Search Results (11,267)

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders. Full article

Modular integrated construction (MiC) is an innovative construction method that shifts on-site activities to a controlled factory environment, thereby offering sustainability benefits. However, current carbon management relies on labor-intensive manual data collection, causing delayed and inaccurate carbon accounting that increases greenwashing risks. Existing approaches lack real-time, automated, and trustworthy carbon tracking capabilities across fragmented supply chains. This study develops and validates the Blockchain-enabled IoT-BIM Platform (BIBP), which combines Internet of Things (IoT), Building Information Modeling (BIM), and blockchain for real-time carbon monitoring. IoT sensors automate data capture from construction equipment and BIM provides spatial visualization of carbon at the module and building levels. A Hyperledger Fabric blockchain ensures the authenticity, immutability, and traceability of carbon records. Validated on a 15-story MiC project in Hong Kong, BIBP established a cradle-to-end-of-construction baseline of 949.84 kgCO_2e/m², identifying steel and concrete as the primary hotspots (80% of material emissions). Real-time analytics demonstrated that combining high-volume ground granulated blast furnace slag (GGBS) concrete substitution, new energy sea–land multimodal transport, and 10% steel waste reduction achieves over 20% carbon savings. Furthermore, the BIBP automated data acquisition and calculation, improving assessment efficiency by 92.4%. The platform demonstrates the potential to transform carbon management from a static, retrospective evaluation into a proactive, data-driven monitoring process, equipping stakeholders with a tool to dynamically track emissions and make timely interventions toward carbon reduction targets. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

This study examines the determinants of household biogas adoption among dairy farmers in West Java, Indonesia. Despite ongoing government efforts to promote renewable energy, adoption remains limited and often not sustainable. Using cross-sectional survey data from biogas users and non-users, a binary logistic regression model was applied to assess the role of socioeconomic characteristics, resource availability, and institutional support. The results indicate that perceived time savings, fuel-cost pressure, and participation in training programs are significant positive drivers of adoption. In contrast, higher levels of formal education and livestock ownership are associated with a lower likelihood of adoption, suggesting that resource availability alone does not guarantee use. Overall, the findings highlight that adoption depends not only on economic factors but also on how well biogas systems align with household practices and capabilities. The study provides policy-relevant insights for improving the design and implementation of biogas programs in rural Indonesia. Full article

Asphalt pavements are essential to modern transport infrastructure but remain highly dependent on virgin aggregates and petroleum-based binders, resulting in high energy demand and significant greenhouse gas emissions. In response, research has advanced recycled-material solutions and low-temperature asphalt technologies. However, sustainability is still often inferred from isolated environmental indicators, without consistent consideration of mechanical durability or economic feasibility throughout the life cycle. This review provides an integrated synthesis of sustainable asphalt mixtures by jointly examining recycling strategies, temperature-reduction processes (warm-mix, half-warm-mix, and cold-mix asphalt technologies), and their combined applications through an integrated performance–cost–environment perspective. The literature reveals substantial methodological fragmentation, with limited harmonisation of functional units, system boundaries, and allocation rules, which constrains cross-study comparability. Evidence indicates that reclaimed asphalt, recycled concrete aggregates, and steel slag can maintain or improve rutting resistance, stiffness, and moisture durability while enabling material cost savings of approximately 5–68%. Temperature-reduction technologies further achieve significant energy and GHG reductions in the production phase (20–70%), with integrated recycling–temperature-reduction systems showing the most consistent combined benefits. Overall, this review demonstrates that asphalt sustainability cannot be established through single-dimensional assessments but requires harmonised life-cycle frameworks that explicitly link environmental gains to mechanical performance, durability, and economic viability. Full article

Protein and phycocyanin production is challenged by freshwater scarcity in arid coastal regions. This study assessed and optimized the cultivation of Limnospira platensis BEA 1257B in full seawater. Eight cultivation phases were conducted in 10,000 L raceways under a greenhouse to evaluate the effects of seawater content, nutrient availability, shading, CO₂ supply, and medium recycling on biomass productivity and biochemical composition. Freshwater, energy, and fertilizer savings, together with effluent characteristics of the optimized full-seawater recirculation strategy (SWR), were evaluated against a conventional freshwater cultivation process. Lower productivity was associated with high salinity and irradiance. Under long-term optimized conditions (615 days), the strain achieved stable productivities of 4.1 ± 1.4 gDW m⁻² day⁻¹ (14.8 ± 5.0 tDW ha⁻¹ year⁻¹). Increasing salinity promoted carbohydrate accumulation in the biomass (26.0% AFWD), while protein (64.4%) and C-phycocyanin (9.9%) moderately decreased. Nevertheless, protein quality, phycocyanin, and essential fatty acids remained high. Spray-dried biomass exhibited nutritionally relevant contents of K, Mg, Ca, Fe, and Mn, and complied with international food safety standards. SWR reduced energy demand by 10.5% and freshwater consumption by 12% on a surface basis, although these advantages were partially offset when expressed per unit of product, while clearly supporting environmentally sustainable and regulatory-compliant Limnospira production. Full article

