Journal of Agricultural Machinery

Abstract This research seeks to determine the highest possible yield by integrating wastewater treatment plant sludge with food waste from plate scraps at Adama Science and Technology University (ASTU) in Ethiopia. Feedstock characterization and biogas co-generation were done on different Plate Scrap (PS), Wastewater Treatment Plant Sludge (WTPS), and 100 ml cow manure combination ratios. The feedstocks were evaluated for their TS and MC before combination, and TS, VS, TDS, COD, BOD, and pH after combination. This experiment was done in two rounds using three water baths and twenty-seven Batch Reactors (BR) with 2.5 L volume each. In the first round, eighteen reactors were used, and nine were used in the second experiment. Triplicate testing was used to evaluate the feedstock sample characteristics and to run the experiment. The reactors were operated for thirty-five days at a hydraulic retention time and a temperature of 50 °C. The daily biogas yield using the water displacement method, total biogas yield, and methane composition were measured and reported. Three sub-reactors were considered to find the average biogas yield of individual reactors. A notable increase in both daily and total biogas yield was observed with the reactor composition of 75% PS Injera (PSI) flat bread and 25% WTPS. The daily maximum and the average biogas yields were 220 mL and 810 mL, with the TS of 55,066 mg L^-1 and the VS of 51,000 mg L^-1. The maximum methane inside the produced biogas was 68%, from PSI75% and WTPS25%. This combination also showed the highest biogas yield.

Abstract Residual plastic mulch film pollution in agricultural fields threatens soil health and sustainable agriculture due to structural degradation and inefficient recovery. To address this, this study investigated the effects of mulch film thickness (0.006-0.014 mm), mulching duration (0-120 days), and two contrasting ecological regions in the Guizhou Province of China: Longgang Town (Kaiyang County) and Linquan Town (Qianxi County), on physical properties and recyclability in tobacco cultivation. Analyses of mechanical, optical, and recycling efficiency revealed that tensile, tear, and puncture strengths increase proportionally with thickness across identical durations, while elongation rates initially increase and then decline. Prolonged mulching reduces mechanical performance at fixed thicknesses, with longitudinal tensile and tear strengths consistently exceeding transverse values. Optical properties vary significantly: unused films exhibit peak light transmittance and haze, while 0.008 mm films achieve maximum transmittance, and thicker films (0.010-0.014 mm) show higher haze. Recycling efficiency correlates positively with thickness and inversely with mulching duration. After 120 days, recycling efficiency strongly correlates with longitudinal and transverse tear loads. Regional variations significantly affect the mechanical properties of 0.010 mm films, suggesting that 0.010 mm films may adapt better to diverse environments. Thicker films show higher recyclability after 120 days of mulching due to retained structural integrity. These findings systematically link physical degradation patterns to recyclability under field conditions, offering actionable insights for optimizing mulch film use, designing durable products, and improving recovery machinery. The study supports sustainable agricultural practices by balancing film performance, environmental adaptability, and end-of-life recovery efficiency.

Abstract Introduction
Improving the sustainability of agricultural systems requires the optimisation of inputs use, especially chemical fertilisers and pesticides, which are major contributors to energy consumption and environmental degradation. Variable Rate Technology (VRT), as a precision agriculture strategy, allows site-specific management of inputs based on spatial variability in soil fertility, weed distribution, and crop requirements. Although VRT has shown promise in enhancing resource-use efficiency, its integrated effects on energy indicators and environmental burdens in wheat production under irrigated conditions remain insufficiently investigated. This study assessed the impact of VRT on energy performance and environmental emissions in comparison with conventional uniform application, using a real six-hectare winter wheat field in Karaj, Iran.
Materials and Methods
A comprehensive field-scale simulation of VRT was conducted for nitrogen, phosphorus, potassium fertilisers, herbicides, and insecticides. Spatial maps of soil fertility and weed distribution were generated using UAV-based remote sensing and ground sampling. Two scenarios were examined: (1) VRT-based variable application of chemical inputs and (2) conventional uniform application. Energy inputs and other outputs were calculated based on standard coefficients and categorised as direct, indirect, renewable, and non-renewable. Environmental impacts, including Global Warming Potential (GWP) and pollutant emissions to air, water, and soil, were quantified using the ReCiPe 2016 Midpoint (H) method. All results were compared for the production of 34,800 kg of wheat.
Results and Discussion
Energy Indicators
The total energy input under VRT (131,631.57 MJ) was lower than that of consumed under conventional management (160,318.55 MJ). Direct and indirect energy uses declined by 12.64 MJ and 19.31 MJ, respectively, in the VRT system. VRT improved all energy indicators i.e., energy ratio increased to 6.82 (21.79% higher than the conventional method), energy productivity rose to 0.264 kg MJ⁻¹, and energy intensity decreased to 3.78 MJ kg⁻¹. Net energy under VRT reached 765,705.76 MJ, exceeding the conventional value. Major reductions were attributed to substantial decreases in herbicide use (80.40%) and potassium fertiliser (77%), driven by UAV-derived weed distribution maps and soil fertility maps.
Environmental Impacts
The GWP of the VRT scenario was 17,691.21 kg CO₂-eq, representing approximately a 20% reduction compared to the 22,202.74 kg CO₂-eq emitted under conventional application. Nearly half of the GWP originated from direct field emissions, followed by nitrogen fertiliser use. Optimisation of nitrogen rates and reduced field-level emissions were the primary contributors to the decrease. Pollutant emissions to the atmosphere also declined significantly: CO₂ by 16.2%, N₂O by 21.9%, and NH₃ by 22.5%. Waterborne pollutants were reduced, with nitrate declining by 22.4% and phosphorus by 38.9%. Heavy metal emissions to soil also decreased, with Pb reduced by 22.4% and Zn by 35.0%, while elements with naturally low accumulation (Fe, B, Mn, Mo) remained unchanged. These improvements align with previous international findings demonstrating the effectiveness of UAV-assisted VRT in reducing chemical inputs, enhancing energy efficiency, and minimising environmental pollution across diverse cropping systems.
Conclusion
The results demonstrate that implementing VRT for applaying chemical inputs in wheat production substantially reduces both energy consumption and environmental impacts. Compared with the conventional uniform method, VRT improved all energy indicators, decreased total energy inputs, and increased net energy output. Environmentally, VRT reduced GWP by roughly 20%, lowered key atmospheric pollutants, and substantially decreased nutrient leaching and heavy metal accumulation in soil. Overall, VRT proves to be a highly effective strategy for achieving sustainable wheat production through optimised input management, enhanced energy efficiency, and minimised ecological burdens.
Acknowledgement 
The authors gratefully acknowledge the support of the agricultural research team and field specialists involved in data collection, UAV operations, and soil and crop analyses throughout the study.

Abstract Rice husk is a widely available agricultural residue used for energy generation in many developing countries. Its gasification in micro-gasifier stoves offers a practical method for clean energy production. However, the unique shape and bulk density of rice husk cause fuel bridging and poor flow, leading to incomplete gasification unless sufficient aeration is provided. In this study, design modifications were applied to an existing micro-gasifier stove, and a forced-aeration system was integrated to ensure adequate oxygen supply during combustion. A full factorial experiment was conducted to evaluate stove performance, with injection air velocity and fuel mass selected as experimental factors. Using a 3.97-litre micro-gasifier stove, three air velocities (0.6, 0.7, and 0.8 m s^-1) and three fuel levels (200, 300, and 400 g) were tested during the cold-start phase following the water boiling test protocol. Results were statistically analysed using factorial analysis within a completely randomised design. The findings showed that both air velocity and fuel mass factors significantly affect stove performance metrics (p ≤ 0.05), including thermal power, thermal efficiency, fuel consumption rate, and boiling time. The highest thermal efficiency (51.7%) was achieved with 200 g of fuel at an air velocity of 0.6 m s^-1. The maximum thermal power (5420 W) occurred at an air velocity of 0.8 m s^-1.

