Search Results (8,523)

Climate change and increasing pressure on natural resources highlight the limitations of the predominantly linear economic model of production and consumption, and the need for more integrated sustainable approaches. Circular economy (CE) strategies offer a potential alternative, contributing to reduction of emissions and resource conservation, while increasing the resilience of systems to climate shocks. This review examines the relationship between CE, emission reduction, resource efficiency, and climate resilience. A bibliometric analysis was first conducted for the initial subset of reviewed articles obtained from the Web of Science (WoS), following particularly predesigned criteria for inclusion and exclusion, to establish the main conceptual and thematic structures in the literature. Across the 72 studies included in the final review, sectors such as construction, energy, and waste management emerged as prominent application fields, with strategies like reuse, recycling, industrial symbiosis, and product as a service among frequently investigated strategies. The contribution of CE to climate resilience in the current review remains limited. Resilience, however, is often harder to measure and less well-documented. To address this gap, an integrated framework is proposed that brings together indicators of emissions reduction, resource saving, and climate resilience, allowing potential synergies and trade-offs among these dimensions. The findings further highlight the need to move from isolated assessment towards a more systematic interconnected assessment. Full article

Personal thermal management (PTM) textiles can reduce building energy consumption and improve personal comfort, yet their practical application is constrained by the inherent trade-off between flexibility, thermal insulation, and mechanical strength. Herein, porous single-component polyimide (PI) fibers were fabricated via coagulation bath-modulated wet spinning of 3,3′,4,4′-benzophenone tetracarboxylic dianhydride (BTDA)–4,4′-oxydianiline (ODA) poly(amic acid) (PAA). By tuning the EtOH/H₂O ratio (20/80–60/40) and winding speed (2.6–13.1 mm/s), the fiber cross-sectional morphology evolves from finger-like macropores to uniform spongy networks, with diameters controllable from 120 to 335 μm. The PI porous fibers exhibit a maximum tensile strength of 56.79 MPa, elongation at break of 13.89%, toughness of 4.98 MJ/m³, and thermal conductivity as low as 0.043 W·m⁻¹·K⁻¹. The highly imidized structure was confirmed by FTIR (imidization index = 0.848), and TGA revealed high thermal stability with 5% weight loss temperatures of 491 °C (N₂) and 488 °C (air). Compared with commercial insulators, a single-layer PI fabric (0.892 mm) shows thermal insulation comparable to that of the thicker aramid 1313 fabric (1.588 mm) under the same 100–200 °C hot-plate conditions, while also exhibiting self-extinguishing behavior equivalent to that of aramid 1313. The 5-layer PI stack (3.637 mm) is only half as thick as glass fiber cotton (7.342 mm) but retains 84–91% of its temperature difference, delivering 1.7–1.8 times higher thickness-normalized insulation efficiency. The ultrathin porous PI fabrics integrate robust mechanical performance, excellent thermal shielding, and flame retardancy, and are promising for extreme-environment thermal management including fire protection, spacecraft thermal control, and battery insulation. Full article

In this work, Intelligent Bidirectional Long Short-Term Memory with Temporal Fusion Transformer-based prediction and Proximal Policy Optimization (IBT-PPO) is proposed in response to the challenges of uncertain renewable generation, fluctuating demand, and inefficient energy scheduling in hybrid renewable energy systems. The algorithm is based on a dual-stage framework that integrates machine learning forecasting with reinforcement learning-based planning. Initially, a hybrid Bi-LSTM-TFT model is employed to generate accurate short-term forecasts of wind power, solar power, and demand, which employs temporal dependencies and multi-horizon patterns. After that, the PPO strategy is designed to optimize scheduling decisions, adaptively balancing battery usage, grid reliance, and renewable dispatch. To enhance robustness, adaptive feature weighting and temporal gating strategies are incorporated, ensuring stable convergence and reduced planning redundancy. Subsequently, the energy allocation is refined through iterative learning to minimize operational cost and maximize renewable penetration. The proposed framework is evaluated as an offline/post hoc forecasting and scheduling approach, with the Bi-LSTM–TFT module exploiting historical temporal representations and the PPO agent optimizing energy-management decisions based on the resulting forecasts. The experimental evaluation is carried out using the Open Power System Data (OPSD) dataset, which provides realistic time-series data for wind, solar, demand, and electricity prices. Thus, the IBT-PPO system integrates multi-horizon probabilistic forecasting and adaptive feature weighting for better prediction and planning accuracy and achieves a 24.1% cost reduction and 95.5% renewable utilization, thereby advancing efficient and intelligent energy prediction and planning. Full article