The aviation industry is under increasing pressure to enhance sustainability by improving energy efficiency and reducing climate impact. A promising approach is to reduce aerodynamic drag using laminar flow technologies, particularly Natural Laminar Flow (NLF) and Hybrid Laminar Flow Control (HLFC). Previous research has primarily focused on aerodynamic performance, often considering only one technology at a time, using simplified HLFC system design models, and targeting long-range aircraft. This study adopts a more holistic approach by conducting a conceptual design of a short-medium range (SMR) aircraft equipped with both NLF and HLFC. The technologies are applied to the wing and empennage, with detailed HLFC system modelling integrated into the conceptual design process using established methods. A failure analysis is also performed to assess the performance impact of potential malfunctions. Results indicate that combining NLF and HLFC can reduce fuel consumption by 5.9% on the design mission compared to a fully turbulent reference aircraft. Moreover, selectively applying the technologies to specific components enhances fuel savings while reducing system complexity. These findings demonstrate the potential of laminar flow technologies to improve fuel efficiency in SMR aircraft and highlight the importance of integrated aerodynamic and systems-level evaluation. Full article

The lithium resource reserves in Xinjiang’s Dahongliutan reach 1.1 million tons, making it one of the most representative spodumene deposits in China. Through process mineralogy analysis, the ore was identified as having inherent characteristics that control density-based separation: Coarse crystallization, a high monomer dissociation degree, and a density contrast. Based on these mineralogical characteristics, dense-medium separation experiments were conducted to investigate the mineralogically controlled separation behavior as a function of particle size and medium density. Three process flows (two-product, pressureless three-product, and two-stage, two-product) were further designed and comparatively evaluated. It indicated that the dense-medium separation efficiency is positively correlated with the monomer dissociation degree of spodumene, and the 0.5~6 mm size fraction is the optimal particle size range because it achieves a balance between ore crushing dissociation and coarse-grain dense-medium separation adaptation. Furthermore, all three dense media processes can save grinding energy, and each of them has its own advantages and disadvantages. Comprehensively considering the grade of the concentrate, recovery, the grade of the tailings, and grinding energy consumption, it is recommended to adopt a combined process of two-stage, two-product dense-medium separation and flotation. Full article

To improve the anti-slip performance and energy-efficient torque coordination of four-wheel-drive articulated tractors operating in hilly and mountainous terrains, this study proposes an integrated control framework that combines a 7-DOF tractor dynamics model, a GA-optimized fuzzy slip-ratio controller, and a three-level dynamic torque allocation strategy. First, a control-oriented full-vehicle dynamics model is established by integrating tractor body dynamics, wheel rotational dynamics, and the Dugoff tire model. Then, a fuzzy slip-ratio controller is designed using the slip-ratio tracking error and its rate of change as inputs, and its key parameters are optimized using a genetic algorithm. On this basis, a three-level dynamic torque allocation strategy is developed to coordinate the four in-wheel motors according to wheel-load distribution and slip-related constraints. MATLAB/Simulink (version 2023a) simulations and hardware-in-the-loop (HIL) tests are carried out to validate the proposed strategy. Under the straight-line driving condition, the RMSE of the proposed GA-fuzzy controller is reduced from 0.02716 to 0.00897. Under the steering condition, the average RMSE is reduced from 0.02079 to 0.01003. In addition, under the torque-allocation validation condition, the average four-wheel RMSE is reduced from 0.29632 under equal torque allocation to 0.02159 under the proposed three-level dynamic torque allocation strategy. The results indicate that the proposed method can effectively maintain the slip ratio near its target value, suppress excessive slip and redundant torque output, and improve the anti-slip and energy-efficient performance of articulated tractors. More importantly, the study provides an integrated control framework that unifies GA-optimized slip regulation and three-level torque coordination specifically for four-wheel-drive articulated tractors. Full article

