Search Results (1,553)

Building- and residential-scale energy systems are becoming increasingly complex due to the growing use of renewable energy sources, variable generation profiles, and uncertainties in user demand. This study comprehensively examines the role of electrical, thermal, and hydrogen-based energy storage technologies in building-scale hybrid energy systems and evaluates these systems alongside energy management strategies. In this context, lithium-ion batteries, supercapacitors, flywheel systems, thermal energy storage solutions, and hydrogen-/fuel cell-based architectures are discussed in terms of their technical characteristics, intended uses, limitations, and complementary aspects. The reviewed studies show that individual storage technologies remain limited in their ability to meet all operational requirements, whereas hybrid storage architectures offer significant advantages in terms of power quality, energy flexibility, energy storage system lifetime, renewable energy utilization, and long-duration energy supply security. Furthermore, energy management systems are shown to be critical not only for cost minimization but also for user comfort, grid interaction, forecasting accuracy, uncertainty management, and the coordination of storage units operating at different timescales. Consequently, achieving high efficiency, low-carbon operation, and energy autonomy in building- and residential-scale systems requires the integrated design of multilayered hybrid storage approaches that are supported by intelligent energy management. Full article

Artificial intelligence (AI) is increasingly transforming tourism management by improving operational efficiency, enhancing visitor experiences, and supporting sustainability objectives. However, existing studies primarily focus on individual AI technologies and lack a comprehensive framework for comparing their sustainability performance. Therefore, this study evaluates and ranks major AI applications used in tourism management from a sustainability perspective. An integrated Entropy–TOPSIS framework was developed to assess six AI applications: Generative AI Systems, AI Chatbots, Digital Twin Systems, Smart Destination Management, Smart Governance, and Open Data Tourism Platforms. Ten evaluation criteria covering economic, environmental, social, managerial, and ethical dimensions were identified through a comprehensive literature review. The Entropy method was employed to determine objective criterion weights, while the TOPSIS method was used to rank the alternatives. The results indicate that personalization capability, privacy and ethical risk, and energy efficiency are the most discriminating evaluation criteria, with weights of 0.2217, 0.2190, and 0.2108, respectively. Smart Destination Management achieved the highest sustainability performance (CC = 0.5968), followed closely by Digital Twin Systems (CC = 0.5950) and Open Data Tourism Platforms (CC = 0.4978). The findings suggest that AI applications integrating sustainability-oriented destination management, operational intelligence, and personalized visitor services outperform solutions focusing on isolated technological functions. The proposed framework offers a practical decision support tool for tourism managers and policymakers seeking to prioritize AI investments that maximize sustainability outcomes while supporting responsible digital transformation. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

This research presents a physics-informed deep learning framework for investigating the magnetohydrodynamic flow of a dissipative non-Newtonian micropolar nanofluid induced by a stretching sheet, incorporating Stefan blowing, internal heat generation, and the zero nanoparticle mass flux condition. The physical model consists of the interplay between the microrotation dynamics, resistance of porosity on the microrotation, Brownian diffusion, and thermophoretic transport phenomenon. The numerical solutions for the nonlinear yielded equations that result from the above interaction are obtained by employing a PINN that considers the laws of physics and boundary conditions. With this technique, the flow behavior, temperature, concentration, and microrotation fields can be predicted accurately without requiring huge datasets. This shows the ability of PINNs to numerically treat highly-coupled nonlinear transport equations in a very efficient manner compared to other traditional methods. The important discoveries from this study include that the porous and magnetic factors increased the skin friction coefficient, but the magnetic effect and viscous dissipation decreased the rate of heat transfer, and the thermophoresis effect decreased the rate of mass transfer while the Brownian effect increased it. The precision of the PINN algorithm is confirmed by comparison of the results with the earlier findings, which proves very high accuracy and hence the robustness of the current computing framework. Results of this research are useful for the development of some thermal management systems, energy converters, cooling methods, chemical reaction processes, fuel cell technology, porous media reactors, and ocean engineering involving the transport of complicated non-Newtonian nanofluids. Full article

