Search Results (12,893)

Wind turbines play an important role for renewable energy generation related to sustainable development. Selection of a suitable blade shape is a key factor in wind turbine design, especially in low wind speed conditions such as urban areas. In addition, two airfoil models of the S-series, S4110 and S1012, are often selected based on their suitable aerodynamic properties with low Reynolds numbers, high applicability, and stable performance. However, there is no research design for wind turbine blades based on S4110 and S1012 under low wind conditions in countries around the world. The angle of attack was adjusted to observe variations in the key aerodynamic parameters while applying appropriate boundary conditions for different regions. The study results show that the overall performance of the optimized S4110 is better than that of the optimized S1012, particularly at larger angles of attack. The performance of the airfoil S4110 shows a strong improvement after optimization, with the aerodynamic performance from 17.35 at 3 m/s to 50.78 at 5 m/s. This paper proposed the airfoil combination usage of S4110 at the blade tip and S1012 at the blade root to form an optimal hybrid airfoil configuration for wind turbine blade, which can both take advantage of high aerodynamic efficiency in low wind conditions and ensure the necessary mechanical strength and stability for the entire wind turbine blade. The performance of the proposed small wind turbine blade model based on the optimal S4110 and S1012 airfoils was analyzed using the Qblade program. Its purpose is to create a new blade model for small wind turbines that moves beyond conventional applications to explore novel and integrated solutions for a sustainable energy future. Full article

In the context of the zero-carbon transition, this article provides a comprehensive review of Organic Rankine Cycle (ORC) technologies for low-grade heat recovery and conversion to power. It surveys a wide range of renewable and waste heat sources—including geothermal, solar thermal, biomass, internal combustion engine exhaust, and industrial process heat—and discusses the integration of ORC systems to enhance energy recovery and thermal efficiency. The analysis examines various configurations, from basic and regenerative cycles to advanced transcritical and supercritical designs, cascaded systems, and multi-source integration, evaluating their thermodynamic performance for different heat source profiles. A critical focus is placed on working fluid selection, where the landscape is being reshaped by stringent regulatory frameworks such as the EU F-Gas regulation, driving a shift towards low-GWP hydrofluoroolefins, natural refrigerants, and tailored zeotropic mixtures. The review benchmarks ORC against competing technologies such as the Kalina cycle, Stirling engines, and thermoelectric generators, highlighting relative performance characteristics. Furthermore, it identifies key trends, including the move beyond single-source applications toward integrated hybrid systems and the use of multi-objective optimization to balance thermodynamic, economic, and environmental criteria, despite persistent challenges related to computational cost and real-time control. Key findings confirm that ORC systems significantly improve low-grade heat utilization and overall thermal efficiency, positioning them as vital components for integrated zero-carbon power plants. The study concludes that synergistically optimizing ORC design, refrigerant choice in line with regulations, and system integration strategies is crucial for maximizing energy recovery and supporting the broader zero-carbon energy transition. Full article

Photoelectrochemical water splitting (PEC-WS) provides a sustainable route to transform solar energy into hydrogen; however, its overall efficiency is constrained by the inherently slow kinetics of the oxygen evolution reaction. Bismuth vanadate (BiVO₄) is considered an attractive visible-light-responsive photoanode due to its suitable band gap (~2.4 eV) and chemical stability; however, its efficiency is restricted by limited charge transport and significant charge carrier recombination. To overcome these limitations, BiVO₄–MoS₂ (BVO–MS) heterostructures were synthesized through a simple in situ hydrothermal approach, ensuring robust interfacial coupling and uniform dispersion of MS nanosheets over BVO dendritic surfaces. This intimate contact promotes rapid charge transfer and improved light-harvesting capability. Structural and spectroscopic analyses confirmed the formation of monoclinic BVO with uniformly integrated amorphous MS. The optimized BVO–MS10 electrode delivered a photocurrent density of 4.72 mA cm⁻² at 0.6 V vs. SCE, approximately 5.3 times higher than pristine BVO, and achieved an applied bias photon-to-current efficiency of 0.49%. Mott–Schottky analysis revealed a distinct negative shift in the flat-band potential for BVO–MS10, indicative of an upward movement of its conduction band and the establishment of a strong internal electric field that enhances charge separation and interfacial electron transport. These synergistic effects collectively endow the in situ engineered BVO–MS heterostructure with superior PEC water oxidation performance and highlight its promise for efficient solar-driven hydrogen generation. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such as thermal runaway. Liquid cooling has become the dominant strategy in commercial EVs due to its superior thermal performance over air cooling. However, optimizing liquid cooling systems remains challenging due to the trade-off between heat transfer efficiency and pressure drop. Recent studies have explored various coolant selection, nanofluid enhancements, and complex channel geometries, an ideal balance remains difficult to achieve. While advanced methods such as topology optimization offer promising performance gains, they often introduce significant modeling and manufacturing complexity. In this study, we propose a practical alternative: an interconnected straight-channel cooling plate that introduces lateral passages to disrupt the thermal boundary layer and enhance mixing. Comparative analysis shows that the design improves temperature uniformity and reduces peak battery temperature, all while maintaining a moderate pressure drop. The proposed configuration offers a scalable and effective solution for next-generation BTMS, particularly in EV applications where thermal performance and manufacturability are both critical. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

