Search Results (1,250)

Desalination plays a critical role in addressing global water scarcity, yet brine disposal remains a significant environmental challenge. This study evaluates a minimal liquid discharge (MLD) membrane-based system integrating high-pressure reverse osmosis (HPRO) and membrane distillation (MD) for brine treatment, with a focus on the Eastern Mediterranean. A techno-economic assessment (TEA) was conducted to analyze the system’s feasibility, water recovery performance, energy consumption, and cost-effectiveness. The results indicate that the hybrid HPRO-MD system achieves a high water recovery rate of 78.65%, with 39.65 m³/day recovered from MD and 39 m³/day from HPRO. The specific energy consumption is 23.2 kWh/m³, with MD accounting for 89% of the demand. The system’s cost is USD 0.99/m³, generating daily revenues of USD 228 in Cyprus and USD 157 in Greece. Compared to conventional brine disposal methods, MLD proves more cost-effective, particularly when considering evaporation ponds. While MLD offers a sustainable alternative for brine management, challenges remain regarding energy consumption and the disposal of concentrated waste streams. Future research should focus on renewable energy integration, advanced membrane technologies, and resource recovery through brine mining. The findings highlight the HPRO-MD MLD system as a promising approach for sustainable desalination and circular water resource management. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

Nuclear energy has undergone a significant transformation over the past decades, driven by technological innovation, shifting safety priorities, and the urgent need to mitigate climate change. This study presents a comprehensive review of the historical evolution, current developments, and future prospects of nuclear energy as a strategic low-carbon resource. A structured literature review was conducted following Kitchenham’s methodology, covering peer-reviewed articles and institutional reports from 2000 to 2025. Key advances examined include the deployment of Small Modular Reactors, Generation IV technologies, and fusion systems, along with progress in safety protocols, waste management, and regulatory frameworks. Comparative environmental data confirm nuclear power’s low life-cycle CO₂ emissions and high energy density relative to other generation sources. However, major challenges remain, including high capital costs, long construction times, complex waste disposal, and issues of public acceptance. The analysis underscores that nuclear energy, while not a standalone solution, is a critical component of a diversified and sustainable energy mix. Its successful integration will depend on adaptive governance, international cooperation, and enhanced social engagement. Overall, the findings support the role of nuclear energy in achieving global decarbonization targets, provided that safety, equity, and environmental responsibility are upheld. Full article

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance. Full article

With the gradual penetration of new energy generation/storage, accurate and reliable load forecasting (LF) plays an increasingly important role in different energy management applications (e.g., power resource allocation, peak demand response, energy supply and demand optimization). In recent years, data-driven and artificial intelligence (AI) technologies have received considerable attention in the field of LF. This study provides a comprehensive review on the existing advanced AI and data-driven techniques used for LF tasks. First, the reviewed studies are classified from the load’s spatial scale and forecasting time scale, and the research gap that this study aims to fill in the existing reviews is revealed. It was found that short-term forecasting dominates in the time scale (accounting for about 83.1%). Second, based on the summary of basic preprocessing methods, some advanced preprocessing methods are presented and analyzed. These advanced methods have greatly increased complexity compared with basic methods, while they can bring significant performance improvements such as adaptability and accuracy. Then, various LF models using the latest AI techniques, including deep learning, reinforcement learning, transfer learning, and ensemble learning, are reviewed and analyzed. These models are also summarized from several aspects, such as computational cost, interpretability, application scenarios, and so on. Finally, from the perspectives of data, techniques, and operations, a detailed discussion is given on some challenges and opportunities for LF. Full article

Against the backdrop of intensifying global climate change and advancing the goal of the “dual-carbon” strategy, the built environment is being viewed as a complex socio-technical system in which technological, economic, demographic and institutional subsystems are coupled and evolving at different scales. As a core node in this system, residential buildings not only carry infrastructural functions, but are also deeply embedded in energy flows, material cycles and behavioural structures, which have a significant impact on carbon emissions. Given the high volume of residential buildings in China and the significant differences between urban and rural construction, there is an urgent need to systematically identify and analyse the implicit carbon emissions during the materialisation phase. In this paper, from the perspective of systems engineering, we selected 30 urban and rural residential buildings in provinces and cities from 2005 to 2020 as the research objects, adopted the life cycle assessment (LCA) method to account for the implied carbon emissions in the materialisation stage, and systematically identified the driving factors of carbon emissions based on the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. From this study, we made the following conclusions: (1) the total carbon emissions of residential buildings in urban and rural areas in China continue to rise during the materialisation stage, showing a spatial pattern of “high in the south-east and low in the north-west”, with a significant trend of structural transformation in urban and rural areas and with steel–concrete structures dominating in towns and cities, and bricks and steel being used in rural areas. (2) Resident population and disposable income are generally positive driving factors, while the influence of industrial structure and energy intensity is heterogeneous between urban and rural areas. For overall residential buildings, every 1% increase in resident population and income will lead to a 1.055% and 0.73% increase in carbon emissions, respectively. The study shows that life-cycle-oriented carbon accounting and the identification of multidimensional driving mechanisms are of great policy value in developing urban–rural differentiated emission reduction paths and enhancing the effectiveness of carbon management in the building sector. Full article

