Abstract

This paper presents a techno-economic evaluation of solid oxide fuel cells powered by hydrogen produced from hybrid systems using either ethanol or methane. Steady-state models were developed based on equations and parameters reported in the literature. By integrating simulation results with cost estimations, the study provides insights into the viability and competitiveness of cell-based systems for low-carbon energy generation. Scenarios involving 21 MW (biogas) and 101.91 GWh/year of electricity production were investigated. Results indicate that cell modules are the primary cost drivers, accounting for approximately 70–80% of total capital investment, while ethanol procurement emerged as the main contributor to operational expenditures in relevant scenarios. Comparative analysis showed that the systems can achieve lower levelized costs of electricity than conventional back-up technologies such as photovoltaic systems coupled to batteries and diesel generators—reaching $112.70/MWh and $166.93/MWh in the most favourable cases. These findings highlight the technological and economic potential and suggest that, with continued development and scale-up, such systems could become increasingly competitive in future energy markets.

Abstract

In the current context of energy transition, accurately forecasting solar power production is a major challenge for the efficient operation of electricity grids that include renewable energy sources. This work aims to improve prediction performance by combining a nonlinear physical model based on the principles of photovoltaic conversion with a recurrent neural network designed for time series analysis. The underlying hypothesis is that integrating physical knowledge with data-driven learning can better capture the complexity of solar energy patterns. The proposed method involves careful selection of input variables through correlation analysis and embedding the physical model within a deep learning structure. Results, evaluated using standard error metrics, demonstrate a clear improvement in forecasting accuracy compared to conventional approaches. While the physical model considered on its own produces a high error level (root mean square error = 338.55 and mean absolute error = 182.08), methods based on artificial intelligence significantly reduce these values (long short-term memory network: root mean square error = 3.29; recurrent neural network: 2.87). The hybrid method developed in this study achieves the best overall performance (root mean square error = 2.83 and mean absolute error = 1.26). This study contributes to the development of more reliable prediction systems capable of anticipating fluctuations in solar power generation due to changing environmental conditions.

Abstract

The potential of petroleum nut (Pittosporum resiniferum) oil (PNO) as raw material for biodiesel and source of valuable compounds demands the investigation of its large-scale extraction. Previous laboratory extraction experiments of PNO were already conducted. This study investigates the scale-up of the laboratory process. DWSIM, a free chemical process simulation software, was used to generate a process model for PNO solvent extraction with a capacity of 8,881 kg/day. The Hansen solubility parameters were applied. The generated process model had comparable oil recovery to other solvent extractions. Using the process data from DWSIM, carbon footprint intensity method was applied to estimate the carbon footprint of the extraction process and determine hotspots for carbon footprint reduction. The theoretical oil yield from the simulation is 33.73%, based on theoretical solubility interactions. The total cost and carbon footprint of the process is USD 1.03 M and 143 Mg CO₂/day. The cost is high because the current price of petroleum nut is for small-scale production. The overall carbon footprint can be reduced by 3.60% if the de-oiled cake were turned to briquettes and used as fuel to generate steam. This study contributes to alternative renewable energy sources as well as carbon footprint reduction analysis for process industries.

Abstract

The transition to renewable energy is essential for addressing climate change, with biofuel positioned as a key transport alternative. Despite Indonesia’s B20 and B30 mandates, public acceptance remains limited, highlighting the need to understand behavioural factors. This study examines the relationships between knowledge, attitude, and perception of biofuel and tests the mediating role of attitude. Data were collected through a 20-item survey completed by 256 public transport users. Structural Equation Modelling was performed using the semopy library in Python. The results indicate that knowledge has a positive but statistically non-significant effect on attitude (β = 0.10; p > 0.05; 95% CI [−0.07, 0.26]), whereas attitude strongly and significantly predicts perception (β = 0.62; p < 0.001; 95% CI [0.38, 0.85]). The direct effect of knowledge on perception was non-significant, suggesting that higher knowledge alone does not necessarily translate into favourable public perception. Mediation analysis also shows that attitude does not significantly mediate the knowledge–perception pathway, as the bootstrap confidence interval includes zero. Model fit indices demonstrate acceptable fit (Root Mean Square Error of Approximation = 0.07; Comparative Fit Index = 0.92; Standardized Root Mean Square Residual = 0.06). Overall, attitude plays a much stronger role than direct knowledge in shaping public perception of biofuel. These findings imply that policy strategies emphasizing attitude formation—such as value framing, community engagement, and trust-building— may be more effective than purely informational campaigns in enhancing biofuel acceptance.