Maintenance is crucial for the durability of the existing building stock and should be perceived as an opportunity to improve the built environment. The implementation of thermal retrofitting measures to the building’s envelope enhances global energy performance, which is economically and environmentally beneficial. Building-related energy consumption during the operation phase is key to tackling carbon neutrality and climate change. Introducing thermal retrofitting within the context of maintenance planning can be cost-optimizing, as it reveals the technical–economic synergy between building pathology and energy efficiency. Maintenance activities and energy demand throughout the building’s service life influence life-cycle costs (LCCs). Decision-making based on LCC awareness is an advantage for owners. This study discusses the impact of implementing an optimal retrofitting solution (ORS), according to different maintenance strategies, on the LCC of an existing single-family home. The ORS comprises the following measures: adding an external thermal insulation composite system (ETICS) to external walls, extruded polystyrene (XPS) panels to the roof, and replacing the existing windows with others with improved thermal performance. The three maintenance strategies involve different complexity levels, concerning the type, number and timing of activities. Moving beyond isolated assessments, this study develops an integrated framework that bridges based on two existing background methodologies, involving optimal thermal retrofitting and condition-based maintenance planning, which, combined with new research, enable the assessment of maintenance, energy and global LCC for a time horizon of 100 years. The evaluation of energy-related LCC is based on simulations. The results indicate that these costs represent the majority of the global LCC. The ORS has a considerable positive impact on energy and global LCC. Adopting a maintenance strategy characterized by fewer planned activities and an earlier schedule of replacement interventions, which determines the implementation of the retrofitting measures, is better in terms of LCC savings. Full article

Natural ventilation is widely regarded as an energy-saving strategy in buildings; however, under continuous mechanical ventilation in Japanese residential buildings, its performance remains insufficiently understood. This study evaluates the performance of different natural ventilation strategies for a typical two-story detached house across eight climate zones in Japan using dynamic building energy simulation. Four ventilation strategies are examined, including baseline mechanical ventilation (S0), shoulder-season natural ventilation (S1), summer night ventilation (S2), and an adaptive natural ventilation strategy with humidity constraints (S3). Annual HVAC loads, monthly variations, and the structure of cooling loads are analyzed. Results show that shoulder-season natural ventilation (S1) does not lead to energy savings and may result in a slight increase in annual HVAC loads in most climate zones. In contrast, summer night ventilation (S2) reduces annual HVAC loads by approximately 8–10% in transitional climates (CZ3–CZ5), while its effect is weaker in hot and humid regions. The adaptive strategy (S3) achieves moderate reductions of up to about 2–3% and significantly decreases the proportion of latent cooling loads. Overall, the effectiveness of natural ventilation is governed by the trade-off between sensible load reduction and latent load increase and is strongly climate-dependent. These findings provide a basis for optimizing hybrid ventilation strategies under continuous mechanical ventilation conditions. Full article

Energy efficiency in computing has emerged as a critical concern due to escalating environmental and financial costs, particularly in the context of cluster computing, where there is an ever-increasing software workload. Achieving meaningful improvements in energy efficiency requires a comprehensive understanding of the interplay between hardware and software. This research investigates how algorithmic optimisations, language choice, and parallelisation strategies influence energy efficiency and how hardware-level strategies such as underclocking, overclocking, cooling, and on-demand computing can further impact energy usage. A set of measures that can be used generally to show the impact trade-off of power and performance are defined, including the Energy Factor (EF) and a new Efficiency–Performance Score (EPS). Validation experiments on a custom-built Raspberry Pi Bramble cluster used workloads like Monte Carlo Pi simulations in Python and C. Energy and performance trade-offs were evaluated using the Energy Factor and Efficiency–Performance Score on a small example cluster to validate the approach. Results show parallelisation greatly improves energy efficiency over serial execution. Cooling slightly boosts speed under heavy loads but increases total energy use. Perhaps counter-intuitively, underclocking actually raises total energy consumption, while overclocking reduces it. Language choice also impacts efficiency, with C offering notable energy savings over Python. The findings support the hypothesis that software optimisation alone can improve energy efficiency, but the most impactful results are achieved when both software and hardware strategies are jointly considered. These insights contribute to the design of future energy-aware computing systems and provide a foundation for sustainable, high-performance computing architectures. Full article