High-valent metal species (iron, manganese, cobalt, copper, and ruthenium) based advanced oxidation processes (AOPs) have emerged as sustainable technologies for water remediation. These processes offer high selectivity, electron transfer efficiency, and compatibility with circular chemistry principles compared to conventional systems. This comprehensive review discusses recent advances in the synthesis, stabilization, and catalytic applications of high-valent metals in aqueous environments. This study highlights their dual functionality, not only as conventional oxidants but also as mechanistic mediators within redox cycles that underpin next-generation AOPs. In this review, the formation mechanisms of these species in various oxidant systems are critically evaluated, highlighting the significance of ligand design, supramolecular confinement, and single-atom engineering in enhancing their stability. The integration of high-valent metal-based AOPs into photocatalysis, sonocatalysis, and electrochemical regeneration is explored through a newly proposed classification framework, highlighting their potential in the development of energy efficient hybrid systems. In addition, this work addresses the critical yet underexplored area of environmental fate, elucidating the post-oxidation transformation pathways of high-valent species, with particular attention to their implications for metal recovery and nutrient valorization. This review highlights the potential of high-valent metal-based AOPs as a promising approach for zero wastewater treatment within circular economies. Future frontiers, including bioinspired catalyst design, machine learning-guided optimization, and closed loop reactor engineering, will bridge the gap between laboratory research and real-world applications. Full article

This paper introduces a novel quantum-inspired algorithm for numerical multiobjective optimization, uniquely integrating the multilevel structure of qudits with principles of controlled thermonuclear fusion. Moving beyond conventional qubit-based approaches, the algorithm leverages the qudit’s higher-dimensional state space to enhance search capabilities. Fusion-inspired dynamics—modeling particle interaction, energy release, and plasma cooling—provide a powerful metaheuristic framework for navigating complex, high-dimensional Pareto fronts. A hybrid quantum-classical version of the algorithm is presented, designed to exploit the complementary strengths of both computational paradigms for improved efficiency in solving dynamic multiobjective problems. Experimental evaluation on standard dynamic multiobjective benchmarks demonstrates clear performance advantages. Both the quantum-inspired and hybrid variants consistently outperform leading classical algorithms such as NSGA-III, MOEA/D and GDE3, as well as the quantum-inspired NSGA-III, in key metrics: identifying a greater number of unique non-dominated solutions, ensuring superior uniformity along the Pareto front, maintaining stable convergence across generations, and achieving higher accuracy in approximating the ideal solution. Full article

This review critically examines photoresponsive supercapacitors based on TiO₂/graphene hybrids, with a particular focus on their emerging dual role as energy-storage devices and environmental sensors. We first provide a concise overview of the electronic structure of TiO₂ and the key attributes of graphene and related nanocarbons that enable efficient charge separation, transport, and interfacial engineering. We then summarize and compare reported device architectures and electrode designs, highlighting how morphology, graphene integration strategies, and illumination conditions govern specific capacitance, cycling stability, rate capability, and light-induced enhancement in performance. Particular attention is given to the underlying mechanisms of photo-induced capacitance enhancement—including photocarrier generation, interfacial polarization, and photodoping—and to how these processes can be exploited to embed sensing functionality in working supercapacitors. We review representative studies in which TiO₂/graphene systems operate as capacitive sensors for humidity, gases, and volatile organic compounds, emphasizing quantitative figures of merit such as sensitivity, response/recovery times, and stability under repeated cycling. Finally, we outline current challenges in materials integration, device reliability, and benchmarking, and propose future research directions toward scalable, multifunctional TiO₂/graphene platforms for self-powered and environmentally aware electronics. This work is intended as a state-of-the-art summary and critical guide for researchers developing next-generation photoresponsive supercapacitors with integrated sensing capability. Full article