Abstract Fossil fuel limitations and environmental concerns have increased interest in renewable energy sources like solar power for agricultural applications. This study presents the development and evaluation of a photovoltaic water pumping system equipped with a single-axis solar tracker, designed to automate irrigation in gardens and fields lacking access to the electrical grid. The system integrates solar energy absorption and storage, a solar tracker, and automatic irrigation units. Solar energy captured by the panel is transferred to a battery via a charge controller, then converted through a voltage converter and motor driver to operate the water pump. Experimental results indicate that the system’s energy intake peaks between 10 a.m. and 2 p.m. Under sunny conditions with an active tracker, the system stored 53.58 watts in the battery, compared to 43.4 watts with an inactive tracker, and 35.3 watts during cloudy weather. The study also found that the orientation of the solar panel relative to the sun and the use of the solar tracker significantly influenced energy collection, with statistical significance at the 5% level. Additionally, the type of soil moisture sensor impacted system performance, with a disturbance matrix demonstrating 100% irrigation accuracy. Overall, the solar tracker proved effective in sunny conditions, enhancing energy collection and system efficiency. The findings support the adoption of such systems for automatic irrigation, especially in remote or off-grid locations, contributing to sustainable agricultural practices and reducing reliance on fossil fuels. The integration of tracking technology and optimised sensor use can significantly improve the reliability and efficiency of solar-powered irrigation systems, making them a viable solution for modern agriculture.

Abstract The ecological footprint reflects human pressure on natural resources and environmental biocapacity and is widely used to assess agricultural sustainability. This study examined the effect of preceding crops (wheat, barley, and canola) on the environmental and economic performance of mung bean production. Data were collected via farmer questionnaires and interviews. Environmental indicators [ecological footprint (EF), biocapacity (BC), ecological balance (EB)] and economic indicators [gross margin, benefit–cost ratio (BCR)] were calculated. Results showed that the preceding crop significantly affected most indicators (p < 0.05); canola differed from wheat and barley, while wheat and barley did not differ significantly. Environmentally, mung bean after canola had the lowest total EF (1.055 gha ha⁻¹) and highest relative ecological efficiency (0.315), but the lowest BC (1.541 gha ha⁻¹). Mung bean after wheat exhibited the highest EF (1.777 gha ha⁻¹) and the highest BC (2.277 gha ha⁻¹). Although EB was positive for all rotations (0.482–0.500 gha ha⁻¹), the Ecological Footprint Index (EFI) fell within the 'weak sustainability' category (0.216–0.315), and the ratio of overexploitation footprint to input footprint (EF_ovp/EF_inp) ranged from 4.30 to 8.59, indicating that overexploitation pressure overwhelmingly dominates input‑driven pressure. Economically, wheat achieved the highest gross margin ($ 1,887 ha⁻¹) and BCR (2.844), while canola gave the lowest ($ 1,032 ha⁻¹; 1.479). A composite index with adjustable ecological‑economic weights revealed a trade‑off threshold at w≈0.55: canola is superior under ecological priority, wheat under economic priority. Given local biocapacity, selecting wheat as the preceding crop offers the most balanced combination of EF management and profitability for mung bean production in this region.

Abstract Introduction
The increasing demand for sustainable and renewable energy sources has intensified research on bioenergy production from agricultural wastes. Anaerobic digestion is a widely applied biological process for converting organic residues into valuable gaseous fuels such as biohydrogen and biomethane. However, the efficiency of this process is often limited by the complex structure of lignocellulosic biomass and the slow hydrolysis step, which acts as a rate‑limiting factor. Therefore, various pretreatment techniques have been proposed to enhance substrate biodegradability and improve gas yields. Among physical pretreatment methods, ultrasonic pretreatment has attracted significant attention due to its ability to disrupt cell walls, reduce particle size, and enhance solubilisation of organic matter through cavitation effects. Despite numerous studies on biogas enhancement using ultrasonic pretreatment, limited research has simultaneously investigated its effect on biohydrogen production, gas composition (H₂, CH₄, CO, and H₂S), and the associated metabolic pathways for different agricultural residues. Accordingly, the main objective of this study was to evaluate the impact of ultrasonic pretreatment on the anaerobic digestion performance of selected agricultural wastes, including corn residues, potato waste, and banana waste. In addition to gas production performance, particular emphasis was placed on analysing changes in metabolic pathways and alcohol formation to better understand the mechanisms governing hydrogen and methane generation.
Materials and Methods
In this study three types of agricultural wastes, namely corn residues, potato waste, and banana waste, were used as feedstocks. The substrates were mixed with animal manure and water to provide appropriate microbial inoculation and moisture content. The prepared mixtures were mechanically stirred at 500 rpm for 10 minutes to ensure homogeneity. Ultrasonic pretreatment was applied using an ultrasonic device operating at 300 W for 5 minutes. Both pretreated and untreated samples were then subjected to anaerobic digestion under controlled conditions. Total solids (TS) and volatile solids (VS) were measured to characterise the substrates before digestion. During the anaerobic digestion process, the composition of the produced gases, including hydrogen (H₂), methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S), was monitored. In addition, total alcohol concentration (ALC) was measured to assess shifts in fermentation pathways. The obtained data were analysed to compare the performance of ultrasonic pretreatment across different feedstocks and to evaluate its influence on metabolic reactions.
Results and Discussion
The results demonstrated that the effect of ultrasonic pretreatment on gas production strongly depended on the type of agricultural waste. For corn residues, ultrasonic pretreatment significantly enhanced biohydrogen production, increasing hydrogen concentration from approximately 2,585 ppm to 3,900 ppm. Methane production also showed a moderate increase, rising from about 104,000 ppm to 107,000 ppm. These improvements can be attributed to enhanced solubilisation of organic matter and improved accessibility of fermentable substrates. In contrast, potato waste exhibited decreased hydrogen and methane production following ultrasonic pretreatment. This behaviour suggests that excessive disruption of the substrate structure may have promoted alternative metabolic pathways unfavourable for gas generation. Banana waste showed a substantial percentage increase in hydrogen production after ultrasonic pretreatment, although its effect on methane production was less pronounced. Across all substrates, ultrasonic pretreatment led to an increase in carbon monoxide concentration and a noticeable reduction in hydrogen sulfide, which is considered beneficial due to the corrosive nature of H₂S. Metabolic pathway analysis revealed that ultrasonic pretreatment, particularly in banana and corn wastes, promoted pathways leading to alcohol production, such as ethanol and methanol formation. This shift explains the observed reduction or stagnation in hydrogen and methane production during later stages of digestion, as substrates were partially diverted toward solventogenic reactions.
Conclusion
The results of this study indicate that ultrasonic pretreatment can be an effective approach for improving the performance of anaerobic digestion; however, its effectiveness strongly depends on the type of substrate used. Among the investigated agricultural residues, corn waste demonstrated the most favourable response to ultrasonic pretreatment, showing improvements in both hydrogen and methane production. Banana peel also exhibited a noticeable increase in hydrogen generation after pretreatment, whereas potato waste showed a less favourable response and did not benefit significantly from the ultrasonic treatment. In addition, the variations observed in gas composition and alcohol production suggest that changes in microbial metabolic pathways play an important role in determining the outcomes of pretreatment processes. Overall, these findings suggest that ultrasonic pretreatment has considerable potential for enhancing bioenergy recovery from agricultural wastes, provided that pretreatment conditions are carefully optimised according to the characteristics of each substrate.