The rising demand for cooling in hot semi-arid cities like Jaipur is putting increasing pressure on energy infrastructure and urban resilience. This study investigates the potential of Jaali, a traditional perforated screen used in Indian architecture, as a passive strategy to reduce energy demand in a contemporary office building through data-driven optimisation and computational analysis. Using detailed energy simulations in DesignBuilder, this research explores how variations in orientation, cavity depth, perforation ratio and screen thickness affect cooling performance during the summer months through a systematic parametric study generating 84 simulation configurations. The model is based on a 12-storey office building designed according to local energy codes. The results show that the optimal configuration differs by orientation. On the south façade, the optimal combination is a 100 mm Jaali with 20% perforation and a 1.5 m cavity, which delivers the best performance. The west façade performs best with a thicker 150 mm screen, the same 20% perforation ratio, and a 1.0 m cavity depth. On the east façade, the strongest performance is achieved with a 150 mm Jaali, 50% perforation, and a 1.5 m cavity, with cooling demand reduction of up to 8.71%. These findings demonstrate that traditional design elements, when optimised for modern use, can offer measurable energy savings through predictive modelling frameworks. More importantly, their widespread adoption could support urban cooling strategies, reduce peak electricity loads and contribute to sustainable development across rapidly growing cities in hot climates. The comprehensive dataset generated provides a foundation for future AI-enhanced building energy optimisation applications. Full article

Industrial production still relies heavily on thermal processes that predominantly use fossil fuels for energy. This has significant consequences for primary energy use and greenhouse gas emissions. Meanwhile, rapid advances in electrotechnologies—defined as processes that use electrical energy to transform materials through internal heat dissipation (inductive, conductive, or dielectric/microwave) or heat transfer via resistance and infrared systems—are paving the way for a transition to a non-fossil fuel-based energy supply across a wide range of temperatures and power densities. However, replacing fuel with electricity is not simply a case of making a straightforward substitution; the feasibility of this change is determined by process requirements, constraints on installation space and grid connection, the reliability and volatility of the electricity supply, and economics. This paper therefore proposes a simple, decision-oriented methodology to assess the feasibility of defossilisation from energetic and economic perspectives. The methodology centres on a “substitution coefficient” that compares the amount of fossil energy substituted by a given amount of electrical energy and benchmarks this against the primary energy intensity of electricity generation. The methodology is demonstrated using case studies from energy-intensive sectors such as cement production (using resistance and microwave methods), steel strip processing (with inductive boosting combined with resistive holding) and metal melting for cast iron and aluminium. The case studies show under which conditions electrification can be implemented as a drop-in substitute, a hybrid booster or an enabler of new production models. The results indicate where electrotechnologies can deliver primary energy savings and CO₂ reductions today and outline the conditions under which their advantages will increase as power systems become more decarbonised. Full article

Selecting the best multi-objective algorithms for photovoltaic energy storage system (PV-ESS) design remains challenging due to limited benchmarking across renewable energy studies. This study addresses this gap through a systematic evaluation of four widely used multi-objective optimization algorithms: NSGA-II, Multi-Objective Particle Swarm Optimization (MOPSO), weighted-sum scalarization, and ε-constraint methods. Performance assessment utilized three Pareto front quality metrics: Inverted Generational Distance (IGD) for convergence quality, hypervolume (HV) for objective-space coverage, and spacing for solution distribution uniformity. The algorithms were tested on PV-ESS design problems in three developing economies (Nigeria, South Africa, India) under identical problem formulations and computational resources. NSGA-II achieved superior performance across all metrics in all three case studies. For convergence quality, NSGA-II attained a mean IGD of 0.0083, outperforming MOPSO by 29%, ε-constraint by 64%, and weighted-sum by 131%. For objective-space coverage, NSGA-II achieved a mean HV of 0. 700, representing 10–16% better coverage than other methods. For solution distribution, NSGA-II showed a mean spacing of 0.076, indicating 30–117% more uniform Pareto fronts. Computational efficiency analysis revealed that NSGA-II’s runtime is between 5.5 and 7.8 h per case, providing better quality–time ratios compared to ε-constraint methods (which are 18 times slower), while avoiding MOPSO’s premature convergence. Statistical validation confirmed NSGA-II’s superiority, with p < 0.01 across all quality metrics. These results establish NSGA-II as the best algorithm for lifecycle-aware PV-ESS optimization, offering quantitative, evidence-based guidance for practitioners selecting optimization tools for renewable energy system design. The demonstrated performance leads to $ 45,000–$ 60,000 lifecycle cost savings per MW/MWh of system capacity through improved Pareto front identification. Full article