Traditionally, the dynamics of power systems have been governed by synchronous generators and their associated rotating masses. However, with the increasing penetration of renewable generation and power electronic interfaces, the inertia contributed by rotating machines has been gradually displaced. This makes it imperative to study alternative elements capable of mitigating the reduction in inertia in modern power systems. This article addresses the problem of optimal sizing and placement of Battery Energy Storage Systems to enhance frequency response in power grids through the application of optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Several inertia scenarios are analyzed, where the algorithms determine the optimal locations for Battery Energy Storage Systems units while minimizing the total installed Battery Energy Storage Systems capacity. As key contributions, this study models Battery Energy Storage Systems units, which emulate inertial responses based on the system’s Rate of Change of Frequency, and evaluates the impact of Battery Energy Storage Systems on frequency stability by analyzing parameters such as the frequency nadir, zenith, and steady-state frequency according to the installed Battery Energy Storage System’s size and location. A comparative analysis of the optimization scenarios shows that the Particle Swarm Optimization algorithm with 50% rotational inertia is the most efficient, requiring the lowest total installed power (277.11 MW). It is followed by the Particle Swarm Optimization algorithm with 100% rotational inertia (285.79 MW) and Genetic Algorithms with 50% rotational inertia (285.57 MW). In contrast, Genetic Algorithms with 25% rotational inertia demand the highest total installed Battery Energy Storage Systems power (307.44 MW), a result directly associated with a significant reduction in system inertia. Overall, an inverse relationship is observed between the available inertia level and the required Battery Energy Storage Systems capacity: the lower the inertia, the greater the power that the Battery Energy Storage Systems must supply to keep the system frequency within acceptable operational limits. Full article

Microbial electrolysis cells (MECs) are bioreactors that utilize electroactive microorganisms to catalyze the oxidation of organic substrates in wastewater, generating electron flow for hydrogen production. Despite the concept, a persistent performance gap exists where metabolically active anodic biofilms frequently fail to achieve expected current densities by the flow of electrons to produce hydrogen. This review examines the multiple causes that lead to the disconnect between robust biofilm development, electron transfer, and hydrogen production. Factors affecting biofilm generation (formation, substrate selection, thickness, conductivity, and heterogeneity) are discussed. Moreover, factors affecting electron transfer (electrode configuration, mass transfer constraints, key electroactive species, and metabolic pathways) are discussed. Also, substrate diffusion limitations, proton accumulation causing inhibitory pH gradients in stratified biofilms, elevated internal resistance, electron diversion to competing processes like hydrogenotrophic methanogenesis consuming H₂, and detrimental biofilm aging, impacting hydrogen production, are studied. The critical roles of electrode materials, reactor configuration, and biofilm electroactivity are analyzed, emphasizing advanced electrochemical (CV, EIS, LSV), imaging (CLSM, SEM, AFM), and omics (metagenomics, transcriptomics, proteomics) techniques essential for diagnosing bottlenecks. Strategies to enhance extracellular electron transfer (EET) (advanced nanomaterials, redox mediators, conductive polymers, bioaugmentation, and pulsed electrical operation) are evaluated for bridging this performance gap and improving energy recovery. The review presents an integrated framework connecting biofilm electroactivity, EET kinetics, and hydrogen evolution efficiency. It highlights that conventional biofilm metrics may not reflect actual electron flow. Combining electrochemical, microelectrode, and omics insights allows precise evaluation of EET efficiency and supports sustainable MEC optimization for enhanced hydrogen generation. Full article