Abstract Olive harvesting is one of the most labour-intensive operations in olive production, particularly in high-density orchards, where technology selection strongly affects productivity and sustainability. This study combines quantitative field experiments with a sustainability-oriented multi-criteria decision-making framework to evaluate three harvesting methods including hand rake, branch shaker, and over-the-row combine harvester, for Arbequina and Koroneiki cultivars in Iran. A factorial RCBD was implemented to measure harvesting loss, harvesting efficiency, harvesting rate, oil content, and chemical quality indices. Results showed significant differences among methods in harvesting loss, efficiency, and rate (P < 0.01). Hand rake achieved the highest harvesting percentage (100%) but also the highest fruit loss (3.33%). Branch shaker recorded the lowest fruit loss (1.12%) but only 53.8% efficiency. Combine harvester provided the highest harvesting rate (4429–5158 kg h⁻¹) and the greatest economic return, with benefit–cost ratios of 1.297 for Arbequina and 1.375 for Koroneiki. To integrate technical, economic, social, and environmental indicators, fuzzy AHP was used to compute criterion weights, and Grey Relational Analysis (GRA) was applied to rank alternatives. Combine harvester obtained the highest GRA score (0.812), followed by the shaker (0.774) and the hand rake (0.682). A comprehensive sensitivity analysis was conducted using ten weighting scenarios and four multi-criteria decision-making methods (GRA, TOPSIS, VIKOR, and ELECTRE), which confirmed the robustness of the rankings across different policy priorities. Also, scenario-based sensitivity analysis demonstrated that combine harvesters consistently ranked first under economic and technical priorities, while shakers became the preferred option in sustainability-oriented scenarios emphasising employment and reduced non-renewable energy use. These results confirm that no single harvesting method dominates across all performance dimensions. Proposed integrated framework provides a robust, evidence-based decision support tool for selecting olive harvesting technologies that balance operational efficiency, profitability, and long-term sustainability.

Abstract Introduction
Only three percent of the Earth’s water is fresh and usable for drinking. Population growth and climate change have exacerbated water scarcity, implicating many countries, including Iran, in a crisis. Desalination, particularly using solar energy, is considered an effective solution to compensate for the shortage of fresh water resources. Iran, due to its high solar irradiation, has a high capacity for developing solar desalination systems. However, for various reasons, the efficiency of these systems is very low, and solutions are needed to improve their performance. Research has shown that Phase Change Materials (PCMs), by storing latent heat, improve the efficiency of solar collectors and the thermal stability of the system. Furthermore, the CFD (Computational Fluid Dynamics) method is a precise tool for analysing thermal behaviour and determining optimal conditions in desalination systems, and it can impact the improvement of system efficiency. The present study examined a novel combination of a helical tube containing PCM, a spray system, geothermal cooling, and a solar tracker, which leads to increased efficiency and thermal uniformity of the system.
Materials and Methods
Experimental tests were performed under various environmental conditions and at different saline water flow rates. Two system configurations were studied: one with PCM (paraffin mixture) surrounding the collector tubes and another without PCM. Data on temperature, thermal energy input and output, energy and exergy efficiencies, Performance Ratio (PR), and Recovery Ratio (RR) were recorded during the experiments. Additionally, CFD simulations were carried out using appropriate models for two-phase flow (water and air) and heat transfer to analyse the details of velocity, temperature, and phase fraction distribution within the tank and over the heat exchanger.
Results and Discussion
This research evaluates the performance of a novel solar desalination system equipped with a Phase Change Material (PCM) and conducts a Computational Fluid Dynamics (CFD) analysis. The primary objective is to investigate the impact of adding PCM on improving the thermal efficiency and the quality of desalinated water compared to a system without PCM.
Key Findings:
· Thermal Performance: The presence of PCM significantly increased the working fluid temperature in the tank and reduced heat loss. The maximum thermal energy input to the tank with PCM increased by 2.85 MJ, while the total collector output energy reached 38.42 MJ.
· Efficiency: At a flow rate of 4.2 L min^-1, the collector energy efficiency increased from 42.65 to 47.27 percent, while its exergy efficiency was 8.21percent; the desalination unit achieved an energy efficiency of 47.26 percent an exergy efficiency was 7.65 percent. With the addition of PCM, the Performance Ratio (PR) and Recovery Ratio (RR) also rose to 1.32 and 0.129, respectively.
· Velocity Distribution: Velocity contour plots indicated a maximum flow velocity of approximately 1.18 m s^-1 in the central nozzle region. In PCM-equipped systems, the saline water outlet velocity decreased due to more uniform energy distribution and higher evaporation rates.
· Temperature Distribution: The temperature pattern within the tank and on the heat exchanger was analysed. The average saline water temperature was around 54.26°C, with insulation playing a role in reducing temperature loss.
· Phase Fraction Distribution: The water volume fraction was highest at the tank inlet and lowest near the bottom. The inclusion of PCM enhanced the evaporation rate by approximately 6.63%, which reduced the vapour volume fraction from 0.184 (without PCM) to 0.171 (with PCM) at 10.524 seconds, indicating greater vapour generation. However, the average vapour volume fraction throughout the desalination process was 21.05% for the PCM system and 19.74% for the system without PCM.
· Water Production and Quality: The PCM-equipped system produced 987 mL of water, a significant increase compared to 842 mL from the system without PCM. Water quality analysis showed a substantial reduction in Total Dissolved Solids (TDS) with a decrease of at least a 300 mg L^-1, as well as a greater than 30% reduction in sodium and potassium levels relative to tap water, these results confirm the production of high-standard fresh water.
Conclusion
This study demonstrates that incorporating PCM into a solar desalination system significantly enhances its thermal performance and overall efficiency. The increased energy and exergy efficiencies, improved PR and RR, and a notable rise in desalinated water production are the main advantages of using PCM. CFD analysis provided deeper insights into the fluid dynamics and thermal distribution within the system, confirming the positive impact of PCM on evaporation processes. The quality of the produced water also meets the required standards. Total dissolved solids concentration was significantly reduced compared to municipal water (at least 300 mg L^-1). Overall, this system offers a robust and economical solution for water desalination, particularly in regions with limited access to potable water resources.