Abstract

The circular economy has become a framework for improving resource efficiency, reducing waste, and supporting climate change mitigation. Yet, its adoption in transition economies such as Serbia remains limited by structural and financial constraints. This study applies the Analytical Hierarchy Process to evaluate perspectives from private-sector companies and circular-economy experts on the key barriers to circular economy implementation. Result values represent the normalised weights (priority vectors) where a value closer to 1.0 indicates a higher level of importance or impact of a specific factor within the model. The results reveal a discrepancy between stakeholder groups: while companies emphasised social barriers, including weak consumer culture and resistance to change, experts identified financial (0.338) and institutional barriers (0.272) as the most influential. At the sub-factor level, changes in accordance with European directives (0.062) emerged as the most significant social barrier, followed by understanding the concept of the circular economy (0.050) and education regarding the circular economy (0.042). Results provide evidence-based insights into the dominant challenges limiting circular economy adoption in Serbia and the relevance of multi-criteria decision-making for prioritising circular economy barriers in transition economies.

Abstract

Concentrated solar power plants, a well-established technology, employ efficient thermal energy storage systems, utilizing molten salts. This study endeavours to diminish energy consumption in conventional residential hot water systems through the utilization of solar energy and the molten salt's thermal storage capacity. To achieve this objective, a pioneering hybrid domestic hot water system is proposed, integrating solar power for application within smart building contexts. This hybrid configuration encompasses solar heliostats to amplify solar irradiance, a dedicated molten salt reservoir for efficient thermal energy storage, and a heat exchanger facilitating the heating of domestic hot water. A specific molten salt mixture, comprising 60% of Sodium Nitrate (NaNO₃) and 40% of Potassium Nitrate (KNO₃), is meticulously selected for its superior thermal capacity. Solar energy is effectively harnessed to leverage renewable and environmentally sustainable energy sources, focusing sunlight onto the molten salt reservoir within the smart building infrastructure. Subsequently, the stored thermal energy within the reservoir is efficiently utilized to heat water through the heat exchanger. The performance evaluation of this hybrid system is meticulously conducted through rigorous experimentation within a test building located in Ankara, Turkey.

Abstract

In this article, we improved the control of the three phase multilevel inverter for a photovoltaic system connected to the grid, in order to minimize the total harmonic distortion of current. This topology being considered as a new converter topology applied to photovoltaic systems and has the advantage of very high-energy efficiency, but has the disadvantage linked to harmonics injected into the grid, which causes switching of states if the number of levels increases, which increases the rate of harmonic distortion. In this paper, an improved control based on sinusoidal control (SPWM) and space vector control (SVPWM) was proposed to improve the control of multilevel inverters. The simulation is based on MATLAB SIMULINK platform is presented for different multilevel inverter topologies with fewer switches and with different control methods and sinusoidal pulse. A detailed comparison of various Sinusoidal Pulse Width Modulation (SPWM) and vector control schemes (SVPWM) is presented in this article with reference to Total Harmonic Distortion (THD<5%) in the output voltage and current of the grid. This control strategy eliminates current harmonics and improves the quality of energy supplied to the grid by the photovoltaic system, and It can be seen that among all the control methods, the THD is minimum at the output of different multilevel inverter topologies.

Abstract

This paper presents a techno-economic analysis and optimal sizing of a hybrid microgrid for a lunar surface base, combining a photovoltaic power system, a battery energy storage system, an electrolyzer, a hydrogen storage tank, and a hydrogen fuel cell. The components are coordinated by an energy management strategy that first supplies the load directly from solar power, then charges the battery, and finally produces and stores hydrogen for use by the fuel cell during power shortages. An hourly simulation model is developed using a representative lunar base load profile and environmental conditions consistent with space operation. The influence of photovoltaic capacity and battery technology is examined for three battery types: lithium-ion, lead–acid, and nickel–cadmium. For each case, a complete economic evaluation is performed, including capital, operation and maintenance, fuel, and replacement costs, together with system-level indicators of energy cost and storage cost. In addition, a mass-based assessment relates subsystem sizing to launch mass and transportation cost, which are critical for space missions. The results identify combinations of photovoltaic capacity and battery type that minimise energy cost while satisfying reliability requirements for a crewed lunar base, and they provide design guidelines for efficient hybrid microgrids in space applications.