Multiband solar cells offer a promising route to surpass the Shockley-Queisser limit by harnessing sub-bandgap photons through three active energy band transitions. However, realizing their full potential requires overcoming key challenges in material design and device architecture. Here, we propose a novel multiband stacked anti-parallel junction solar cell structure based on highly mismatched alloys (HMAs), in particular dilute GaAsN with ~1–4% N. An anti-parallel junction consists of two semiconductor junctions connected with opposite polarity, enabling bidirectional current control. The structures of the proposed devices are based on dilute GaAsN with anti-parallel junctions, which allow the elimination of tunneling junctions—a critical yet complex component in conventional multijunction solar cells. Semiconductors with three active energy bands have demonstrated the unique properties of carrier transport through the stacked anti-parallel junctions via tunnel currents. By leveraging highly mismatched alloys with tailored electronic properties, our design enables bidirectional carrier generation through forward- and reverse-biased diodes in series, significantly enhancing photocurrent extraction. Through detailed SCAPS-1D simulations, we demonstrate that strategically placed blocking layers prevent carrier recombination at contacts while preserving the three regions of photon absorption in a single multiband semiconductor p/n junction. Remarkably, our optimized five-stacked anti-parallel junctions structure achieves a maximum theoretical conversion efficiency of 70% under 100 suns illumination, rivaling the performance of state-of-the-art six-junctions III-V solar cells—but without the fabrication complexity of multijunction solar cells associated with tunnel junctions. This work establishes that highly mismatched alloys are a viable platform for high efficiency solar cells with simplified structures. Full article

Resource-based cities generally have large carbon-emission, and their carbon balance status is receiving more attention. Land use is a key factor in regulating regional carbon balance. To explore the relationship between land use patterns and carbon balance in resource-based cities, we selected nine cities in Anhui, a major energy province, as the research object. Based on the land use data (2000–2020) and the carbon emission coefficient method, we calculated the carbon emissions, carbon sequestration, and net carbon emissions to show their spatiotemporal evolution. The Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving factors of carbon emissions. The results indicated the following: (1) Net carbon emissions increased by 149.60%, and the growth rate had slowed down since 2015. Forestland constituted the primary carbon sink, whereas cropland was the dominant carbon source. The spatial distribution of carbon emissions and carbon sequestration was uneven. (2) The economic development level and energy consumption density were the principal factors of emission increases. Conversely, carbon emission intensity and land use economic efficiency served as the key mitigating factors. Full article

The main objective of this article is to model, simulate, and analyze the interaction of energy storage systems with BIPV installations. Currently, due to the instability of energy generation, the economic challenges of integrating PV installations into the electricity grid, and the desire to increase self-consumption, energy storage facilities are becoming increasingly popular. Subsidy programs most often favor PV installations, including BIPV, that work with energy storage devices. Therefore, there is a justified need to model energy storage devices for use with BIPV. The article describes the rationale for the benefits of using energy storage systems within current billing models, using Poland as an example. The introduction also provides an overview of the most popular energy storage technologies compatible with renewable energy installations. To achieve these objectives, appropriate system solutions were designed in the MATLAB environment and used to perform simulations, taking into account variable energy demand. An economic analysis of the system’s operation was conducted using a prosumer net-billing model, and adjustments were made to the system configuration. It has been shown that the use of appropriate energy storage solutions, cooperating with photovoltaic installations, allows for increased self-consumption and more efficient management of electricity obtained in BIPV, which has a positive impact on the payback time and economic profits. The analysis method used and the results obtained are true for the assumed known load profile; however, the method can be successfully applied to various load profiles. Full article

The integration of artificial intelligence (AI) into intelligent control systems has advanced significantly, enabling improved adaptability, robustness, and performance in nonlinear and uncertain environments. This study conducts a PRISMA-2020-compliant systematic mapping of 188 peer-reviewed articles published between 2000 and 15 January 2025, identified through fully documented Boolean queries across IEEE Xplore, ScienceDirect, SpringerLink, Wiley, and Google Scholar. The screening process applied predefined inclusion–exclusion criteria, deduplication rules, and dual independent review, yielding an inter-rater agreement of κ = 0.87. The resulting synthesis reveals three dominant research directions: (i) control model strategies (36.2%), (ii) parameter optimization methods (45.2%), and (iii) adaptability mechanisms (18.6%). The most frequently adopted approaches include fuzzy logic structures, hybrid neuro-fuzzy controllers, artificial neural networks, evolutionary and swarm-based metaheuristics, model predictive control, and emerging deep reinforcement learning frameworks. Although many studies report enhanced accuracy, disturbance rejection, and energy efficiency, the analysis identifies persistent limitations, including overreliance on simulations, inconsistent reporting of hyperparameters, limited real-world validation, and heterogeneous evaluation criteria. This review consolidates current AI-enabled control technologies, compares methodological trade-offs, and highlights application-specific outcomes across renewable energy, robotics, agriculture, and industrial processes. It also delineates key research gaps related to reproducibility, scalability, computational constraints, and the need for standardized experimental benchmarks. The results aim to provide a rigorous and reproducible foundation for guiding future research and the development of next-generation intelligent control systems. Full article