Abstract Bean planting systems are essential to global agriculture, serving as a vital food source for many populations. Optimizing these planting methods is crucial for enhancing efficiency and reducing environmental impacts. This study evaluates the energy inputs and outputs associated with two pinto bean cultivation techniques: flat and strip systems. Conducted in Fars province, southern Iran, the research involved 90 farms, 60 employing flat systems and 30 utilizing strip systems. Energy consumption was assessed in MJ ha^-1 for various inputs, including labor, machinery, diesel, chemical fertilizers, biocides, electricity, and seeds. The flat system exhibited energy consumption of 20,067.12 MJ ha^-1, while the strip system utilized 18,171.76 MJ ha^-1. In terms of yield, the flat system produced 3000 kg ha^-1, in comparison to 3500 kg ha^-1 from the strip system. Energy efficiency metrics indicated that the strip system outperformed the flat system with a higher energy use efficiency ratio (3.85 against 2.99) and better energy productivity (0.19 kg MJ^-1 vs. 0.15 kg MJ^-1). Additionally, the strip system demonstrated lower specific energy consumption at 5.19 MJ kg^-1, compared to 6.69 MJ kg^-1 for the flat system. The net energy gain was also greater for the strip system, recording 51,828.24 MJ ha^-1 versus 39,932.88 MJ ha^-1 for the flat system. Overall, the results highlight the favorable energy requirements and efficiency of the strip planting method over the traditional flat system, underscoring its potential for optimized resource allocation in pinto bean cultivation. The MOGA results indicated that strip systems achieve substantial energy savings of 3749.11 MJ ha^-1 (25.99%), compared to flat systems, which save 3707.62 MJ ha^-1 (22.66%). This further highlights the efficiency benefits of strip planting.

Abstract Grain harvesting operations account for approximately 25-30% of total direct energy consumption in crop production systems. Developing appropriate blades for harvesting canola (Brassica napus L.) is crucial due to its distinct characteristics compared to other cereal grains. This study investigated the effects of blade angles (placement angles: 30°, 45°, and 60°; sharpness angles: 30°, 45°, and 60°), reciprocating movement speed (800, 1100, and 1400 courses per minute), and moisture levels (19%, 22%, and 24%) on reducing force, shear stress, and energy consumption during canola harvesting. Results showed that a blade sharpness angle of 30° yielded the lowest shear stress (0.175 N mm^-2) compared to 60° (0.303 N mm^-2). The 45° blade placement angle demonstrated minimum shear stress (0.177 N mm^-2) versus 60° (0.320 N mm^-2). Increasing moisture content from 19% to 24% reduced shear stress from 0.256 N mm^-2 to 0.200 N mm^-2. The highest reciprocating speed (1400 courses per minute) resulted in the lowest shear stress (0.167 N mm^-2) compared to 800 courses per minute (0.286 N mm^-2). Life cycle assessment revealed that varying blade placement angles (30° to 60°) could increase marine aquatic ecotoxicity by up to 55,762.55 kg dichlorobenzene equivalent, while changes in blade sharpness angles and reciprocating speed could lead to increases of 377,429.87 kg and 143,185.69 kg dichlorobenzene equivalent, respectively. The optimal configuration—comprising a sharpness angle of 30°, a placement angle of 45°, a moisture content of 24%, and a reciprocating speed of 1400 courses per minute—significantly reduced both shear energy and environmental impact.

Abstract The vibrations generated by the use of different fuel mixtures in tractor engines can lead to accelerated wear of engine components, significant increases in maintenance costs, and reduced comfort and safety for operators. Nowadays, renewable fuels, namely biodiesel and bioethanol, have been of great interest to many researchers. In the present study, vibrations of the engine of MF285 tractor were measured in three directions, at speeds of 1000, 1600, and 2000 rpm for ten different fuel levels obtained from different compositions of biodiesel, bioethanol, and diesel fuels. To analyze the effects of the concerned parameters on engine vibrations, the response surface methodology (RSM) and artificial neural network fuzzy inference system (ANFIS) were applied. The obtained results demonstrated that increasing the engine speeds was in direct proportion to the vibrations increase. Furthermore, pure diesel fuel accounted for the major portion of vibrations, and B₅E₄D₉₁ had the highest vibrations among the fuel compositions. Moreover, vibrations were meaningfully reduced with the increase of biodiesel in fuel compositions. The optimization analysis revealed that the most effective fuels, exhibiting the lowest vibration levels, were identified as B₂₅E₆D₆₉ through RSM and B₂₅E₄D₇₁ via ANFIS.

Abstract In this study, the drying process of apples was explored using a new combined solar dryer known as the Refractance Window-Parabolic Trough Collector (RW-PTC). The drying kinetics, energy efficiency in the solar collector and dryer, and the role of the dryer in reducing energy consumption and pollutant emissions during the drying process were investigated. Drying experiments were carried out with three energy sources, including conventional non-renewable energy (RW), solar-assisted drying (PRW), and fully solar drying (SRW). In the first and second methods (RW and PRW), drying was performed at three temperature levels (65, 75, and 85 °C), and in the third method (SRW), drying was performed at the temperature of the solar collector. The average optical and thermal efficiency of the PTC collector during the experimental hours were 62.01% and 49.31%, respectively. The lowest specific energy consumption was observed in the SRW method at 10.24 (kWh kg^-1). The results showed that the solar energy used in the combined drying methods of PRW-65, PRW-75, PRW-85, and SRW accounted for 54.91%, 52.62%, 48.85%, and 70.30% of the total energy consumption, respectively, and by the same amount, energy consumption from non-renewable sources was reduced. By using a solar collector in the PRW and SRW drying methods, the CO₂ emission was reduced by 54.64% and 80.94%, respectively, compared to the conventional RW method. Overall, the implementation of solar energy in the PRW and SRW methods improved energy parameters and reduced pollutant emissions during the drying process.

Abstract Introduction
One of the key structural factors in agricultural mechanization is the selection of appropriate technology. Today, examining the effects of technology application and development on agricultural production remains of highly importance. Innovative technologies, such as spraying drones, play a critical role in advancing agriculture and ensuring food security. Without these technologies and proper input management, environmental impacts are likely to intensify. Achieving sustainable production and ensuring food security is a major challenge for researchers and global policymakers. This study evaluates and compares the performance of spraying drones and boom sprayers in controlling weeds and yellow rust disease in wheat fields. The aim of this study is to optimize pesticide use and achieve sustainable agriculture.
Materials and Methods
This research was conducted to evaluate the field performance and economic feasibility of using spraying drones compared to boom sprayers for controlling weeds and yellow rust disease in wheat fields. Experiments were carried out in regional Khorramabad, Iran, using a DJI Agras MG-1P spraying drone and a 400-liter 400B8 TF boom sprayer. The aim was to investigate the impact of modern technology, specifically spraying drones, compared to traditional methods, such as boom sprayers, for managing weeds and yellow rust disease. Additionally, the study assessed the profitability of these technologies. The experiments followed a randomized complete block design with three treatments: boom sprayer, spraying drone, and control. They were conducted in two separate, independent fields to examine treatment effects on weeds and yellow rust in wheat. For weeds control, 2-4-D herbicide was applied at 1.5 L ha^-1, and for yellow rust control, Tilt fungicide was used at 0.5 L ha^-1.
Results and Discussion
Results showed that the deposition rate of pesticides in boom sprayers (82.8%) was higher than with drone spraying (69.9%). Furthermore, the average dry weight of weeds in boom sprayer was 172 g m^-2, and in drone spraying, it was 163 g m^-2, which was not statistically significant. Additionally, the average weed density was 25 plants per square meter for boom sprayers and 29.3 plants per square meter for drone spraying, with no statistically significant difference. The average harvest index in weed control experiments was 44% for boom sprayer and 41% for drone spraying, which was statistically significant at the 1% level. The average severity of yellow rust infection in wheat fields was 30.7% for boom sprayer and 25.3% for drone spraying, which was not statistically significant at the 1% level, but both treatments were significantly different from the control (68.3%). The harvest index in yellow rust experiments was better in drone spraying (43.8%) compared to boom sprayer (41.9%). The total annual cost for drone owners in the studied region (2980.3 million rials) was higher than the total cost for boom sprayer owners (513.48 million rials). However, the benefit-cost ratio for drone owners (1.215) exceeded that of boom sprayer owners (1.030), demonstrating economic viability for both sprayers. Overall, drones are found to be more economical for spraying than boom sprayers due to their higher efficiency and profitability. The use of drones can significantly increase the efficiency and profitability of spraying operations.
Conclusion
The results of this study showed that both drone and boom sprayer were effective in reducing the dry weight of weeds, but there was no statistically significant difference between them. Weed density was higher with drone spraying, and the harvest index was better with drone spraying compared to boom sprayer. The costs of using drones were higher than boom sprayers, but despite the higher costs, drones are superior option for spraying due to their increased efficiency and profitability.

