Search Results (1,179)

The industrial scalability and economic competitiveness of biomass-to-bioenergy and biorefinery systems depend on reliable feedstock supply, consistent biomass quality, and efficient logistics. An aspect that remains underexplored in biomass-to-biorefinery supply chain optimization is the incorporation of biomass quality uncertainty into decision-making models. Biomass quality characteristics, including ash content, moisture, chemical composition, and dry matter loss, can influence storage, preprocessing, transportation, conversion efficiency, biorefinery yields, process reliability, and overall energy utilization. Although these characteristics are difficult to model due to their spatial, temporal, and operational variability, ignoring their effects can lead to suboptimal supply-chain designs, inaccurate cost estimates, and unrealistic assessments of biorefinery performance. This paper reviews the treatment of biomass quality characteristics in the literature on quantitative modeling and analysis of biomass-to-biorefinery supply chains. Positioned from an Operational Research (OR) perspective, this review emphasizes mathematical modeling, computer simulation, optimization, and decision-support approaches for biomass-to-bioenergy systems. A total of 71 English-language published articles are reviewed and classified according to modeling approach and quality characteristic(s) considered. Across the selected literature that quantified biomass quality effects, cost reductions along supply chain operations ranging from 6% to 31% were reported when quality-aware models were compared with approaches that ignored quality or assumed unrealistic biomass quality characteristics. Despite these findings, biomass quality remains underrepresented in current analytical models; ash content, dry matter loss, and chemical composition were considered in only 10.4%, 4.3%, and 0.9% of the reviewed literature, respectively. This review summarizes the current state of research and outlines a future research agenda for integrating biomass quality control, uncertainty modeling, and optimization into scalable bioenergy and biorefinery systems. Full article

Energy efficiency is a primary design objective for battery-powered IoT devices. While offloading computation-intensive tasks to edge servers has been extensively studied to mitigate power drains, relatively little attention has been paid to the long-term financial cost of commercial edge services. This article proposes Volunteer Edge, a cost-effective real-time task offloading framework that exploits underutilized computing resources of privately managed nodes to execute offloaded workloads. Unlike conventional public edge servers, volunteer edge nodes provide inexpensive computing resources but are subject to unpredictable node churn. To address this challenge, we present a dual-class task model that partitions workloads into critical and normal tasks, and selectively applies task replication to volunteer edge nodes. The framework jointly optimizes task placement, processor frequency scaling, and replication decisions using a steady-state genetic algorithm to minimize task execution cost and IoT-device energy consumption while satisfying schedulability and reliability constraints. Extensive simulations demonstrate that Volunteer Edge significantly reduces offloading cost while maintaining IoT-device energy efficiency and protecting critical tasks against volunteer node failures. Specifically, the proposed framework reduces edge rental costs by 54.0% on average compared with conventional public-edge-based offloading while maintaining reliable execution of critical real-time tasks. Full article

Solar radiation is the foundation of efficient greenhouse production. Studying how structural parameters affect light transmission and solar energy utilization efficiency is crucial for optimizing greenhouse design and increasing crop yields. This study constructed a full-process solar radiation model using ray tracing technology. It examined the solar radiation energy intercepted, captured, and distributed by the greenhouse. The simulation yielded high prediction accuracy and rapid subsequent computation after pre-calculation of the shape factor matrix. The coefficient of determination R² was at least 0.95 for four observation points, with computations for 51 time points completed within 125 s. Subsequently, a comparative analysis of large-span insulated plastic greenhouses with different parameters was conducted during the cold and warm seasons based on eight representative solar terms. The results indicated that for a 20-m-span greenhouse at 35° N, an east-west orientation with a 15 m south roof and a 6 m ridge was better for the cold season. For comprehensive seasonal adaptability, a north-south orientation with a 5 m ridge was recommended. This study provides seasonal-oriented decision support for the structural parameter design of large-span insulated plastic greenhouses. Full article