Smart Grid, integrating modern information and communication technologies with traditional power infrastructure, is already widely used in many countries around the world. Its domain is generating large amounts of energy and, at the same time, measuring data from various sources, especially Renewable Energy Sources. Acquiring measurement data from generators and power receivers requires appropriate infrastructure and tools. An even greater challenge is the effective processing of measurement data in order to obtain information helpful in energy management in Smart Grid. The article will present an effective method of acquiring and processing measurement data from a photovoltaic system with a peak power of 50 kWp supplying the administrative building of the university. Unsupervised clustering will be used to create signatures of both generated and consumed power. Analysis of the relationships between measured network parameters in the three-state space allows for a quick determination of the power generated by the photovoltaic system and the power needed to power the building. The applied approach can have a wide practical application, both in Energy Management in institutional buildings. It can also be successfully used to train AI algorithms to categorize operating states in Smart Grid. The traditional and AI-assisted algorithms used by the authors are used to obtain practical information about the operation of Smart Grid. Such expert-validated knowledge is highly desirable in Advanced Process Control, which aims to optimize processes in real time. Full article

The electric arc furnace (EAF) is a key technology in the steel production industry, particularly for recycling scrap iron. It plays a crucial role in the shift to low-carbon metallurgy, responding to the growing demand for more sustainable production methods. Alongside its environmental and energy benefits, the EAF process generates significant amounts of solid by-products, including dust (EAFD) and slag (EAFS). These wastes are not only rich in base metals but also contain critical elements, which have attracted increasing scientific and industrial interest. Depending on the waste type, key metals such as zinc (from EAFD) and chromium, vanadium, and titanium (from EAFS) are targeted for recovery. This review examines the chemical and phase compositions of these wastes, various leaching techniques (often combined with pretreatment stages), and methods for final metal recovery, either in their pure form or as compounds. Key challenges in hydrometallurgical routes include chloride contamination, the dissolution of refractory zinc ferrite, and impurity management. Despite current limited industrial adoption, hydrometallurgical approaches show significant promise as efficient and environmentally friendly solutions for resource recycling, offering high-purity metal recovery. Full article

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. Accurate state-of-charge estimation is essential for effective energy management in power systems employing SC banks. This work presents a novel state estimation approach for SC banks. First, a dynamic model of an SC bank is derived by applying a fractional-order Thévenin equivalent circuit to a single-cell SC. Then, an observability analysis is conducted, which reveals that the system is empirically weakly observable. This is the fundamental challenge for state-of-the-art observers to robustly perform state estimation. To address this challenge, an implicitly regularized observer is developed based on generalized parameter estimation techniques. The performance of the proposed observer is benchmarked against a fractional-order extended Kalman filter using experimental data. The results demonstrate that incorporating a regularization law into the observer dynamics effectively mitigates observability limitations, offering a robust solution for the SC bank state estimation. Full article

The global pursuit of a sustainable and decarbonized energy landscape requires the development of novel materials capable of supporting lightweight construction, advanced energy conversion, storage, and thermal management technologies. Among these, metal foams have emerged as a versatile class of porous materials, offering a unique combination of low density, high surface area, three-dimensional (3D) interconnected porosity, and favorable thermal and electrical conductivities. These attributes make them highly suitable for a broad range of applications critical to the ongoing energy transition, assuming an increasingly central role in enabling clean, efficient, and resilient energy infrastructures. From this key perspective, the present review highlights the relevance of the adoption of metal foams in several fields crucial for the energy transition. By presenting methodologies and outcomes of research results, mainly from the last five years, the paper underscores the potential of low-weight, high-surface, and high-performance porous materials in contemporary and future industry, supporting sustainable development and, more generally, energy transition and circular economy. The approach also aims to minimize negative impacts and promote sustainability, for example, by recycling and transforming waste materials. Full article

The integration of renewable energy resources (RES) into microgrids (MGs) poses significant challenges due to the intermittent nature of generation and the increasing complexity of multi-energy scheduling. To enhance operational flexibility and reliability, this paper proposes an intelligent energy management system (EMS) for MGs incorporating a hybrid hydrogen-battery energy storage system (HHB-ESS). The system model jointly considers the complementary characteristics of short-term and long-term storage technologies. Three conflicting objectives are defined: economic cost (EC), system response stability, and battery life loss (BLO). To address the challenges of multi-objective trade-offs and heterogeneous storage coordination, a novel deep-reinforcement-learning (DRL) algorithm, termed MOATD3, is developed based on a dynamic reward adjustment mechanism (DRAM). Simulation results under various operational scenarios demonstrate that the proposed method significantly outperforms baseline methods, achieving a maximum improvement of 31.4% in SRS and a reduction of 46.7% in BLO. Full article