Abstract Introduction
The increasing global population has intensified the demand for sustainable energy solutions. Meeting this need requires leveraging renewable energy sources that also address pollution management and reduce greenhouse gas emissions. Plant microbial fuel cells (PMFCs) have gained attention as innovative systems that produce electricity by decomposing organic matter in their anodic chambers, providing a dual benefit of clean energy generation and environmental remediation. These systems align closely with global sustainable development goals and represent a novel approach to energy production from organic materials.
Materials and Methods
This research focused on a plant microbial fuel cell system designed to contribute to sustainable development objectives. The system employed Cyperus plant and Shewanella oneidensis microorganisms to decompose organic substrates, including carbohydrates secreted by plant roots or other external sources, within the anodic chamber. Voltage output was measured using a voltage sensor connected to an Arduino UNO board, with data collected at two-hour intervals. The experiment investigated the effects of two parameters: oxygenation rate in the cathodic chamber and sodium acetate concentration in the anodic chamber, on the system performance.
Results and Discussion
The results revealed significant effects of both oxygenation and sodium acetate concentrations on the voltage output of the PMFC system. Increasing the oxygenation rate from 0 to 1 liter per minute enhanced the voltage output from 103 mV to 185 mV. Similarly, increasing sodium acetate concentration from 0 to 10 g L^-1 raised the voltage from 103 mV to 170 mV. Furthermore, pollution removal efficiency was evaluated using chemical and biological oxygen demand (COD and BOD) measurements. At the highest levels of sodium acetate concentration (20 g L^-1) and oxygenation rate (3 L min^-1), the pollution removal rate reached 90%. These findings underscore the capability of PMFCs to combine energy production with effective environmental cleanup.
Conclusion
The microbial-plant fuel cell system demonstrates considerable potential as a dual-purpose solution for renewable energy generation and pollution removal. Its high efficiency in utilizing microorganisms and plants for these tasks suggests that it could play a critical role in sustainable development. Future research should focus on addressing the system’s limitations and enhancing its scalability and reliability to support broader applications in renewable energy and environmental remediation.

Abstract Rice is a major staple food consumed worldwide, but its processing has significant environmental impacts due to water and energy consumption and greenhouse gas emissions. As a result, rice producers are adopting sustainable processing techniques to reduce negative environmental impacts and increase profitability. This study analyzed the sustainability of modern and traditional paddy rice processing techniques among smallholder rice farmers in Southeast Nigeria. The data was collected from 240 rice producers using statistical approaches such as descriptive statistics, sustainability indicator (Weight Assessment Ratio Analysis), and multinomial regression analysis. The results showed that 34.7% of rice farmers used modern processing techniques while 65.3% used traditional methods. Traditional milling produced substantial carbon emissions, according to 77% of small-scale farmers, while 68% rated noise pollution as high. 80-100% of small-scale farmers using modern techniques cared about the environment and wanted to reduce their gas emissions, solid waste, energy use, and water use. The sustainability index for farmers using traditional and modern processing techniques was affected by gender experience, labor size, investment, income, cost of production, understanding of climate change, and environmental sustainability. The study recommends using renewable energy sources to increase productivity and reduce environmental effects.

Abstract Introduction
Efficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. Therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using Artificial Bee Colony (ABC) and Genetic Algorithms (GA).
Materials and Methods
Extensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in Rezvanshahr city, located in the province of Guilan, Iran, during the 2019-2020 production year. The farms consisted of 80 high-grading and 40 high-yielding paddies. The independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. The dependent variable was paddy yield per hectare [of the farm area]. In the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. In the second step, all the variables that maximize paddy yield were entered into MATLAB software. An artificial bee colony (ABC) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (GA). The Sphere, Repmat, and Unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. In each generation, 900 new solutions were created, and the algorithm iterated 200 times. For the genetic algorithm, the population was defined as a double vector with a size of 100.
Results and Discussion
The findings revealed that the Hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 GJ·ha^-1, respectively. The Jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 GJ·ha^-1 and double the energy production of the Hashemi at 62.522 GJ·ha^-1. In both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. The average energy consumption of tractors in the Hashemi and Jamshidi cultivars was 25.111 and 25.865 GJ·ha^-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. This undoubtedly demonstrates the significant effect of this input and reflects the operators' skill and experiential knowledge. The evaluation indexes, including R², RMSE, MAPE, and EF, as well as statistical comparisons such as mean, STD, and distribution, consistently demonstrated that the ABC algorithm provided the essential conditions for the fitness function. The results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. Through this optimization, energy consumption in the Hashemi and Jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.
Conclusion
Given its impressive performance and potential for minimizing energy consumption, the ABC-GA algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. This approach could lead to more sustainable and efficient practices in the agricultural sector.

