Search Results (55,420)

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions. Full article

The transition to net-zero energy systems requires operationally grounded decision-making frameworks that integrate technology performance, infrastructure readiness, and policy constraints at local scale. Food transportation represents a high-emission and operationally critical component of regional energy and supply chain systems, particularly in food-producing regions. This study proposes a preliminary Local Area Energy Planning (LAEP) framework to support operational decision-making for the decarbonisation of food transportation, using Lincolnshire, UK, as a case study. The framework evaluates alternative freight transport technologies—battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (HFCEVs), battery electric road systems (BERS), and conventional internal combustion engine vehicles—across energy efficiency, CO₂ emissions, infrastructure requirements, and cost implications. Secondary data from national statistics, regional planning documents, and peer-reviewed literature are analysed using comparative quantitative and qualitative assessment methods. Results indicate that BEVs currently offer the most energy-efficient and cost-effective solution for short-haul and last-mile food logistics, achieving overall efficiencies of approximately 77–82% with zero tailpipe emissions. HFCEVs and BERS present potential long-term operational advantages for heavy-duty and long-haul freight, but remain constrained by high infrastructure investment, energy conversion losses, and system-level costs. The findings highlight the importance of phased technology adoption, renewable energy integration, and infrastructure prioritisation to enable sustainable energy operations in freight transport systems. By embedding technology comparison within a place-based planning framework, this study contributes actionable insights for local authorities, logistics operators, and policymakers seeking to support operational decision-making in sustainable energy systems. The proposed LAEP framework is transferable to other food-producing regions aiming to decarbonise freight transportation while maintaining operational efficiency. Full article

Industrial processing of apple to obtain products like juice or cider generates a significant amount of pomace, which represents 25–30% of the fresh fruit mass. Different technologies are needed to valorize apple pomace (AP), considering its significant amount of high-value compounds, such as fiber, vitamins, and polyphenols. Hot-air convection (CA) and infrared (IR) drying are widely used methods for preserving polyphenols from by-products, such as apple pomace (AP), while also extending their shelf life. This study aimed to evaluate the influence of CA and IR drying on drying kinetics, color parameters, and the preservation of polyphenolic compounds, as well as to identify a sustainable extraction approach. Both drying methods significantly affected the color characteristics and content of polyphenols with high antioxidant activity. A significant impact was noticed at higher temperatures, which may be associated with the partial inactivation of browning enzymes. IR drying resulted in a shorter drying time and lower specific energy consumption compared to CA. Furthermore, the assessment of solvent efficiency in ultrasound-assisted extraction (UAE) indicated that the natural deep eutectic solvent (NaDES) composed of choline chloride and glycerol (1:1 molar ratio) provided superior recovery of phenolic compounds with high antioxidant activity compared to conventional solvents and the other NaDES analyzed. Optimization of UAE conditions using this polyol-based NaDES allowed for achieving an extract characterized by a polyphenolic profile dominated by flavan-3-ols (catechin and epigallocatechin), followed by phenolic acids, mainly chlorogenic acid. These results confirm the potential of AP as a valuable source of bioactive compounds and of polyol-based NaDESs as a sustainable and efficient alternative for their recovery. Full article

