Search Results (3,049)

This study investigates the use of the complex reductant FeAlSiCa as an alternative to the conventional FeSiCr in the EAF smelting of FeCr. The smelting process using FeAlSiCa is characterized by a stable furnace operation, active discharge of metal and slag, and effective phase separation. It was found that a 20% excess of FeAlSiCa over the stoichiometric requirement leads to a sharp increase in Si content in the FeCr alloy, with approximately 85% Cr recovery into the metal. A stoichiometric amount of FeAlSiCa results in a metal with 1.5–1.6% Si content and about 80% Cr recovery. A comparable Cr recovery using FeSiCr was achieved only when applying a 20% excess of that reductant. The use of FeAlSiCa also holds promise for technological sustainability due to its low production cost and the utilization of waste materials during its synthesis. The resulting slags are solid and rock-like and show no signs of disintegration after storage for more than 45 days. Full article

Mine Thermal Energy Storage (MTES) offers a promising solution for sustainable heating by repurposing abandoned, water-filled mines as underground thermal reservoirs. This study assesses the feasibility of implementing MTES in Nova Scotia, with a focus on the Sydney coalfield region, particularly Glace Bay. The research combines geological analysis, residential heat demand estimation, thermal storage capacity estimation, and cost–benefit evaluation to determine whether abandoned coal mines can support district heating applications. Results show that MTES can deliver substantial heating cost reductions compared to oil-based systems, while significantly lowering greenhouse gas emissions. The study also explores the integration of MTES with local renewable energy sources, including wind and solar, to enhance energy system flexibility and reliability. International case studies from Springhill (Canada), Heerlen (Netherlands), and Bochum (Germany) are referenced to contextualize the analysis and demonstrate how the findings of this study align with broader MTES scalability, performance, and operational challenges. Key technical barriers, such as water quality management, infrastructure investment, and seasonal variability in heat demand, are discussed. Overall, the findings highlight MTES as a viable and sustainable energy storage approach for Nova Scotia and other regions with legacy mining infrastructure. Full article

To reduce the high computational cost and lengthy design cycles of traditional solid rocket motor (SRM) development, this paper proposes an efficient surrogate-assisted multi-objective optimization approach. A comprehensive performance model was first established, integrating internal ballistics, grain structural integrity, and cost estimation, to enable holistic assessment of the coupled effects of key motor components. A parametric analysis framework was then developed to automate the model, facilitating seamless data exchange and coordination among sub-models through chain coupling. Leveraging this framework, a large-scale, high-fidelity dataset was generated via uniform sampling of the design space. The Kriging surrogate model with the highest global fitting accuracy was subsequently employed to replicate the integrated model’s complex responses and reveal underlying design principles. Finally, an enhanced NSGA-III algorithm incorporating a phased hybrid crossover operator was applied to improve global search performance and guide solution evolution along the Pareto front. Applied to a specific SRM, the proposed method achieved a 4.72% increase in total impulse and a 6.73% reduction in cost compared with the initial design, while satisfying all constraints. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

To enhance spatial resource complementarity and cross-entity coordination among multi-regional integrated energy systems (MRIESs), an optimized operation strategy is developed based on a bilevel Stackelberg game framework. In this framework, the integrated energy system operator (IESO) and MRIES act as the leader and followers, respectively. Guided by an integrated demand response (IDR) mechanism and a collaborative green certificate and carbon emission trading (GC–CET) scheme, energy prices and consumption strategies are optimized through iterative game interactions. Inter-regional electricity transaction prices and volumes are modeled as coupling variables. The solution is obtained using a hybrid algorithm combining particle swarm optimization (PSO) with mixed-integer programming (MIP). Simulation results indicate that the proposed strategy effectively enhances energy complementarity and optimizes consumption structures across regions. It also balances the interests of the IESO and MRIES, reducing operating costs by 9.97%, 27.7%, and 4.87% in the respective regions. Moreover, in the case study, renewable energy utilization rates in different regions—including an urban residential zone, a renewable-rich suburban area, and an industrial zone—are improved significantly, with Region 2 increasing from 95.06% and Region 3 from 77.47% to full consumption (100%), contributing to notable reductions in carbon emissions. Full article