Abstract Introduction
In Asia, two-wheeled agricultural tractors predominantly use single-cylinder two-stroke diesel engines, which are characterized by high fuel consumption and substantial air pollution. At the same time, the severe environmental impacts of energy production from diminishing fossil fuel reserves are increasingly evident. Therefore, it is essential to develop sustainable and clean energy sources to meet these needs. Biodiesel is an alternative fuel that can be blended with conventional diesel to help reduce environmental pollution. In this study, diesel-biodiesel blends produced from rapeseed, soybean, and palm oil were evaluated for their effects on engine performance metrics, including power (P), torque (T), and specific fuel consumption (SFC). Furthermore, the emissions of pollutants (NOx, HC, CO, and CO₂) from these fuels were measured and modeled using linear and non-linear regression, as well as the adaptive neuro-fuzzy inference system (ANFIS).
Materials and Methods
To leverage the benefits of palm oil biodiesel, known for its high calorific value, along with the low kinematic viscosity of biodiesel derived from soybean and rapeseed oils, pure diesel was blended with 10% and 20% mixtures of rapeseed, soybean, and palm biodiesel, as well as 10% and 20% combinations of all three biodiesels. These nine fuel blends were tested at four engine speeds (1800, 2100, 2400, and 2700 rpm) under full load conditions. The diesel-biodiesel blends were produced at Sari Agricultural Sciences and Natural Resources University (SANRU) and transported to the engine laboratory at Tarbiat Modares University in Tehran, Iran, for detailed analysis. A total of 36 treatments were evaluated using a randomized complete block design (RCBD), incorporating four engine speeds and nine fuel types. The measured outputs included engine power, torque, specific fuel consumption, and pollutant emissions such as NOx, HC, CO, and CO₂. The collected data were used as input for modeling through both linear and non-linear regression in SPSS software, as well as ANFIS in MATLAB software.
Results and Discussion
This study evaluated nine diesel-biodiesel blends derived from palm, rapeseed, and soybean oils using a diesel engine in a controlled laboratory setting. Tests were carried out at four engine speeds—1800, 2100, 2400, and 2700 rpm—under full load conditions to assess engine performance and exhaust emissions. The results showed that for all tested fuel blends, power, specific fuel consumption, and pollutant emissions increased with engine speed, while torque decreased. Based on the findings, a blend containing 20% palm biodiesel can be used as an alternative fuel in diesel engines without requiring any modifications. The modeling results indicated that non-linear regression provided better accuracy than linear regression. However, ANFIS demonstrated a much higher correlation between actual and predicted values, with R² exceeding 0.98 for both performance parameters and emissions, compared to R² values below 0.47 for linear regression and below 0.92 for non-linear regression. The ANFIS model achieved its highest and lowest R² values at 0.99 for specific fuel consumption (SFC) and 0.98 for power (P), respectively; substantially higher than those from linear regression, which yielded 0.47 for torque (T) and 0.00 for power. Non-linear regression resulted in R² values of 0.92 for SFC and 0.60 for carbon monoxide (CO), still lower than those achieved by ANFIS. Overall, the highest R² value recorded was 0.7525 for torque, and the lowest was 0.6112 for power.
Conclusion
Single-cylinder diesel engines, which have high fuel consumption and contribute to air pollution, are commonly used in two-wheel agricultural tractors across Asia. One approach to reducing the environmental impact of fossil fuels is to use biodiesel in these engines without requiring any modifications. The results of this study showed that a 20% blend of palm biodiesel can be an effective alternative fuel for diesel engines, with no need for engine modification. Furthermore, the modeling results indicated a significantly higher correlation (R² > 0.98) between actual and predicted values of performance variables and emissions using ANFIS, compared to linear regression (R² < 0.47) and non-linear regression (R² < 0.92). Therefore, ANFIS can be effectively used to accurately predict engine performance and emission parameters.

Abstract Introduction
Energy analysis offers significant benefits by establishing a foundation for resource conservation, quantifying the energy consumed at each stage of production, identifying processes that require minimal energy input, and supporting sustainable management practices. In sustainable agricultural systems, maximizing the productivity of input energies is a key objective. This study aims to assess energy consumption patterns within the sugar industry and to compare the optimization of energy consumption indicators using two meta-heuristic algorithms, ultimately seeking to enhance resource efficiency and promote sustainable production methods.
Materials and Methods
This study evaluated energy efficiency and environmental impacts in sugarcane-based sugar production at Dehkhoda Sugarcane Agro-Industry Company (in Khuzestan Province, Iran), during the 2019-2020 agricultural cycle. Data collection integrated field questionnaires, expert interviews, operational records from the facility, and national agricultural databases (Ministry of Agriculture Jihad statistics and energy balance sheet). Energy flow were analyzed using MATLAB statistical software and the Equinonet database, with comparative optimization through genetic algorithms and imperialist competitive algorithms to identify efficiency improvements.
Results and Discussion
The results showed that, for the majority of indicators evaluated, the imperialist competitive algorithm outperformed the genetic algorithm in optimizing energy consumption. In addition to reducing the environmental impacts of this profitable industry in the country, it has a high potential for energy savings. The total energy input reduction with the genetic algorithm was 17.05%, while the imperialist competitive algorithm achieved a higher reduction of 26.40%. Natural gas consumption decreased by 3.82% using the genetic algorithm, and by 27.60% with the imperialist competitive algorithm. Direct energy savings were 16.97% for the genetic algorithm and 27.48% for the imperialist competitive algorithm. Soil acidification reduction was 23.03% with the imperialist competitive algorithm and 19.19% with the genetic algorithm, compared to conditions before optimization.
Conclusion
In general, it can be concluded that, given the growing demand for sugar production and related industries, as well as the high efficiency of the sugar production sector, it is advisable to utilize expert knowledge and apply meta-heuristics methods to optimize energy consumption and available inputs with the aim of reducing harmful environmental impacts.

Abstract Optimal use of resources, including energy, is one of the most important principles in modern and sustainable agricultural systems. Exergy analysis and life cycle assessment were used to study the efficient use of inputs, energy consumption reduction, and various environmental effects in the corn production system in Lorestan province, Iran. The required data were collected from farmers in Lorestan province using random sampling. The Cobb-Douglas equation and data envelopment analysis were utilized for modeling and optimizing cumulative energy and exergy consumption (CEnC and CExC) and devising strategies to mitigate the environmental impacts of corn production. The Cobb-Douglas equation results revealed that electricity, diesel fuel, and N-fertilizer were the major contributors to CExC in the corn production system. According to the Data Envelopment Analysis (DEA) results, the average efficiency of all farms in terms of CExC was 94.7% in the CCR model and 97.8% in the BCC model. Furthermore, the results indicated that there was excessive consumption of inputs, particularly potassium and phosphate fertilizers. By adopting more suitable methods based on DEA of efficient farmers, it was possible to save 6.47, 10.42, 7.40, 13.32, 31.29, 3.25, and 6.78% in the exergy consumption of diesel fuel, electricity, machinery, chemical fertilizers, biocides, seeds, and irrigation, respectively.