Abstract

One of the key factors affecting both the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems and the indoor thermal comfort of inhabitants is the indoor air temperature in buildings. A primary parameter that significantly influences HVAC energy demand and thermal comfort is the clothing insulation of the users. Although clothing insulation is carefully selected according to well-known standards, its value depends on various factors: i) gender, as males tend to feel warmer than females in cool conditions; ii) metabolic heat production; and iii) the performed activity. Consequently, building occupants can adjust their thermal comfort by modifying clothing insulation values based on outdoor temperature variations. Additionally, the indoor set-point temperature values in a building also depend on its weather zone, which is influenced by outdoor air relative humidity and outdoor air temperature. This work presents a dynamic simulation model for analyzing comfort conditions and energy consumption for space cooling and heating, taking into account changes in clothing insulation values for a specific user. A particular office room was considered for the study. The space heating and cooling energy demand of the office was calculated using a suitable thermal zone modelled by Type 56 of TRNSYS, coupled with the Google SketchUp TRNSYS3d plug-in. The selected case study is an office room located at the University of Federico II in Naples, Southern Italy. To evaluate both comfort conditions and energy demands, several sensitivity analyses were conducted by varying the clothing insulation and office set-point temperature. A sensitivity analysis on the weather zone of the office building was also performed to assess the dependency of clothing insulation values on weather parameters. The presented model serves as a suitable and general tool to propose simple clothing variation behaviors, aiming to achieve a trade-off among dress code, energy saving, and thermal comfort.

Abstract

The conventional energy sources, like fossil fuels, are considered unsuitable due to pollution; researchers are investigating new ways to source renewable energy, such as solar energy, which is transformed into electrical energy through photovoltaic cells, depending on prevailing weather conditions. The traditional maximum power point tracking techniques face challenges in identifying the maximum power point due to variable weather conditions, thereby diminishing the efficiency of the photovoltaic system. Optimizing the maximum power point tracking process is essential to address this issue, and any effective maximum power point tracking must adapt to changing environmental conditions. This paper presents a simulation and evaluation of a novel variable irradiance particle swarm optimization algorithm to improve the tracking rate and performance under fluctuating weather conditions (unlike the conventional particle swarm optimization methods). This method includes a current-sensing mechanism that detects 5% changes in current to reinitialize the parameters. The contributions of this work are declared in the development of an enhanced particle swarm optimization algorithm fitted for variable irradiance environments, Integration of a dynamic reset mechanism based on real-time current variations, and validation of performance through MATLAB/Simulink simulations using a single PV panel and comparing it with standard maximum power point tracking methods. The performance of the variable irradiance particle swarm optimization algorithm showed improvements in speed with a response time of less than 0.1s, reduced the steady state ripple by 1%, and efficiency for maximum power point tracking of 99%, with statistically validated performance using the Friedman test, confirming its robustness for practical photovoltaic applications.

Abstract

Pulsating Heat Pipe offers an effective passive cooling method to mitigate the efficiency drop in solar photovoltaics caused by excessive temperature. In the present work, single-loop Pulsating Heat Pipe using methanol and ethanol working fluids with filling ratios of 35%, 45%, and 55% with initial pressures of 30 kPa, 50 kPa, and 100 kPa were tested at power input of 13.8-60 W. The factors that affect startup performance and thermal resistance were studied experimentally on a lab scale. The results demonstrated that Pulsating Heat Pipe charged with methanol shows superior startup performance and lower thermal resistance compared to ethanol. Experimental results indicate that a 35% filling ratio provides the fastest startup condition, while the best thermal performance is obtained at 45%. Decreasing the initial pressure on Pulsating Heat Pipe can accelerate startup and improve thermal performance. Integrating Pulsating Heat Pipe with the solar panel can potentially reduce the panel temperature by up to 5.1 °C.