Sand production constrains artificial-lift reliability, shutdown management, and energy efficiency. This study develops an integrated experimental framework linking particle settling, bulk sand transport, shutdown-related plugging, and electrical demand using 12 anonymized Kumkol wells. The database contains 4380 daily records, 720 laboratory tests, 84 shutdown/restart events, 3600 energy points, 4800 high-frequency restart samples, and 132 maintenance events. In 480 settling column tests, Stokes yielded R² = 0.937 and MAPE = 24.8%; one-parameter calibration improved R² to 0.981 and MAPE to 11.3%, with leave-one-well-out MAPE of 11.4%. In 240 flow loop tests, the full-data non-unstable threshold was 1.052 m/s; nested held-out well accuracy was 87.9%, and the strict stable criterion yielded 83.8%. Shutdown duration was the dominant field predictor: plug odds rose 3.87-fold per 10 h, and nested threshold validation yielded 78.6% accuracy. For ESPs, the ratio-based SEC minimum occurred near 0.773 m/s. A denominator-free active power model controlling for pressure rise, VFD frequency, sand concentration, and well effects achieved R² = 0.851 and retained a positive velocity coefficient in all leave-one-well-out fits. The results define a locally calibrated energy–transport operating window; laboratory transport thresholds are not claimed as direct field setpoints without hydrodynamic scaling and on-well verification. The flow loop threshold block comprises only four independent wells; accordingly, its held-out results are treated as small-cluster evidence, and the numerical velocities remain laboratory reference constraints rather than direct field settings. Full article

The increasing penetration of variable solar and wind generation requires flexible resources capable of improving energy balancing, reliability, and renewable energy utilization. This review critically assesses biofuel-integrated hybrid energy systems as complementary architectures for renewable energy integration, connecting system configuration, energy conversion efficiency, storage and dispatch, intelligent control, environmental performance, and scalability. A critical narrative and integrative approach was applied using literature retrieved from Scopus and Web of Science, and organized across solar–bioenergy, wind–bioenergy, multi-source, storage-supported, microgrid, and multi-energy configurations. The evidence indicates that biomass-derived fuels can provide dispatchable and storable renewable energy that complements variable generation and supports decentralized and multi-energy applications. However, no architecture is universally superior, and greater hybridization does not inherently result in higher thermodynamic efficiency. Performance depends strongly on resource complementarity, feedstock availability and quality, conversion pathways, storage requirements, and operating strategy. Advanced energy management, model predictive control, machine learning, and digital twins can improve system coordination, although much of the available evidence remains simulation-based or limited in experimental scale. Environmental benefits are likewise pathway- and boundary-dependent, particularly when avoided emissions, coproduct allocation, infrastructure, and feedstock supply chains are considered. Overall, biofuel-integrated hybrid systems represent an application-dependent flexibility option rather than a universally optimal solution; future progress requires dynamic uncertainty-aware modeling, harmonized techno-economic and life-cycle assessment, and greater pilot- and industrial-scale validation. Full article

To reduce the communication and centralized-computation burden associated with frequent intraday upper-level updates under renewable-energy uncertainty, this paper proposes an edge-autonomous multi-objective energy management strategy with an offline–online architecture. In the day-ahead stage, Gaussian Copula modeling and trajectory screening generate temporally correlated photovoltaic (PV) scenarios. The conventional Strength Pareto Evolutionary Algorithm 2 (SPEA2) first constructs a base library C0. A dual-space coordinated selection mechanism then retains every C0 strategy exactly and adds complementary trajectories according to their objective responses and differences in energy storage system power trajectories. In the intraday stage, the edge controller combines current local measurements with preloaded PV, load, and price profiles, propagates every stored candidate over a receding horizon, and applies the first action of the strict minimum-composite-cost candidate. The case-study results show that, without relying on intraday upper-level communication, the proposed method achieves a composite-objective value close to that of the perfect-information centralized reference. Full article

The power demand of High-Performance Computing (HPC) infrastructures exhibits both stable weekly regularities and rapid workload-driven fluctuations, which are difficult to capture reliably with a single modeling paradigm. Achieving more sustainable HPC operation requires accurate forecasts at multiple horizons: short-term predictions support operational control (e.g., proactive power capping and energy-aware scheduling), whereas long-term forecasts are essential for planning activities (e.g., capacity provisioning and energy procurement). Together, these capabilities reduce operational cost and risk while enabling more efficient and sustainable datacenter management. This paper investigates multi-horizon forecasting of aggregated active power consumption in an operational HPC datacenter utilizing a four-month dataset (five-minute time intervals) from the University of Turin. We propose a hybrid residual-learning framework that integrates a long-term structural forecaster with a short-term residual corrector utilizing a Temporal Convolutional Network (TCN) to address the simultaneous presence of weekly regularities and short-term workload-induced fluctuations. Assessment utilizing a rolling-origin protocol covers a timeframe of 15 min to 6 h and extends 1 to 3 weeks into the future. Performance of the proposed approach has been compared against the SARIMA baseline. Full article