Abstract Introduction
Pomegranate has gained global popularity due to its high vitamin content and antioxidant properties, attracting fans worldwide. The processing of pomegranate into various products, including pomegranate juice, has become a thriving industry. However, this processing requires significant energy and chemicals—most of which are derived from fossil fuels. The combustion of these fuels releases harmful gases, contributing to global warming, environmental damage, and health risks. The costs tied to these environmental burdens are often overlooked, neglecting the principles of environmental sustainability. Therefore, it is vital to assess the monetary value of the environmental impacts throughout the entire life cycle of pomegranate juice production. This research aims to investigate the costs imposed on society, including the social costs of carbon emissions, damage costs from air pollution, and costs associated with environmental prevention measures related to processing pomegranate juice. Feel free to ask for further changes or adjustments.
Materials and Methods
This study focuses on assessing the environmental impact and associated costs generated during the processing of pomegranate juice in Mashhad, Iran, from 2022 to 2023. The research examines the case study of Saman Bazar Razavi Co. to conduct an environmental impact cost assessment. The study begins by evaluating the environmental impacts associated with the pomegranate juice production process using a life cycle assessment (LCA) approach. The costs related to these impacts are then estimated by multiplying the impact amounts with predetermined monetary coefficients. The study adopts a system boundary that extends from the arrival of the fruit at the factory to the departure of the packaged juice, defining a 160g pack of pomegranate juice as the functional unit (FU). SimaPro software, version 9, is utilized for analyzing the environmental impacts. The evaluation of environmental impact costs encompasses three categories: social costs of carbon emissions, damage costs from air pollution, and costs for environmental prevention measures. Carbon dioxide emissions are considered to assess social costs, while five other gases—nitrogen oxides, particulate matter, sulfur dioxide, volatile organic compounds, and ammonia vapor—are included in investigating air pollution damage costs. Furthermore, the calculation of environmental prevention costs takes into account seven impact categories: global warming, photochemical oxidation, respiratory inorganic effects, human toxicity, ecotoxicity, eutrophication, and acidification.
Results and Discussion
Here’s the edited text with corrections marked: The investigation reveals that the production of pomegranate juice emits approximately 0.12 kg CO₂ eq of carbon, with a social cost of $0.0062 per functional unit. The primary contributors to carbon emissions are natural gas and electricity. Furthermore, the evaluation of air-polluting gases indicates a total cost of $0.021 for air pollution damage. Among the five considered gases, ammonia vapor, sulfur dioxide, and nitrogen oxides incur the highest damage costs. The assessment of environmental prevention costs demonstrates a total calculated cost of $0.026, with the impact categories of global warming and acidification making the most substantial contributions of 59% and 28%, respectively. This finding suggests that the majority of costs for preventing damage in pomegranate juice production should be focused on mitigating the effects of global warming. The consumption of natural gas and electricity during the pomegranate juice production process is the main source of carbon dioxide emissions and global warming. Additionally, in terms of acidification, the contributions of pomegranate, electricity, apple, natural gas, and sugar are noteworthy. Based on these findings, it is evident that the resources used in pomegranate juice processing, derived from fossil fuels, have the most significant impact on environmental damage. Therefore, one practical method to prevent the creation of these pollutants is the utilization of alternative bioproducts produced from biomass. Considering the substantial amount of pomegranate waste generated after juice processing,which is often not utilized; these wastes can be effectively employed to produce bioenergy, such as biogas. This approach not only prevents waste disposal but also offers economic and environmental benefits.
Conclusion
This article provides an overview of the environmental impacts and associated costs of pomegranate juice production in Mashhad. Using the life cycle assessment approach, the study calculates the environmental impacts per functional unit (a 160g juice pack) and estimates the corresponding costs. The results indicate that the social cost of carbon emissions, the total damage costs of air pollution, and the total environmental prevention costs per functional unit are $0.0062, $0.021, and $0.026, respectively. These costs should be allocated to mitigating the environmental damage caused by pomegranate juice production in the region.
Acknowledgments
The authors express their gratitude to Ferdowsi University of Mashhad for funding this research (Grant No. 54189).

Abstract Introduction
There are two types of hydropower harvesting methods: conventional and unconventional. In the conventional method, the potential energy of water is harvested using a dam or barrage. However, in the unconventional method, the kinetic energy of flowing water is extracted using hydrokinetic turbines. Resource assessment is a pivotal step in developing hydrokinetic energy sites. Power density (power per unit area) is used to estimate the theoretical hydrokinetic power of a site. Flow velocity and cross-sectional area are the two variables that constitute the power density. Researchers use various methods such as numerical simulation, direct velocity measurement, or indirect velocity calculation using discharge data to conduct resource assessment. In the latter method, the Manning equation is used to convert the discharge data into velocity values. While this method is straightforward for canals, given their fixed and known geometry, it is cumbersome to calculate the hydraulic radius in rivers. To overcome this challenge, numerous researchers have proposed the utilization of hydraulic geometry (HG) to estimate the width and depth of a river reach, and then calculate the hydraulic radius based on these estimated values. The main objective of this study is to present and implement a fast method for assessing theoretical hydrokinetic power using the HG and the Manning equation.
Materials and Methods
In the present study, two hydrometry stations (Gachsar and Siera-Karaj) were selected in the Karaj dam watershed in Iran to implement resource assessment based on HG. A computer code comprising the following four steps was developed in Python using the Google COLAB environment.
Data Preparation: The monthly-averaged discharge, Manning roughness coefficient, and slope were collected and imported into the code. The roughness coefficient could be determined directly or indirectly. In the present study, it was considered to be 0.045 for the Karaj River according to the literature review. ArcGIS software and the Digital Elevation Model (DEM) were used to extract the local slope of each hydrometry station. For this purpose, the stream network of Alborz province was first extracted, and then the longitudinal elevation profile was measured using the 3D Analyst tools.
Discharge Data Processing: The flow duration curve (FDC) is one of the computational tools used by engineers to describe the hydrological regime of watersheds. FDC is a graphical representation of the cumulative distribution of flows. In the present study, an all-time record FDC for each station was constructed, and fitted with five different probability distribution functions (PDF). The results of PDF fittings were evaluated by different goodness-of-fit indices, and the best PDF was selected.
Calculations of HG and the Manning Equation: The HG formulas were used to calculate the width and depth of flow using the reconstructed FDC from the previous step. These values, along with the roughness coefficient and slope, were used to calculate flow velocity using the Manning equation. After obtaining the flow velocity values, the power density was easily computed.
Generating Outputs: In the final step, two categories of outputs are generated: (1) duration curves for width, depth, flow velocity, and power density, and (2) theoretical and turbine-extracted energy diagrams.
Results and Discussion
The goodness-of-fit indices for PDF fitting indicated that the log-normal PDF is the most suitable distribution to describe the FDC with a coefficient of determination of 0.99. The calculated average discharge (Q₅₀) for the Gachsar and Siera stations was 2.34 and 7.68 m³s^-1, respectively. These values are consistent with findings from previous studies. The results of the Manning equation calculations revealed that the flow velocity does not differ significantly between these stations (8% higher at Siera). The base flow depth at the Gachsar and Siera stations is less than 1 m. Therefore, as indicated in the literature review, axial flow (propeller) turbines are not suitable for installation in these rivers because they need to be fully submerged and require at least 1 m of depth. Overall, the use of wide and short turbines, such as Savonius turbines, is suggested in the Karaj River. The energy analysis results show that the maximum monthly theoretical energy at Gachsar and Siera equals 38,500 and 125,500 kWh, respectively. However, considering a turbine with a 1 m² swept area and a power coefficient of 0.2, the maximum monthly extracted energy is limited to 940 and 1,142 kWh at these two stations.
Conclusion
This study presents a fast method for the theoretical assessment of hydrokinetic power, which was applied to two hydrometry stations in the Karaj dam watershed. The results of HG calculations revealed that the base velocity (V₉₀) of 1.34 and 1.49 m/s is present at the Gachsar and Siera stations, respectively. According to the available depths at these stations, the use of wide and short turbines such as Savonius turbines is suggested. Each individual Savonius turbine with a unit swept area at Gachsar and Siera is estimated to extract a maximum monthly energy of 940 and 1,142 kWh, respectively.