Abstract

Electric-vehicle charging can erode operating margins in distribution networks, yet impacts are often localized rather than system-wide. This study quantifies unmanaged charging effects on the Oujda 60/22-kV system using a geo-referenced steady-state power-flow model coupled with a stochastic charging generator for cars, motorcycles and buses. The network representation comprises 124 substations and 117 branches, and we examine snapshots at 0%, 10% and 30% adoption. In thermal terms, the bulk of the network remains comfortably loaded: the share of lines operating at ≤70% of their rating is 81% at baseline, 79% at 10% and 78% at 30%. Localized constraints intensify modestly with penetration: the share of overloaded lines (>100%) rises from 6% to 7% and 9%, and the worst-loaded span increases from 151.6% to 157.2% and 168.1%. Voltage performance is similarly robust in bulk (median around 0.97 per unit), with a small weak-bus tail near charging hotspots. All cases converged reliably. The workflow is lightweight and reproducible, supporting feeder-level hosting-capacity screening and motivating targeted reinforcement or simple smart-charging measures in data-constrained systems.

Abstract

Desalination is now being used more frequently to effectively address global water shortages and provide much-needed freshwater to arid regions and communities in need. Although there have been many improvements in technology at desalination plants, all the other stages involved in operating a desalination system are still affected by inefficiency, increased energy consumption, rising costs, and negative environmental impacts. Overcoming these problems requires improvement across the entire supply chain, rather than just at the plant level. This study assesses the effects of Machine Learning on enhancing the efficiency, robustness, and sustainability of desalination supply chains. For demand forecasting, supervised learning is utilised to detect deviations and optimise supply chain frameworks, which incorporate reinforcement learning, actual data, and trial situations. The integrated Machine Learning has reduced downtime by 18%, improved product distribution by 12%, lowered operating expenses by 14.2%, and nearly halved the company’s carbon emissions compared to standard operations. The results confirm that Machine Learning encourages more than minor changes and has a significant impact on the water management process. Using Artificial Intelligence in desalination helps experts and planners meet the issues of increasing water use and sustainability worldwide. It introduces a fresh, multi-technique Machine Learning model that enhances water supply management and provides a pathway toward greener, more robust desalination methods, supporting the goal of sustainable water security.

Abstract

The Journal of Sustainable Development Indicators - JSDI is established as a premier international platform dedicated to addressing the most critical societal challenge of our era: achieving the sustainable development of energy, water, and environmental systems. Recognizing that the transition to a knowledge-based economy requires rigorous quantification, the journal provides a comprehensive framework for assessing sustainability through a diverse array of standardized, conventional, and innovative matrices. By prioritizing research that explicitly demonstrates the extent to which results address the United Nations Sustainable Development Goals or other vital issues impacting the well-being of global communities, JSDI bridges the gap between theoretical modelling and actionable policy learning.

Abstract

Brick manufacturing in Nepal relies heavily on coal and biomass mixtures, resulting in high energy use and significant air pollution. This study investigates the energy-saving potential and environmental implications of fuel substitution in brick kilns, focusing on coal, biomass, and natural gas. A combination of experimental data collection and steady-state computational fluid dynamics simulations was employed to analyse thermal performance, airflow characteristics, and pollutant formation along the kiln. The study evaluated the effect of varying inlet air velocities on fuel combustion, excess air, and kiln efficiency. For coal and biomass, inlet air velocities ranging from 4.5 to 6.1 m/s were analysed, while natural gas was studied as an alternative fuel for coal and biomass mixture under optimum condition. The results revealed that excess air in the combustion zone significantly influences the thermal efficiency, with coal and biomass showing higher excess air levels compared to natural gas. The kiln efficiency was determined using both direct and indirect method highlighting the potential for energy savings and emission reduction through optimized air supply and fuel transition. The findings provide quantitative insights into the benefits of energy transition in brick industries, demonstrating that adopting cleaner fuels and controlling excess air can improve efficiency and reduce environmental impact. 

