources of waste reduced. Consequently, process effectiveness was enhanced, lead time was shortened, and a 23% reduction in overall lead time was achieved.

Conclusion

The original version of this paper is available on https://www.scientific.net/EI.17.109.pdf

This study presents a case study of improvement in pipe sleeve fabrication by reducing manufacturing lead time (MLT) and improving process cycle efficiency (PCE) through lean tools. This study focused on operation renovation to eliminate non-value added (NVA) time, reduce manufacturing lead time (MLT), and improve process cycle efficiency (PCE) through value stream mapping (VSM), pareto chart, fishbone diagram, 5S, and kaizen. In conclusion, it can be concluded that lean tools are an effective way to identify waste and eliminate non-value added (NVA) and manufacturing lead time (MLT).

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The original version of this paper is available on https://www.scientific.net/EI.17.109.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.

Waiting: time spent waiting for materials, equipment, or people.

Transportation: moving materials or products unnecessarily.

Inventory: excess stock of materials, work in progress, or finished goods.

Overprocessing: performing unnecessary steps in the production process.

Keywords Index

B

Banana Bunch Stalks Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Banana Peels Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Biochar Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

C

Calibration A Study on Environmental Sensors for Low-Cost Weather Stations

Climate A Review on Biochar in Agriculture: Production, Applications, and Impacts

Crop Yield Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

E

Economic Apis Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Electrical Industry Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Eoq Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

I

Inventory Management Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

L

Lean Manufacturing The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Literature Review Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Low-cost Sensor A Study on Environmental Sensors for Low-Cost Weather Stations

M

Machine Learning In Economics Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Manufacturing Lead Time The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Min-max Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

Mrp Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

N

Natural Coagulants Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Net Present Cost; Dc Capacity Optimization; Self-consumption; Self-sufficiency; Net Metering; Residential Photovoltaic Systems Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

O

Open Data Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Orange Seeds Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

P

Pareto Chart The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Peels Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Plastic Blown Film Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

Process Cycle Efficiency The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Pyrolysis Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

R

R Programming Language Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Rop Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

S

Safety Behavior Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Safety Hazards Detection Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Sensor Bme 280 A Study on Environmental Sensors for Low-Cost Weather Stations

Soil Amendment Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Soil Fertility A Review on Biochar in Agriculture: Production, Applications, and Impacts

Surface Water Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Sustainable Agriculture A Review on Biochar in Agriculture: Production, Applications, and Impacts

T

Time Series Forecasting Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

Treatment Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

V

Value Stream Mapping The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

W

Weather Station A Study on Environmental Sensors for Low-Cost Weather Stations

Authors Index

A

Abayomi, O. Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Agboola, E. Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Ajayi, A. Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

Ananda, N. A Study on Environmental Sensors for Low-Cost Weather Stations

B

Budiawan, I. A Study on Environmental Sensors for Low-Cost Weather Stations

Burohman, A. A Study on Environmental Sensors for Low-Cost Weather Stations

C

Cahyo, W. Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Carrasco, J. Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

E

Ekawati, E. A Study on Environmental Sensors for Low-Cost Weather Stations

F

Faloye, O. Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

J

Jongbo, A. A Review on Biochar in Agriculture: Production, Applications, and Impacts

K

Kasih, T. The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

Kurniadi, D. A Study on Environmental Sensors for Low-Cost Weather Stations

M

Mukhlish, F. A Study on Environmental Sensors for Low-Cost Weather Stations

N

Natawijaya, M. The Impact of Lean Tools and Waste Analysis on the Improvement of Process Cycle Efficiency and Manufacturing Lead Time of Pipe Sleeve Fabrication: A Case Study

O

Obakanure, S. Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment

Ojo, O. Performance Evaluation of Orange Seeds and Peels as Natural Coagulants for Surface Water Treatment

Osorto, H. Web API of the Central Bank of Honduras: Evaluation of Its Integration in R and Perspectives for the Development of a Specialized Package

P

Pawenary, P. Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Purnomo, H. Safety Hazards Detections in Electrical Industry: A Systematic Literature Review

Purwoko, J. Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

Putri, A. Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company

Pérez, J. Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

R

Reyes-Duke, A. Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

T

Tayo, B. Comparison of Biochar Derived from Slow Pyrolysis of Banana Peels and Bunch Stalks in Agricultural Soil Amendment; A Review on Biochar in Agriculture: Production, Applications, and Impacts

V

Villatoro, H. Optimization of PV Self-Consumption Systems without Storage Based on Net Present Cost Using LabVIEW

W

Wicaksana, H. A Study on Environmental Sensors for Low-Cost Weather Stations

Wulung, R. Implementing MRP (Material Requirement Planning) for Managing Raw Material Inventory in an Indonesian Plastic Blown Film Company