Abstract Introduction
On the field and in the paddy milling factory dryer losses have always been challenging issues in the rice industry. Different forms of losses in brown rice may occur depending on the field and factory conditions. To reduce the losses, proper management during pre-harvest, harvesting, and post-harvest operations is essential. In this study, different on-field drying and tempering methods were investigated to detect different forms of brown rice losses.
Materials and Methods
The present study was conducted on the most common Hashemi paddy variety during the 2019-2020 season in Talesh, Rezvanshahr, and Masal cities in the Guilan province, Iran with 0.2 hectares and 5 paddy milling factory dryers. On the fields, the method and date of tillage, irrigation, and transplanting used in all experimental units were the same. Moreover, the same amount of fertilizer and similar spraying methods were used across all experiments. For the pre-drying process on the fields, the following three pre-drying methods were applied on the harvest day: A1) The paddies were spread on the cut stems for insolating, A2) The paddies were stacked and stored after being placed on the cut stems for 5h, and A3) The paddies were covered with plastic wrap and stored after 5h of insolating. The first method (A1) is the most common in the area and was chosen as the control treatment. For the second step of the process, the time interval between the on-field pre-drying and threshing was considered: B1) 14 to 19h post-harvest; B2) 20 to 24h post-harvest, and B3) 25 to 29h post-harvest. Afterward, methods A1 to A3 were combined with methods B1 to B3 and feed into an axial flow-thresher at 10 kg min^-1, 550 rpm PTO, and two levels of moisture content at 19 and 26 percent (% w.b). The third process was two-stage or three-stage tempering for 10 or 15 hours resulting in four levels (C1 to C4) and was done in the conventional batch type dryer under temperatures of 40 and 50 ˚C and airspeeds of 0.5 and 0.8 m s^-1 in paddy milling factories. At the end of each process, a 100g sample was oven-dried for 48h and a microscope achromatic objective 40x was used to detect incomplete horizontal or vertical cracks, tortoise pattern cracks, and immature and chalky grains. The equilibrium moisture content was determined to be 7.3 percent. Losses properties were analyzed using a completely randomized factorial design with a randomized block followed by Tukey's HSD test at the 5% probability and comparisons among the three replications were made.
Results and Discussion
Results demonstrated that the stack and plastic drying methods significantly increased the percentage of losses. In the plastic drying method, the percentage of chalky grains and tortoise pattern cracks was higher than other forms of loss. In the first process, irrespective of the pre-drying method, the losses were reduced at a lower level of moisture content. At the end of the first stage, losses in the spreading method were significantly lower at 19% moisture content. Threshing the plastic-wrapped paddies after 14 to 19 hours at 19% moisture content resulted in the maximum threshing loss of 8.446% and over half of the grains were chalky or had tortoise pattern cracks. The threshing loss was halved (4.443%) for paddies threshed 25 to 29h after spreading at a moisture content of 26%. The mean of losses in the second step of the process were 7.229, 5.585, and 5.156% for the time interval between the on-field pre-drying and threshing of 14 to 19h, 20 to 24h, and 25 to 29h, respectively. In the last step of the process in paddy milling factory dryers, there was no significant difference in the minimum percent of losses between 10 and 15 hours of three-stage tempering at 40 °C and with 0.5 m s^-1 airspeed. Furthermore, maximum total losses with the most incomplete horizontal and vertical cracks occurred in the two-stage 10h tempering at 50 °C and with 0.5 and 0.8 m s^-1 airspeed.
Conclusion
Food security has always been a critical matter in developing countries. Furthermore, identifying the source of losses in the fields and the factories is one way to reduce losses and achieve food security. Stacking or wrapping the paddies in plastic after pre-drying on the fields for 5h is not recommended in terms of its effect on increasing the percentage of brown rice losses. Additionally, due to the importance of factory dryer scheduling in the management of the losses, it is recommended to use a three-stage 10h tempering at 40 °C and with 0.5 m s^-1 airspeed. 

Abstract Introduction
With increasing the world's population, the demand for supply water resources is also increasing. Nevertheless, climate change has severely impacted the accessibility of fresh water resources. Consequently, researchers have been focusing on producing drinkable water from seas and oceans. Iran, with its significant levels of solar radiation and access to open water from the north and south, is an ideal country for fresh water production. Using solar water desalination systems is a reliable and cost-effective solution for producing drinking water from salt water sources. The purpose of this research is to enhance the performance of the solar water desalination system by using the latent heat storage system and a solar tracking system. In this experimental setup for fresh water production, water was used as the working fluid, while a parabolic collector functioned as the source of thermal energy.
Materials and Methods
The solar water desalination system was designed and built on a laboratory scale at the University of Kurdistan, and then the necessary experiments were carried out. The flowing fluid (water) inside the spiral tube in the tank is pumped into the absorber tube of the parabolic collector. Inside the receiver tube, there is a spiral copper tube with a 7 cm pitch, which contains paraffin. The parabolic mirror reflects the sunlight onto the receiver tube, causing the working fluid, water, to heat up. The cooling process is achieved using a specific source located in the upper section of the distillation tank. In this case, the steam droplets in the tank hit the bottom surface of this cooling tank, which has the shape of an inverted funnel, leading to condensation. The study was conducted over four consecutive days, from 10:00 to 14:00, under identical conditions from August 24th to August 27th, 2022. It took place at the Renewable Energy Laboratory, University of Kurdistan in Sanandaj, Iran, and was conducted for three different volume flow rates of fluid: 1.9, 3.1, and 4.2 l.min^-1 with phase change materials (PCM) and 4.2 l.min^-1 without phase change materials (WOPCM); the pump’s maximum flow rate was 4.2 l.min^-1. Variations of outlet temperature, thermal efficiency, desalination efficiency, and produced water were investigated under different conditions.
Results and Discussion
The results reveal that by decreasing the pitch of the spiral tube, there is an increase in the amount of heat captured, due to the increase in the Nusselt number. At the beginning of data collection, a significant amount of the energy that enters the receiver tube is absorbed by both the phase change material and the spiral tube inside the receiver and as a result, the initial air temperature is lowered. The highest temperature of salt water occurs when the fluid is flowing at a rate of 4.2 l.min^-1, while the lowest temperature is observed at a flow rate of 1.9 l.min^-1. With a flow rate of 4.2 l.min^-1, the absorbent tube rapidly transfers the absorbed heat to the salt water chamber through the fluid. The input energy to the tank has increased from 1.53 to 2.83, 1.14 to 2.18, and 0.73 to 1.48 MJ for fluid flow rates of 4.2, 3.1, and 1.9 l.min^-1, respectively. At a flow rate of 4.2 l.min^-1, the thermal efficiency of the system without phase change materials (3.51%) is lower compared to the case with phase change materials (5.02%). Moreover, using a solar tracking mechanism increased the thermal efficiency of the collector by 9.86% compared to the system using a photocell sensor. Based on the water quality values, it can be stated that the level of dissolved solids in the water sample has been significantly decreased. This indicates that the water can be used for drinking.
Conclusion
In this research, the process of thermal changes in a solar water desalination system using PCM was investigated. The obtained results demonstrate that the use of PCM improved the thermal efficiency of the collector and the water obtained from the current system is safe for consumption. Furthermore, by implementing a solar panel tracking system, the efficiency of the solar collector is improved.

Abstract In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations of Fe and Fe₃O₄ (10, 20, and 30 mg L^-1) and DWTS (6, 12, and 18 mg L^-1). Leveraging an extensive library of non-linear regression (NLR) models, 26 candidates were scrutinized and eight emerged as robust predictors for the entire methane production process. The Michaelis-Menten model stood out as the superior choice, unraveling the kinetics of dairy manure AD with the specified additives. Fascinatingly, the findings revealed that different levels of DWTS showcased the highest methane production, while Fe₃O₄20 and Fe₃O₄30 recorded the lowest levels. Notably, DWTS6 demonstrated approximately 34% and 42% higher methane production compared to Fe20 and Fe₃O₄30, respectively, establishing it as the most effective treatment. Additionally, DWTS12 exhibited the highest rate of methane production, reaching an impressive 147.6 cc on the 6th day. Emphasizing the practical implications, this research underscores the applicability of the proposed model for analyzing other parameters and optimizing AD performance. By delving into the potential of iron-based additives and DWTS, this study opens doors to revolutionizing methane production from dairy manure and advancing sustainable waste management practices.