Reliable integration of Building Information Modeling (BIM) and Building Energy Modeling (BEM) is essential to sustainable design and renovation because energy-efficiency decisions depend on consistent analytical models. However, native gbXML exports often contain geometric and semantic inconsistencies that can distort energy assessments. This study evaluates native export limitations and the effect of middleware-based transformation on BIM-to-BEM interoperability. A residential building was modelled in Autodesk Revit and Graphisoft Archicad. gbXML exports and the BIMTWIN workflow were assessed using geometry, analytical integrity, and heat-loss indicators; only geometric results were compared with the Energy Performance Certificate. Although all workflows generated gbXML files, their analytical quality differed substantially. The middleware-transformed model deviated by only +1.53% and +2.25% from the reference external and internal volumes, respectively. Native Revit export omitted intermediate floor constructions and produced a total heat loss of 95,785 W, 25.2% higher than the 76,533 W obtained from the transformed model. Native Archicad export produced fragmented geometry unsuitable for reliable heat-loss calculation. The results show that BIM-to-BEM interoperability should be treated as a controlled validation and transformation process. By reducing analytical errors and manual reconstruction, this approach supports more reliable assessment of energy-efficiency measures and better-informed decisions in sustainable building design and renovation. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article

Convolutional Neural Networks (CNNs) are fundamental tools for image analysis and recognition in monitoring systems, particularly in photovoltaic (PV) installations, where visual inspection and thermal imaging play crucial roles in anomaly detection and energy optimization. This publication presents a Hybrid Fuzzy Convolutional Neural Network (HFCNN) that integrates a fuzzy dense layer utilizing Ordered Fuzzy Numbers (OFNs) into the CNN architecture. The architecture is additionally validated on the public ELPV benchmark of 2624 electroluminescence images of photovoltaic cells, where the HFCNN with Mean of Maxima defuzzification attains classification quality statistically indistinguishable from a CNN baseline while using a four times smaller dense layer and training two to three times faster. The methodology combines the feature extraction capabilities of traditional CNNs with the uncertainty handling properties of fuzzy logic. Experiments using the MNIST dataset demonstrate that HFCNN with Mean of Maxima (MOM) defuzzification achieves comparable accuracy to standard CNNs while using significantly fewer parameters (75% reduction in the dense layer). This efficiency gain is advantageous for deployment on edge computing devices. This work constitutes a methodological contribution—establishing, for the first time, the feasibility of integrating Ordered Fuzzy Numbers into CNN architectures without requiring expert membership function design. While the current study validates this approach on MNIST, actual photovoltaic applications require dedicated future research on real PV thermal imagery. Nevertheless, the proposed HFCNN framework could potentially support practical photovoltaic energy system applications in detecting panel degradation, performance anomalies, and autonomous decision-making in large-scale PV installations. Full article

Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support. Full article

The flexible and precise design of photovoltaic (PV) skin morphologies for building facades constitutes a critical element for optimizing building energy efficiency and enhancing indoor spatial performance. However, the complex interrelationships among climatic parameters and their spatiotemporal variations pose significant challenges to the prior validity and accuracy of climate-adaptive parametric skin morphology adjustments. To address these limitations, this study proposes a morphological generative framework for climate-adaptive Building-Integrated Photovoltaics (BIPV) facades integrating Artificial Intelligence Algorithms and Bayesian prior-parameterized building envelopes. This framework is specifically designed to facilitate morphological decision-making regarding the overall climate-adaptive opening states of parametric PV skins under spatiotemporal dynamics. The proposed method integrates AI-based pattern recognition in spatiotemporal climate data with Bayesian Network-based prior probability techniques to derive optimal facade morphology schemes with the highest overall climate adaptability scores derived from weather forecasts, thereby achieving optimal transformations of the building envelope. Specifically, the model first employs an Artificial Intelligence Algorithm to generate the Bayesian Network structure required for overall climate adaptability scoring. Secondly, utilizing the Chinese Standard Weather Data (CSWD), the GRASSHOPPER algorithm is applied to implement variable parametric design on the facade skin, generating dynamic parametric skins and visual climatic data analysis cloud maps for energy benefit assessment. Finally, facade updates are executed based on the overall climate adaptability scores. The results demonstrate that the proposed framework effectively enables the real-time selection of optimal morphologies and opening states for dynamic skins based on comprehensive climatic adaptability criteria. Following model training and validation using 2025 Panjin meteorological data in the EnergyPlus Weather (EPW) format, the generated facade morphologies yielded solar radiation gains of 166.9 kWh/m²·month (peak month) for one of the optimal summer configurations and 90.5 kWh/m²·month (December) for one of the optimal winter configurations. Furthermore, by providing definitive evaluations of PV energy yields and indoor comfort levels across diverse weather scenarios, this framework offers explicit guidance for skin design, thereby reconciling the multi-objective optimization relationship between building energy conservation and occupant comfort. Full article