Automotive paint sludge (APS) is a hazardous industrial waste generated during automotive coating operations and is characterized by high moisture content, variable organic and inorganic composition, volatile organic compounds, pigments and heavy metals. Conventional disposal methods, including landfilling and direct incineration, present increasing environmental and regulatory challenges, thereby motivating interest in thermochemical conversion technologies for APS valorization and energy recovery. This review evaluates the current state of research on APS thermochemical conversion through incineration, pyrolysis and gasification pathways. The review compares the major operational characteristics of thermochemical pathways, including reactor conditions, temperature ranges, product yields, energy recovery potential, pollutant formation and downstream cleanup requirements. Also, techno-economic considerations such as drying energy demand and scale-up limitations are discussed. According to the current literature, incineration is the most industrially mature route for APS destruction, whereas pyrolysis offers more flexibility for fuel and material recovery. Gasification shows potential for syngas and hydrogen production but remains insufficiently studied for APS applications. Despite growing interest in APS valorization, a lot of research gaps remain regarding standardized feedstock classification, pilot-scale validation, process integration, environmental risk assessment and techno-economic optimization. Conclusively, future approaches towards managing APS would need to incorporate process optimization for specific APS types, incorporation of co-processing techniques, as well as an overall assessment for both environmental and economic feasibility. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article

Anaerobic digestion (AD) is an effective technology for sustainable waste management, renewable energy production and resource recovery within a circular economy. This study offers a systematic bibliometric review of global advances in AD research and assesses their relevance to Ghana. Using the PRISMA framework, the literature from 2011 to 2025 was sourced from the Scopus database and analysed through bibliometric and thematic methods. The review emphasises four key factors affecting AD performance: municipal solid waste as feedstock, pretreatment technologies, biochemical methane potential (BMP) assessment and process optimisation. Studies were included if they addressed any of these themes. Non-English publications, inaccessible full texts and papers lacking bibliographic metadata were excluded. After screening 3424 records, 61 studies were included in the systematic review. After metadata screening, 3374 of the 3424 retrieved records were retained for bibliometric analysis. Results show that municipal solid waste, food waste, agricultural residues and sewage sludge are promising sources for biogas generation. Pretreatment techniques, including thermal, chemical, mechanical and biological, significantly enhance substrate biodegradability and methane production. BMP assessment is a reliable way to gauge feedstock suitability and energy recovery potential. Optimising parameters like pH, temperature, organic loading, hydraulic retention time and co-digestion ratios improves process stability and biogas yield. The review highlights the increasing use of modelling and optimisation to boost digester performance and facilitate scale-up. In Ghana, abundant organic waste offers significant opportunities for biogas development. Employing advanced feedstock characterisation, pretreatment, BMP evaluation and optimisation can improve AD efficiency, support renewable energy, reduce waste disposal issues and promote Ghana’s shift toward a sustainable circular bioeconomy. Full article

This study presents the development of a combined heating system for a bioreactor designed to improve the efficiency of organic waste biodegradation. The proposed system maintains the required operating temperature within a specified range without relying on external energy sources, thereby enhancing the sustainability and energy efficiency of the bioconversion process. A bioreactor heating system based on a “water jacket” that is heated by the combustion of coal–water fuel has been developed. The “water jacket” is a system of two 15 mm diameter tubes located along a cylindrical axis inside the bioreactor. Heated liquid flows through the tubes, accelerating biomass fermentation processes. A technology for preparing and burning coal–water fuel using a radial circulation injection device is offered. Calculations are performed to determine the optimal temperature regime for the combustion process. Optimal conditions for electric pulse coal grinding (28 kV, 600 discharges) were established, the required particle size distribution of 50–250 µm was achieved, and the ignition temperature of the coal–water mixture (650 °C) was determined. The findings may contribute to improved waste management technologies and environmental sustainability by reducing carbon emissions and waste generation. Full article

Spent water-based drilling fluids generated during the construction of technological wells impose substantial environmental, water-management, transportation, and energy burdens. Conventional practices, including storage in temporary pits, prolonged settling, and off-site disposal, do not enable process-water recovery and require repeated handling of suspensions with a high solids content. This study evaluates a pressure-driven cylindrical hydrodynamic disperser as the central component of a compact on-site treatment system. Unlike conventional mechanical mixers, the disperser contains no driven shaft within the active chamber. Particle–reagent contact is intensified through controlled jet shear, vortex-induced redistribution, and the motion of freely moving steel balls. Field-derived drilling fluids containing 30–40 wt.% solids, with densities of 1.12–1.17 g/cm³, pH values of 7.4–8.2, and median particle sizes of 15–50 μm, were treated at velocity gradients of 500–1500 s⁻¹ for 60–180 s using Superfloc N-300 dosages of 0–100 g/t. The optimal operating conditions were G = 1300 s⁻¹, τ = 150 s, and D = 50 g/t. Under these conditions, the separation efficiency reached 91–93%, the residual suspended-solids concentration decreased to 120–130 mg/L, process-water recovery reached 80%, sludge volume decreased by 40–60%, and specific energy consumption was approximately 0.30 kWh/m³. More intensive treatment increased the separation efficiency to 94–95% but resulted in a less favorable balance among energy consumption, reagent dosage, and resource recovery. Compared with mechanical mixing, the selected treatment system reduced flocculant consumption by 37.5%, treatment time by more than threefold, and specific energy consumption by 40%. These results support the use of modular on-site systems for process-water recirculation and reduced sludge-transport requirements at remote drilling sites. Full article