This paper estimates potential changes in the total system cost (TSC) of decarbonization of two regional transmission organizations (RTOs) in the United States (U.S.)—Midcontinent Independent System Operator-North (MISO-N) and Southwest Power Pool (SPP) RTO West. In particular, the study serves to highlight potential differences in technology costs between two decarbonization pathways at carbon reduction rates close to 100% (relative to 2019 levels) while maintaining system reliability. In Pathway A, decarbonization is achieved by replacing fossil energy (FE)-fired thermal power plants with variable renewable energy (VRE) technologies coupled with energy storage (ES). Pathway B considers retrofitting fossil fuel-fired units with carbon capture and storage (CCS) and the addition of VRE and ES. The results show that including CCS technologies in the path to decarbonization has a significant benefit from a system cost perspective. When summing up all system costs and avoided emissions over 30 years of operation of the decarbonized systems, the pathway that includes CCS is significantly more cost-effective. TSCs for MISO-N are at least USD 1279 billion (B) and at most USD 910 B under Pathways A and B, respectively. For SPP RTO West, Pathway A TSCs are at least USD 230 B, and Pathway B TSCs are at most USD 153 B. TSCs of Pathway A are 1.4–8 times larger than the total system costs of Pathway B. When CCS is not included, the cost per ton of carbon dioxide (CO₂) avoided is estimated to be USD 124–489/ton for MISO-N and USD 248–552/ton for SPP RTO West. When CCS is included, the cost of avoided CO₂ is projected to decrease by 29–87% (mid-point estimate of 73%) with values varying between USD 64 and 114/ton and USD 74 and 164/ton for MISO-N and SPP RTO West, respectively. These differences highlight the need for consideration of all low-carbon-intensive technology options in cost-optimal approaches to deep decarbonization and the value of CCS technologies in the energy transition. Full article

Proper management of water resources in agriculture is of utmost importance for sustainable productivity, especially under the current context of climate change. However, many smart agriculture systems, including for managing irrigation, involve costly, complex tools for most farmers, especially small/medium-scale producers, despite the availability of user-friendly and community-accessible tools supported by well-established providers (e.g., Google). Hence, this paper proposes an irrigation management system integrating low-cost Internet of Things (IoT) sensors with community-accessible cloud-based data management tools. Specifically, it resorts to sensors managed by an ESP32 development board to monitor several agroclimatic parameters and employs Google Sheets for data handling, visualization, and decision support, assisting operators in carrying out proper irrigation procedures. To ensure reproducibility for both digital experts but mainly non-technical professionals, a comprehensive set of guidelines is provided for the assembly and configuration of the proposed irrigation management system, aiming to promote a democratized dissemination of key technical knowledge within a do-it-yourself (DIY) paradigm. As part of this contribution, a market survey identified numerous e-commerce platforms that offer the required components at competitive prices, enabling the system to be affordably replicated. Furthermore, an irrigation management prototype was tested in a real production environment, consisting of a 2.4-hectare yellow kiwi orchard managed by an association of producers from July to September 2021. Significant resource reductions were achieved by using low-cost IoT devices for data acquisition and the capabilities of accessible online tools like Google Sheets. Specifically, for this study, irrigation periods were reduced by 62.50% without causing water deficits detrimental to the crops’ development. Full article

This study presents the development of a predictive system based on artificial neural networks (ANNs) and fuzzy logic to estimate losses in an agave liquor production and packaging plant. Currently, these losses are discharged into wastewater, generating not only finished product waste, but also greater environmental pollution and higher treatment costs. To address this, agave liquor waste is converted into methane biogas through anaerobic digestion and subsequently transformed into electrical energy. The system begins by collecting historical data from the production process, including production plans and shrinkage rates at each stage of the packaging line. These data are analyzed to identify behavioral patterns and correlations between process variables and losses, allowing a deeper understanding of the packaging process. Critical control points were identified throughout the production stages, and an ANN model was trained with historical data to predict losses. Outstanding results were achieved in the packaging and capping stage, where a significant impact on bottle loss was observed, with a 29% impact in the morning shift and a 35% impact in the afternoon shift. Fuzzy logic was used to manage the uncertainty and subjectivity associated with identifying the stages most susceptible to waste, translating qualitative assessments into quantitative metrics. Estimates allow for approximately 8% to 12% reductions by streamlining the process with this analysis obtained through the use of artificial intelligence tools. This integrated approach aims to optimize operational efficiency, reduce losses, minimize environmental impact, and promote sustainable practices within the agave liquor industry. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

As the global demand for energy-efficient solutions grows increasingly urgent, passive design strategies emerge not only as a means to support the reduction in energy consumption but also as a pathway to minimizing building operational costs while enhancing thermal comfort and architectural attractiveness. On the other hand, the recognition and significance of building environmental certification schemes are steadily increasing worldwide. Within this context, this research investigates the extent to which passive bioclimatic principles are understood, applied, and incentivized in contemporary sustainable building practices in Greece—focusing in particular on their representation within the LEED certification credit structure. Drawing on a questionnaire survey completed by 89 experienced Greek construction professionals, the findings indicate a significant gap between the theoretical value attributed to passive design and its practical implementation. The respondents attribute this gap to two key factors within the Greek context: the lack of adequate education and awareness among key project stakeholders, and the considerable complexity associated with the collaborative frameworks required from the early design stages. Additionally, LEED appears to offer limited incentives for integrating passive design strategies. Instead, it tends to favor technological solutions and follows a standardized structure with minimal scope for regional customization. Enhancing LEED’s region-specific features to reward passive strategies proven effective in local contexts would be particularly expedient in reinforcing its role as a robust and impactful tool for promoting sustainability. Full article