This paper examines the energy renovation of buildings using building-integrated photovoltaic (BIPV) systems applied to facades and roofs. Although BIPVs are increasingly recognized as a vital component of sustainable retrofit strategies, designers still lack comparative decision-support tools for the simultaneous technical and economic evaluation of roof- and facade-integrated (envelope-integrated) configurations. To address this gap, this study presents a methodology for the energy-efficient retrofit of multi-story student residence halls through a parametric analysis. This comparative approach evaluates energy performance and electricity production while estimating the associated costs of BIPV systems. Building upon prior research, the current analysis utilizes innovative, energy-optimized models to form the basis of computer simulations. The methodology relies on a fundamental assumption involving an arbitrary parameter that defines grid-energy substitution rates by BIPV-generated electricity. Regarding the economic aspects, the results indicate that while certain rooftop installations offer lower costs and shorter payback periods than facade systems, other rooftop configurations exhibit higher costs and extended payback periods. This research provides valuable insights for optimizing civil engineering solutions in photovoltaics, where local electricity production effectively replaces grid-supplied energy. Furthermore, the proposed innovative qualitative and quantitative input/output models can be extended to parameterize other building renovation characteristics within civil engineering methods. Full article

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

The increasing penetration of renewable energy has created an urgent need for economically competitive large-scale energy storage technologies. Conventional economic evaluations mainly focus on lifecycle costs while neglecting market participation, revenue diversification, and investment uncertainty. This study proposes an integrated lifecycle economic assessment framework combining discounted cash flow (DCF) theory, lifecycle cost analysis, multi-market revenue modeling, and uncertainty analysis. A revenue-adjusted indicator, termed Net Levelized Cost of Storage (Net LCOS), is introduced to quantify the actual economic competitiveness of energy storage technologies by incorporating revenues from energy arbitrage, ancillary services, and capacity remuneration. The proposed framework is applied to three representative large-scale energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), and battery energy storage (BES). The results show that PHS exhibits the lowest levelized cost of energy (LCOE) (0.519 RMB/kWh) and the strongest economic robustness owing to its long service life and superior capital amortization capability. Incorporating multi-market revenues substantially improves the economic performance of all storage technologies. BES exhibits the largest reduction in Net LCOS, whereas PHS maintains the lowest Net LCOS and the strongest overall economic competitiveness. Sensitivity analysis identifies conversion efficiency, capital investment, capacity remuneration, and operational utilization as the dominant determinants of storage economics. The proposed framework extends a comprehensive approach for comparing large-scale energy storage technologies by integrating lifecycle costs, market revenues, and uncertainties, supporting investment decisions and electricity market design. Full article

Spent cathode carbon (SCC), a hazardous waste generated during primary aluminium production, contains valuable graphitic carbon resources but remains difficult to recycle because carbon purification is governed by complex interactions among multiple leaching parameters. Conventional process optimization relies on extensive laboratory experimentation, resulting in high chemical consumption, energy use, and development costs. This study presents an explainable machine learning framework for cleaner and more resource-efficient carbon purification from SCC under limited-data conditions. Six machine learning algorithms (GBDT, CatBoost, XGBoost, LightGBM, Random Forest, and Decision Tree) were evaluated using experimental data from alkaline leaching. The LightGBM model was systematically optimized by combining Response Surface Method (RSM), Orthogonal Experimental Design (OED), and local parameter optimization methods. The optimized model (min_child_samples = 2, num_leaves = 32, n_estimators = 500, and learning_rate = 0.5) achieved an R² of 0.8015, RMSE of 0.9163, and MAE of 0.6599. SHAP analysis identified initial alkali concentration, liquid–solid ratio, stirring rate, and leaching time as the dominant factors controlling carbon purification, whereas temperature had a comparatively smaller influence within the investigated operating range. The results indicate that improving reagent utilization and hydrodynamic conditions offers greater potential for enhancing carbon purification than increasing thermal input alone. By integrating statistical experimental design with explainable machine learning, this study establishes an efficient, interpretable, and transferable decision-support framework for optimizing hazardous waste recycling and other resource recovery processes under limited-data conditions, thereby supporting cleaner production and circular economy practices. Full article