There is a growing need for high-efficiency power electronic converters that can effectively convert energy, regulate voltages, and enhance power quality in standalone power generators, as the use of renewable energy sources and battery energy storage devices increases. The quasi-impedance-source inverter (qZSI) has attracted significant interest due to its single-stage buck-boost operation, continuous input current, reduced reliance on passive elements, and increased reliability. In this paper, the control strategy and implementation of the qZSI with battery assistance for standalone photovoltaic energy generation are discussed. To analyze the operational characteristics and design the control strategy of the qZSI, the system equations are linearized around the nominal operating point to develop a small-signal model, from which the direct current (DC) side and alternative current (AC) side transfer functions are derived and used as the basis for controller design. Using the proposed model, hybrid controllers are designed to control the shoot-through duty cycle, maintain DC link voltage stability, and battery charging to achieve stable power generation. Furthermore, the SPWM technique is applied to produce AC power with minimal harmonic content and higher efficiency. Application results show stable dynamic behavior, effective battery energy management, improved voltage regulation, and reduced harmonic distortion in the output waveform. The main contribution is a low-complexity coordinated PI and PR control framework for standalone battery-assisted qZSI operation, experimentally validated under DC- and AC-side disturbances without requiring an additional battery-side power-conversion stage. Full article

Double-skin façades (DSFs) offer a promising building-envelope strategy for improving thermal management by promoting heat’s removal from the façade cavity before it is transferred toward the indoor environment. This study numerically investigates the influence of cavity width on the thermo-fluid performance of a mechanically ventilated DSF under summer operating conditions. A two-dimensional computational fluid dynamics (CFD) model was developed using the RNG k-ε turbulence model together with the discrete ordinates radiation model. Mechanical ventilation was imposed through a velocity inlet of 0.765 m s⁻¹, with an inlet air temperature of 17 °C and a solar radiation intensity of 365.4 W·m⁻². The numerical model was validated against published experimental temperature measurements, yielding an average absolute relative error of approximately 5.65%. The validated model was subsequently applied to cavity widths ranging from 0.10 to 0.70 m. Increasing the cavity width substantially modified the airflow development and thermal field. The monitored temperature decreased from 31.66 °C at 0.10 m to 17.64 °C at 0.50 m, while further enlargement produced only minor reductions to 17.43 and 17.28 °C at 0.60 and 0.70 m, respectively. The total heat-transfer rate increased from approximately 1000 W at 0.10 m to a maximum of 1388 W at 0.50 m before slightly decreasing to 1379 and 1376 W at 0.60 and 0.70 m, respectively. This temperature reduction enhances heat removal from the façade cavity, helping to limit heat transfer toward the indoor environment and improve indoor thermal comfort under summer conditions. These results demonstrate a non-monotonic relationship between cavity width and heat-removal performance, with 0.50 m providing the highest heat-transfer rate among the investigated configurations. This result is specific to the geometry, boundary conditions, ventilation rate, and operating conditions considered in the present study. It should not be interpreted as a universally optimal cavity width for mechanically ventilated DSFs. The findings highlight the importance of cavity-width selection in the thermal management and design of mechanically ventilated DSFs for energy-efficient building envelopes. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Energy efficiency (EE) has emerged as the paramount and cost-effective key strategy for achieving climate and energy objectives. Nonetheless, energy efficiency measures (EEMs) are frequently hindered by various barriers. Barriers, and to a lesser extent drivers, have been thoroughly examined across several contexts and sectors; nevertheless, research on barriers and drivers in Tunisia is still lacking. Thus, in the present paper, we explore the key barriers and drivers affecting industrial energy efficiency in Tunisia using qualitative analysis. Semi-structured interviews were performed with a set of industrial firms. The study included the major external key stakeholders. The findings show that economic barriers resulting from high investment costs, limited access to capital, and a lack of incentives are major impediments to the adoption of energy efficiency measures in Tunisia and that technical, institutional, regulatory, informational, awareness, and behavioral barriers may further stymie investment in these measures. This study’s main drivers are cost reductions, subsidies, management commitment, and awareness campaigns. The results offer Tunisian policymakers a useful resource for understanding the barriers to energy efficiency that exist today and creating new policies to get over them. The findings are also a useful resource for other countries. Full article