This article presents an optimized approach to battery sizing and economic dispatch in wind-powered microgrids. The primary focus is on integrating battery depth of discharge (DoD) constraints to prolong battery life and ensure cost-effective energy storage management. Because of the intermittent nature of wind energy, wind-powered microgrids require sophisticated energy storage systems to ensure stable operation. This study develops a metaheuristic optimization method that balances power supply, battery lifespan, and economic dispatch in a microgrid. The proposed method optimizes both battery size and dispatch strategy while considering wind energy variability and the impact of DoD on battery lifespan. Case studies conducted on a wind-powered microgrid under varying load conditions show that the developed approach achieves a 40 to 50% reduction in operating costs and cost of electricity (CoE) compared to other approaches. The results also reveal that the inclusion of DoD constraints enhances battery lifespan. The proposed method offers a practical solution for improving the economic and operational efficiency of wind-powered microgrids, providing valuable understanding for energy planners and grid operators in renewable energy systems. Full article

The integration of artificial intelligence (AI) and machine learning (ML) technologies in energy storage systems has emerged as a transformative approach in addressing the complex challenges of modern energy infrastructure. This comprehensive review examines current state of the art AI applications in energy storage, from battery management systems to grid-scale storage optimization. We analyze various AI techniques, including supervised learning, deep learning, reinforcement learning, and neural networks, and their applications in state estimation, predictive maintenance, energy forecasting, and system optimization. The review synthesizes findings from the recent literature demonstrating quantitative improvements achieved through AI integration: distributed reinforcement learning frameworks reducing grid disruptions by 40% and operational costs by 12.2%, LSTM models achieving state of charge estimations with a mean absolute error of 0.10, multi-objective optimization reducing power losses by up to 22.8% and voltage fluctuations by up to 71%, and real options analysis showing 45–81% cost reductions compared to conventional planning approaches. Despite remarkable progress, challenges remain in terms of data quality, model interpretability, and industrial implementation. This paper provides insights into emerging technologies and future research directions that will shape the evolution of intelligent energy storage systems. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

In modern intelligent buildings, the control of Building Energy Systems (BES) faces increasing complexity in balancing energy costs, thermal comfort, and operational flexibility. Traditional centralized or flat deep reinforcement learning (DRL) methods often fail to effectively handle the multi-timescale dynamics, large state–action spaces, and strict constraint satisfaction required for real-world energy systems. To address these challenges, this paper proposes an energy policy-aware layered control architecture that combines Virtual Energy Storage System (VESS) modeling with a novel Dynamic Constraint-Aware Policy Optimization (DCPO) algorithm. The VESS is modeled based on the thermal inertia of building envelope components, quantifying flexibility in terms of virtual power, capacity, and state of charge, thus enabling BES to behave as if it had embedded, non-physical energy storage. Building on this, the BES control problem is structured using a hierarchical Markov Decision Process, in which the upper level handles strategic decisions (e.g., VESS dispatch, HVAC modes), while the lower level manages real-time control (e.g., temperature adjustments, load balancing). The proposed DCPO algorithm extends actor–critic learning by incorporating dynamic policy constraints, entropy regularization, and adaptive clipping to ensure feasible and efficient policy learning under both operational and comfort-related constraints. Simulation experiments demonstrate that the proposed approach outperforms established algorithms like Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). Specifically, it achieves a 32.6% reduction in operational costs and over a 51% decrease in thermal comfort violations compared to DQN, while ensuring millisecond-level policy generation suitable for real-time BES deployment. Full article

This study presents the development of a novel alumina-based ceramic composite designed for refractory applications in auxiliary components of sintering furnaces. The innovative concept relies on a three-phase microstructural architecture: a fine-grained alumina matrix improves cohesion, coarse particles act as crack propagation barriers, and spherical granules are intentionally introduced to increase porosity while preserving mechanical strength. This design reduces thermal capacity, enhancing the material’s energy efficiency under high-frequency thermal cycling and offering potential for operating cost reduction. A further novelty is the water-free forming process, which eliminates issues related to drying and deformation. The material was characterized using scanning electron microscopy (SEM), mechanical strength testing, and refractoriness under load (RUL) analysis to establish the structure–property relationships of the developed composite. The results demonstrate that the developed spherical alumina-based composite possesses excellent thermal and mechanical properties, making it a promising candidate for high-temperature industrial applications, particularly as auxiliary refractory plates. Full article