Efficient blasting design is increasingly regarded not only as a geomechanical problem but also as a managerial challenge related to energy use, fragmentation quality, downstream comminution costs, and environmental performance. This study develops a shock-adiabat-based analytical model for predicting the radius of the crushing zone around borehole explosive charges and demonstrates its applicability as a decision support tool for energy- and cost-oriented blasting management. The model integrates shock wave propagation parameters, particle velocity behind the shock front, and the physical and mechanical properties of limestone, sandstone, and granite. The calculated crushing zone radiation was compared with a previously developed analytical model based on borehole pressure and validated using finite element simulations in SolidWorks Simulation. The discrepancy between the proposed shock adiabat model and the reference analytical solution did not exceed 6%, while the difference between analytical estimates and numerical simulations remained below 5%. The results show that borehole diameter, compressive strength, and explosive–rock interface pressure significantly affect the crushing zone radius and, consequently, the volume of rock effectively fragmented during blasting. A scenario-based assessment further indicates that improved prediction and management of the crushing zone may reduce downstream crushing and grinding energy demand by approximately 10–20%, generating potential cost savings and indirect CO₂ emission reductions. The proposed method therefore supports the management of blasting energy efficiency, fragmentation quality, operational costs, and sustainability performance in mineral extraction systems. Full article

Given the modernization of power systems in recent years, the quality of electrical energy is changing. With the increasing prevalence of harmonic components and rising current densities, conductor efficiency has become critically important. This study investigates the skin effect as a function of conductor geometry within the framework of electromagnetic field theory. Classical circular cross-section cable geometries and rectangular busbar systems were compared under an AC current of 1350 A (peak) across a frequency range of 50–500 Hz. The findings are comparatively presented, and their electromagnetic and thermal implications are discussed. Numerical modeling and simulation studies were performed using the Finite Element Method. COMSOL Multiphysics^® software AC/DC Module 6.2 version was used for the analyses. In the simulation studies, the magnetic flux density distribution within the conductor and the current concentration induced by eddy currents were analyzed. Frequency-dependent behavioral characteristics were examined in the analyses. The results revealed that the conductor with circular geometry exhibited a more severe skin effect. The rectangular conductor used in busbar systems was found to effectively distribute the current density across its surface area. Thus, rectangular geometry optimizes AC resistance. The analysis results revealed that conductor design and material selection depend not only on the cross-sectional area but also on the geometric shape factor. In this context, it was determined that conductor design has a decisive effect on electromagnetic power losses, which directly govern the heat generation potential within high-current systems. This study serves as a technical guide to evaluate frequency-dependent electromagnetic performance across a 50–500 Hz range—reflecting frequencies relevant to harmonic components—to assist in the design and optimization of high-current energy distribution systems. Full article

Population aging has increased the need for continuous, non-invasive, and context-aware monitoring systems capable of supporting autonomy, safety, and early intervention in daily living environments. Multimodal biometric sensing offers an important technical basis for this purpose, as it combines physiological, motion-related, and environmental signals to provide a more complete view of older adults’ functional and health-related conditions. However, many existing solutions remain fragmented, device-dependent, and insufficiently connected to core instrumentation requirements, including signal quality, sensor calibration, temporal synchronization, latency, energy consumption, interoperability, reliability, and data privacy. This article proposes an Edge-AI instrumentation framework for multimodal biometric sensing in active aging environments, supported by a structured analysis of recent literature on wearable, ambient, and context-aware sensing systems. The framework integrates wearable, ambient, and context-aware sensors with local processing capabilities to support signal acquisition, preprocessing, quality control, feature extraction, anomaly detection, and decision support close to the data source. By placing Edge AI within the instrumentation pipeline, the proposed framework identifies design requirements that may help reduce response time, limit unnecessary transmission of sensitive biometric data, and improve feasibility in home-based and assisted-living contexts. These expected benefits, however, require empirical testing through future prototype implementation and real-world evaluation. The article also defines a validation-oriented perspective for sensor-based active aging systems, covering technical, operational, and human-centered dimensions such as measurement accuracy, signal robustness, usability, privacy preservation, interoperability, reproducibility, energy efficiency, and system scalability. The proposed framework is intended to support the design, comparison, and validation of more reliable, interpretable, and reproducible sensor-based monitoring systems, while offering a structured basis for prototype development and future real-world evaluation in active aging environments. Full article