Search Results (4,343)

Traditional forced-air cooling systems suffer from excessive energy consumption and noise pollution. This study proposes an innovative passive cooling strategy through developing aqueous radiative cooling coatings made from a combination of TiO₂-decorated carbon nanotube (TiO₂-CNT) hybrids and polyacrylic acid (PAA), designed to simultaneously enhance the heat dissipation and improve the mechanical strength of the coating films. Based on CNTs’ exceptional thermal conductivity and record-high infrared emissivity, bead-like TiO₂-CNT architectures have been prepared as the filler in PAA. The TiO₂ nanoparticles were in situ grown on CNTs, forming a rough surface that can produce asperity contacts and enhance the strength of the TiO₂-CNT/PAA composite. Moreover, this composite enhanced heat dissipation and achieved remarkable cooling efficiency at a small fraction of the filler (0.1 wt%). The optimized coating demonstrated a temperature reduction of 23.8 °C at an operation temperature of 180.7 °C, coupled with obvious mechanical reinforcement (tensile strength from 13.7 MPa of pure PAA to 17.1 MPa). This work achieves the combination of CNT and TiO₂ nanoparticles for strong radiative cooling coating, important for energy-efficient thermal management. Full article

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management. Full article

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

This study examines the progress, challenges, and successes in implementing Sustainable Development Goal 12 (SDG12), focusing on responsible consumption and production, using Qatar as a case study. The State has integrated Sustainable Consumption and Production (SCP) into national policies, established coordination mechanisms, and implemented action plans aligned with SDG12 targets. Achievements include renewable energy adoption, waste management reforms, and sustainable public procurement, though challenges persist in rationalizing fossil fuel subsidies, addressing data gaps, and enhancing corporate sustainability reporting. Efforts to reduce food loss and waste through redistribution programs highlight the country’s resilience, despite logistical obstacles. The nation has also advanced hazardous waste management, environmental awareness, and sustainable tourism policies, though gaps in data systems and policy coherence remain. Qatar’s approach provides a valuable local-to-global example of balancing resource-dependent economies with sustainability goals. Its strategies and lessons offer potential adaptability for other nations, especially those facing similar challenges in achieving SDG12. By strengthening data systems, enhancing policy integration, and fostering regional and international cooperation, Qatar’s efforts underscore the importance of aligning economic growth with environmental stewardship, serving as a blueprint for global sustainability initiatives. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

As the penetration of intermittent renewable energy sources continues to increase, ensuring reliable power system and frequency stability is of importance. Battery energy storage systems (BESSs) have emerged as an important solution to mitigate these challenges by providing essential grid support services. In this context, a state-of-charge (SOC)-frequency control strategy for grid-forming BESSs is proposed to enhance their role in stabilizing grid frequency and improving overall system performance. In the system, the DC-link capacitor is regulated to maintain the angular frequency through a matching control scheme, emulating the characteristics of the rotor dynamics of a synchronous generator (SG). Thereby, the active power control is implemented in the control of the DC/DC converter to further regulate the grid frequency. More specifically, the relationship between the active power and the frequency is established through the SOC of the battery. In addition, owing to the inevitable presence of differential operators in the control loop, a high-gain observer (HGO) is employed, and the corresponding parameter design of the proposed method is elaborated. The proposed strategy simultaneously achieves frequency regulation and implicit energy management by autonomously balancing power output with available battery capacity, demonstrating a novel dual benefit for sustainable grid operation. To verify the effectiveness of the proposed control strategy, a 0.5-Hz frequency change and a 10% power change are carried out through simulations and also on a hardware-in-the-loop (HIL) platform. Full article

Multi-objective evolutionary algorithms (MOEAs) have emerged as powerful optimization tools for addressing the complex, often conflicting goals present in modern waste disposal systems. This review explores recent advances and practical applications of MOEAs in key areas, including waste collection routing, waste-to-energy (WTE) systems, and facility location and allocation. Real-world case studies from cities like Braga, Lisbon, Uppsala, and Cyprus demonstrate how MOEAs can enhance operational efficiency, boost energy recovery, and reduce environmental impacts. While these algorithms offer significant advantages, challenges remain in computational complexity, adapting to dynamic environments, and integrating with emerging technologies. Future research directions highlight the potential of combining MOEAs with machine learning and real-time data to create more flexible and responsive waste management strategies. By leveraging these advancements, MOEAs can play a pivotal role in developing sustainable, efficient, and adaptive waste disposal systems capable of meeting the growing demands of urbanization and stricter environmental regulations. Full article

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article