Abstract

The Journal of Sustainable Development of Natural Resources Management (JSDNAREMA) is dedicated to advancing knowledge and providing a platform for scientific discourse on Sustainable Resource Stewardship. It was launched on 1 January 2025 and has published 16 scientific articles and two editorials in its first volume. The articles treat topics such as pollution treatment, environmental accounting, Process Integration and optimisation for pollution reduction, resource recovery from waste, use of digital technologies for improved management of natural resources, water management in agriculture, wider water management issues, capture and seasonal management of solar energy, integrated strategies for energy efficiency and Circular Economy development, and food security.

Abstract

Perovskite solar cells have attracted significant attention in recent years due to their rapidly rising power conversion efficiencies and low-cost fabrication potential, and the incorporation of nanomaterials provides new pathways to further improve their performance and stability. In this work, the structure of the perovskite solar cell is first introduced, accompanied by the function of buffer layer, perovskite photosensitive layer, and electrode. The advantages and drawbacks are analyzed by employing diverse nanomaterials in the three critical regions of perovskite solar cells. The enhanced performance is discussed in terms of the buffer layer, the perovskite photosensitive layer, and each component with the incorporation of nanomaterials, and the underlying mechanisms are examined to reveal how nanomaterials improve photoelectric conversion performance. In perovskite solar cells, nanomaterials play a critical role by passivating defects, suppressing electron–hole recombination, accelerating charge separation, and enhancing carrier transport, thereby improving device efficiency and stability. This study provides insights into the rational design of nanomaterial-based strategies to further advance the development of high-performance and stable perovskite solar cells.

Abstract

The comprehensive quest of a sustainable energy transition has suited a defining challenge, as nations seek the data-driven insights that concurrently address climate change, economic growth, and energy security issues. Energy sector in Pakistan like many other underdeveloped countries, currently faces similar challenges. Therefore, comprehensive data-driven research is required to find sustainable energy solutions. This research presents a method for gradual increase in variable renewable energy sources (VRES) and optimized retirement of fossil fuel-based power plants, finally to attain 100% integration of VRES till 2050 using H2RES modelling tool. The results demonstrate that 100% share of Pakistan's electricity demand can be met through energy storage technologies, flexibility options, endogenous investments into VRES, power to X technologies, etc. This research emphasizes the potential of H2RES model in achieving SGDs 7 and 13 targets as a template to provide a scalable framework for other developing nations facing similar power system issues, making it globally relevant.

Abstract

The growing integration of photovoltaic systems into modern energy networks demands intelligent monitoring solutions to optimize performance and ensure long-term reliability. This study proposes a predictive framework based on an Adaptive Neuro - Fuzzy Inference System for estimating the power output of photovoltaic cells using electroluminescence images and current - voltage curves. The model extracts three key features from electroluminescence images counts of black, grey, and white pixels which reflect internal defects and degradation patterns. These image derived parameters are correlated with electrical performance indicators obtained from current voltage curves to enhance prediction accuracy. By combining the learning capabilities of neural networks with the interpretability of fuzzy logic, the adaptive neuro - fuzzy inference model provides accurate estimations of the normalized maximum power point while maintaining transparency through interpretable fuzzy rules. The system is implemented on a cloud - based platform, enabling real - time data analysis and offering valuable insights for performance assessment and quality control of photovoltaic cells. Experimental validation on a dataset of six hundred crystalline photovoltaic cells demonstrates high predictive accuracy, achieving a mean absolute error of 0.073 and a mean squared error of 0.0084. This approach enhances photovoltaic module efficiency evaluation and supports the development of more effective monitoring strategies in photovoltaic systems.

Abstract

This paper explores the potential of hydrogen as an alternative aviation fuel, focusing on its production, storage, and application in aircraft propulsion. Hydrogen offers high specific energy and zero carbon emissions, making it a strong candidate for decarbonising aviation. The paper compares production methods and evaluates storage options including liquid hydrogen, cryo-compressed hydrogen and metal hydrides. It further examines hydrogen’s use in modified gas turbines and fuel cells for electric propulsion. Key industry initiatives, along with safety and environmental considerations, are discussed together with projections for future fuel demand. In the case of hydrogen, under the most optimistic scenario, demand is projected to reach 568 Mt by 2050, whereas the demand for sustainable aviation fuel is expected to reach 445 Mt by 2050. The paper concludes that hydrogen, especially in liquid form, is a viable long-term solution for medium- and long-range aviation, assuming the development of scalable production and supporting infrastructure.