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

This work presents the design of an attitude control experiment for onboard OPS-SAT-1 satellite execution, conceived with inherent extensibility to future mission architectures. OPS-SATs are ESA nanosatellite mission series designed as an in-orbit testbed for validating novel software and control techniques under real space conditions, OPS-SAT-1 being the first mission. Equipped with an advanced payload computer, OPS-SAT-1 enabled experimentation with innovative mission operations, including real-time attitude control strategies. Two attitude control algorithms, a modified Proportional–Integral–Derivative (mPID) and a fuzzy logic controller, were designed and implemented for the OPS-SAT-1. The design methodology applied to these controllers consisted of (i) modelling the space environment and satellite characteristics, (ii) assessing actuator feasibility, (iii) determining the operational ranges for attitude error and angular velocity, (iv) parametrizing controllers within these ranges, (v) fine-tuning controllers using multi-objective genetic optimization, and (vi) robustness analysis using the Monte Carlo method. Despite the technical issues related to communication with the OPS-SAT-1 hardware, which prevented the execution of the experiment in orbit, this work presents the simulation results that were obtained. These results indicate that fuzzy logic controllers may outperform PID controllers in terms of the accumulated error, settling time and steady-state error, whereas power efficiency appears to be less robust than in the PID. This suggest that a large uncertainty in the model could lead the PID to become more efficient. Near the nominal scenario, the fuzzy controller achieves superior error–cost trade-offs, enabling precise attitude stabilization with lower energy consumption. These findings suggest the potential advantages of modern control approaches compared to classical methods, which will be further assessed through future in-orbit experiments. Full article

The development of lithium-ion batteries (LIBs) capable of extreme fast charging (XFC) while preserving safety, durability, and practical energy density remains a central challenge for next-generation electric transportation and grid-scale storage. Conventional graphite anodes are fundamentally limited at high current densities by sluggish intercalation kinetics, which cause lithium plating, motivating the exploration of alternative insertion materials. This review provides a comprehensive and internally consistent assessment of titanium-based oxide anodes, encompassing TiO₂ polymorphs, lithium titanate (Li₄Ti₅O₁₂), and Wadsley–Roth titanium niobium oxides, through the combined lenses of crystal topology, diffusion pathways, redox chemistry, interfacial behavior, and resource scalability. By systematically comparing structural frameworks and electrochemical mechanisms across these material classes, we demonstrate that fast-charging performance is governed not by nano-structuring alone, but by the intrinsic coupling between operating potential, framework rigidity, and multi-electron redox activity. While Li₄Ti₅O₁₂ establishes the benchmark for safety and cyclability, and TiO₂ polymorphs provide structural versatility, titanium niobium oxides uniquely reconcile high theoretical capacity with minimal lithiation strain and open diffusion channels, positioning them as highly promising candidates for sub-10 min charging without catastrophic degradation. This review highlights the persistent obstacles these materials suffer, such as limited round-trip energy efficiency (RTE), interfacial gas evolution, poor dopant stability, and unsustainable extraction, while simultaneously exploring targeted design strategies to overcome them. Finally, this review provides a materials design and comparison framework for the development of safe, high-power, and commercially viable ultrafast-charging LIBs. Full article

Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpretability, and preserve adaptability as operating conditions evolve. To address this issue, we propose a multimodal short-term photovoltaic (PV) forecasting method that integrates weather-mode partitioning using the Clustering Using Representatives (CURE) algorithm with a self-updating Batch-Lasso model. First, the meteorological-PV dataset is partitioned along two dimensions by combining seasonal grouping with CURE clustering within each season, producing representative weather modes and enhancing the fidelity of weather pattern classification. Second, to extract informative predictors from high-dimensional meteorological inputs while maintaining interpretability, we formulate per-mode Lasso regression and adopt the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to efficiently solve for the sparse regression coefficients. Third, we introduce a batch-based self-update and correction mechanism with rollback verification, enabling the mode-specific models to be refreshed as new historical data become available while preventing performance degradation. Compared with representative machine learning baselines, the proposed method maintains competitive accuracy with substantially lower computational and storage overhead, enabling high-frequency and energy-efficient inference on resource-constrained terminals, thereby reducing operational burdens and computational energy costs and better meeting the deployment needs of sustainable energy systems under heterogeneous weather conditions. Full article