Global growth in electric mobility, portable electronics, and renewable energy storage has increased concerns about the environmental and economic impacts of managing end-of-life lithium-ion and lead-acid batteries. Although these batteries support the transition to renewable energy, their disposal presents significant challenges. Recycling has emerged as a key strategy to reduce resource depletion, limit pollution, and recover valuable materials. This study systematically reviewed and quantitatively synthesised the literature published between 2000 and 2025, assessing the environmental impacts of battery recycling programs. The review followed PRISMA guidelines to ensure a transparent and rigorous study selection process. Data from peer-reviewed articles, industry reports, and policy documents were analysed, focusing on indicators such as greenhouse gas emissions, energy use, material recovery efficiency, and economic returns. Statistical methods, including Hedges’ g, heterogeneity testing, and sensitivity analysis within a random-effects model, were applied to account for variability across technologies and battery types. The results show that recycling generally lowers emissions and improves resource recovery compared to virgin material extraction, though performance varies. Lead-acid recycling demonstrates stronger environmental benefits due to mature technologies and established systems, while lithium-ion recycling shows positive but lower gains, limited by higher energy demands and less-developed processes. Overall, recycling is essential for reducing environmental impacts and supporting a circular economy, though lithium-ion systems require further technological and policy advancements. These findings can be used by governments to strengthen regulatory frameworks to support recycling industries and invest in advanced lithium-ion recycling technologies to improve efficiency. Despite the existing limitations, the benefits of recycling outweigh the drawbacks, making it a necessary strategy for sustainable battery waste management. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

Smart public lighting systems are increasingly evolving from energy-efficiency technologies into distributed urban data infrastructures. This study examines an IoT-based smart public lighting pilot implemented on a university campus in southern Quito, Ecuador, with the objective of quantifying its informational capacity, storage requirements, and scalability potential. The analysis was based on 11 smart luminaires and 49 days of real operational data. Measurements were recorded every 10 min, considering 10 variables per record, including measured consumption, reference consumption, active power, accumulated energy, operating hours, operational status, events, faults or alarms, energy savings, and avoided CO₂ emissions. The results show that each luminaire can generate 1440 data values per day. Under the same acquisition configuration, a network of 10,000 luminaires would generate approximately 5.256 billion data values per year, while a network of 100,000 luminaires would generate approximately 52.56 billion data values per year, requiring an estimated 1051.2 GB/year of storage. These values are interpreted as scalability scenarios rather than definitive city-wide predictions, given the limited scale and observation period of the pilot. As a methodological contribution, the study proposes the Smart Lighting Informational Capacity Index (SLICI) to estimate the daily data intensity generated by smart lighting networks. The findings demonstrate that the expansion of smart public lighting requires not only efficient luminaires, but also robust digital architectures for data storage, processing, interoperability, cybersecurity, and analytics. The study positions smart public lighting as a strategic platform for urban energy management, predictive maintenance, and data-driven sustainable planning, while highlighting the need for future validation over longer periods and across different urban typologies. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

The extensive deployment of renewable energy sources (RES) across modern energy infrastructure has introduced significant operational complexity, necessitating the development of advanced data-driven control strategies to ensure reliable and efficient system operation. Among these approaches, reinforcement learning (RL) has emerged as a promising paradigm for managing renewable generation and coordinating interconnected energy subsystems under uncertainty and dynamic operating conditions. The current paper presents a comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems. The paper begins by outlining the fundamental characteristics of these smart grid energy environments along with the mathematical foundations of RL and its principal algorithmic families. A structured analysis of recent peer-reviewed studies is then conducted, with the literature systematically categorized according to the corresponding energy domain. A high number of impactful selected studies are further examined across multiple key dimensions, including RL methodologies, agent architectures, reward design, baseline control strategies, RES-integrated technologies, and control objectives. Based on this multi-dimensional evaluation, the review identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications. Finally, the observations are critically discussed and future research directions are outlined towards the development of scalable, practical, and reliable RL-based energy management solutions for next-generation smart grid systems. Full article

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article