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a transformative technology for optimizing the operation, control, monitoring, and maintenance of battery storage systems. This review provides a comprehensive overview of AI-driven BESS technologies for renewable energy applications. The study examines recent advances in machine learning, deep learning, reinforcement learning, and hybrid intelligent algorithms applied to battery state estimation, energy management, fault diagnosis, predictive maintenance, thermal management, and lifetime prediction. Furthermore, the integration of AI-based BESSs with photovoltaic systems, wind farms, microgrids, and smart grids is critically analyzed. The review highlights the advantages of AI techniques in improving system efficiency, reliability, adaptability, and decision-making capabilities under uncertain operating conditions. Current challenges, including data quality, model interpretability, computational requirements, cybersecurity concerns, and real-time implementation issues, are also discussed. Finally, emerging research directions such as digital twins, explainable artificial intelligence, federated learning, and edge intelligence are explored to provide insights into the future development of intelligent battery storage systems. This review aims to serve as a valuable reference for researchers, engineers, and practitioners working at the intersection of artificial intelligence, battery technologies, and renewable energy systems. Full article

The transition toward Open Radio Access Network (O-RAN) architecture has enabled unprecedented intelligence and flexibility in 5G and 6G network slicing. However, a fundamental challenge remains in managing the tension between radio unit energy efficiency and the strict Service Level Agreement (SLA) requirements of Ultra-Reliable Low-Latency Communication (URLLC) slices, particularly under highly dynamic traffic conditions. Existing O-RAN approaches suffer from a timescale conflict where Non-Real-Time (Non-RT) policy planners optimize for long-term energy but fail to react to rapid traffic surges, while Near-Real-Time (Near-RT) controllers prioritize reliability at the cost of significant energy over-provisioning. To address this, we propose H-RLS, a hierarchical multi-timescale framework that decouples control into a Non-RT Proximal Policy Optimization (PPO) agent for strategic, energy-aware policy planning and a Near-RT Recursive Least Squares (RLS)-assisted xApp. By predicting millisecond-level delay risks, the xApp acts as a mathematically constrained safety net, applying bounded tactical adjustments when critical SLA violations are detected. Extensive evaluations across dynamic traffic transitions demonstrate that H-RLS maintains zero SLA violations. By actively preventing resource over-provisioning, the framework achieves the lowest composite Energy-SLA cost across all tested regimes, significantly minimizing dynamic power consumption while preserving Enhanced Mobile Broadband (eMBB) service integrity. Full article

Amid global economic uncertainty caused by the COVID-19 pandemic and geopolitical conflict, countries worldwide have prioritized urban economic resilience (UER). Using panel data for 283 Chinese cities from 2012 to 2023, this paper applies a difference-in-differences model to estimate the effect of the Pilot Battery Recycling Policy (PBRP) on UER. The results show that: (1) The estimated PBRP coefficient is positive at 0.004 units, equivalent to approximately 4.5% of the sample mean, and the result remains stable across a series of robustness and identification tests. (2) The mechanism and bootstrap mediation analyses provide evidence consistent with three potential city-level transmission channels associated with pilot designation: higher government technology spending, improved energy utilization, and greater total factor productivity. (3) The policy effect is positive and significant in upstream and downstream cities of the Yangtze River Basin, declining resource-based cities, and medium- and small-sized cities; it is negative in regenerative resource-based cities and megacities, while the remaining subgroup estimates are statistically insignificant. The subgroup patterns indicate that industrial foundations, adjustment costs, market capacity, and city scale may help shape policy effectiveness. (4) The Spatial Durbin Model identifies a positive direct effect on pilot cities and a negative indirect effect on neighboring cities under both selected spatial weight matrices. The matrix-specific admissibility and numerical checks confirm the validity of the spatial decomposition. Taken together, the findings point to a positive contribution of pilot designation to local economic resilience. The city-level results point to technological investment, energy-use efficiency, and productivity growth as potential transmission channels, while regional coordination can help manage cross-city competition for recycling resources and capacity. Full article

This study investigates the water–energy nexus in vineyards adopting innovative practices, focusing on water management through rainwater harvesting systems and the installation of photovoltaic panels for renewable energy production. The research focuses on two regions in Greece, Nemea in Corinthia and Nea Anchialos in Magnesia, using historical time series of meteorological data to establish water and energy balances. The study aims to examine the practical use of these technologies to improve water and energy efficiency in grape and wine production, which are important parts of the country’s primary sector. A daily water balance model is applied to estimate the required storage capacity of rainwater tanks for irrigation use in vine cultivation, using daily rainfall and evapotranspiration data over 20 hydrological years (2001/02–2020/21). Additionally, the installation of photovoltaic panels covering a specific percentage of the total utilized area in the study parcels is examined. The analysis showed that the use of a rainwater collection system with a catchment area of 500 m² for crop areas from 500 to 10,000 m² and using rainwater tanks from 10 to 200 m³ can ensure demand coverage rates from 60% to 95%. The production of green energy through the panels ranges from 149 to 156 MWh per year. Full article