Target tracking for uncrewed aerial vehicles (UAVs) demands both low-latency, real-time inference and robust, long-term temporal consistency. Current approaches often face a trade-off between efficiency and stability in practice. This tension is particularly pronounced in resource-limited UAV platforms: computationally heavy architectures can exceed onboard processing capacity and energy budgets, whereas overly lightweight models degrade temporal state fidelity—leading to cumulative drift under challenging conditions such as occlusion, motion blur, rapid scale variation, and cluttered backgrounds. To address this challenge, we propose SPM-Track, a lightweight yet temporally consistent tracking framework grounded in explicit state maintenance. It introduces a dual-loop judgment-calibration architecture comprising three coordinated components: (1) the content-aware state encoder, which employs input-gate modulation, selectively models temporal dynamics to suppress noise propagation into the state; (2) the hierarchical state manager enhances robustness against long-term occlusions and appearance variations by coordinating short-term state updates with a long-term reliable snapshot library via dual-path cooperation; (3) the adaptive feature recalibration module applies joint spatial-channel discriminative weighting before response map generation, effectively enhancing target distinctiveness and mitigating background clutter interference. Experiments on UAV123, DTB70, UAVTrack112, and LaSOT show that SPM-Track outperforms lightweight baselines and remains competitive with several Transformer-based trackers, demonstrating a favorable trade-off between edge-deployable efficiency and long-term robustness in UAV-based tracking. Full article

Sustainable ridge-based peanut production following winter wheat requires soil preparation technologies capable of simultaneously ensuring precise ridge formation, reduced energy consumption and efficient in situ utilisation of crop residues. This study aimed to develop and experimentally validate a combined soil preparation technology integrating shallow tillage, deep loosening and ridge formation within a single field pass, and to quantify its technological and biological performance. Field experiments were conducted using a prototype combined machine with analytically justified geometric parameters of the working tools, followed by multifactor optimisation and statistical modelling. Technological performance was assessed by soil fragmentation degree and draft resistance, while biological effects were evaluated using residue incorporation (Pz), biodegradation coefficient after 60 days (k₆₀) and dehydrogenase activity after 30 days (DHA₃₀). The results showed statistically significant nonlinear relationships between tool parameters and technological responses, with coefficients of determination exceeding 0.94 for soil fragmentation and 0.97 for draft resistance. The proposed technology increased residue incorporation efficiency by 15–20%, enhanced biodegradation intensity (k₆₀) by up to 18%, and reduced energy consumption due to single-pass operation compared with conventional multi-pass systems. A strong relationship between Pz and biological indicators confirmed the key role of residue placement in controlling microbial processes. These findings demonstrate that integrated control of soil processing and residue placement enables energy-efficient single-pass technologies for ridge-based peanut production systems. Full article

This study aims to explore and propose a design-oriented methodology for solar–thermal methanol reforming (ST-MSR) hydrogen production equipment suitable for marine applications. To address key challenges such as the intermittency of solar energy, spatial and environmental constraints on board ships, operational safety, and user experience, a multidisciplinary integrated-design decision-making framework is established. First, the Kano model is employed to systematically analyze the latent needs of target users regarding ST-MSR equipment, while the analytic hierarchy process (AHP) is used to determine the weighting of evaluation criteria. Second, the theory of inventive problem solving (TRIZ) is applied to generate innovative conceptual design solutions. Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is adopted to conduct a multi-dimensional comprehensive evaluation and optimization-based selection of the conceptual alternatives. The optimal design scheme is thus identified in terms of energy performance, product characteristics, user experience, economic feasibility, and environmental adaptability. The results indicate that the microchannel and phase-change thermal-storage integrated solar–thermal-tracking chemical reactor achieves the highest comprehensive evaluation score among the proposed schemes, demonstrating superior performance in terms of safety, energy efficiency, and adaptability to marine environments. This research provides a systematic industrial design methodology and practical reference for the design and product development of clean energy equipment for ships, contributing to the green and sustainable transformation of the maritime industry. Full article

Background/Objective: Buccal delivery offers a potential route to circumvent gastrointestinal degradation and hepatic first-pass metabolism, but hydrophilic peptides typically exhibit limited mucosal permeation. Nanostructured lipid carriers (NLCs) have been proposed as delivery platforms capable of modulating interfacial interactions and improving mucosal transport. This study aimed to quantitatively evaluate the ex vivo buccal permeation of angiotensin II (Ang II), used as a hydrophilic peptide model, when associated with NLCs compared with free peptide under matched Franz diffusion cell conditions. Methods: Ang II-associated NLCs were prepared by melt emulsification combined with a low-energy injection technique. Particle size, polydispersity index, and zeta potential were determined by dynamic light scattering and laser Doppler electrophoresis. Association efficiency and drug loading were quantified by indirect spectrofluorometric analysis. Ex vivo permeation studies were conducted using porcine buccal mucosa mounted in Franz diffusion cells, and cumulative permeation, steady-state flux, and apparent permeability coefficients were calculated. Results: The NLCs exhibited nanometric size, moderate polydispersity, and association efficiency above 80%, and remained colloidally stable at 4 °C for 28 days. In ex vivo experiments, Ang II-associated NLCs showed measurable cumulative permeation, reaching approximately 9% after 2 h, whereas free Ang II was not detected in the receptor compartment under the tested conditions. Conclusions: This work provides a quantitative ex vivo buccal transport comparison of a hydrophilic peptide model delivered as NLC-associated versus free peptide under matched Franz cell conditions. The findings support further investigation of NLC-based approaches for buccal delivery of vasoactive peptides and provide a rational basis for future in vivo evaluation of mucosal delivery performance and systemic exposure. Full article

Stadiums are large buildings that attract attention due to their high energy consumption and environmental impact. Considering the effects of climate change, the integration of sustainable energy solutions and energy efficiency is of great importance in the design and planning of these buildings. This study focuses on pitch lighting, which accounts for a significant and fluctuating share of energy consumption in stadiums, and aims to reduce its carbon footprint through the integration of renewable energy. This study aims to analyze the feasibility of achieving a net-zero annual energy balance for different levels of field lighting of a football stadium in accordance with FIFA lighting standards with solar energy systems in different climate zones and under future climate change scenarios. In addition, it is aimed at revealing the effect of climate change scenarios and climate zone differences on the azimuth angle, tilt angle, and area of the solar panel. In the study, a stadium model was created using parametric design—Grasshopper—and optimization software; lighting systems were designed according to FIFA standards, and lighting performance on the field was optimized with simulations through ClimateStudio and Galapagos. Based on Liverpool FC’s home match data, the annual illumination time is calculated, and the azimuth angle, tilt angle, and area of the solar panel systems are optimized for different climate scenarios. The most useful result of this study is that it demonstrates that the solar panel area required to meet stadium lighting needs varies depending on climate scenarios and geographical conditions and that the same energy production can be achieved with less panel area in low-emission scenarios. For instance, simulation results for Liverpool under the RCP 2.6 scenario show a decrease in the required panel area from 86.09 m² in 2050 to 84.27 m² by 2100. Similarly, in Moscow for the year 2050, the medium-emission scenario (RCP 4.5) requires a larger panel area (92.22 m²) compared to the low-emission RCP 2.6 scenario (88.12 m²) to achieve the same energy output. Full article

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

This study focuses on optimizing hybrid photovoltaic (PV)–wind–lithium-ion battery systems, aiming to balance lifecycle cost (LCC) minimization and power supply reliability (measured by loss of power supply probability, LPSP). A multi-algorithm optimization framework was constructed to compare the performance of Particle Swarm Optimization (PSO), Moth–Flame Optimization (MFO), Grey Wolf Optimization (GWO), and Hybrid Optimizer of PSO and GWO Merits (PSO-GWO) for off-grid power supply; additionally, a PSO-GWO was proposed to address multi-objective demands of economy, environment, and reliability for remote grid-connected power supply. Combined with system architecture design, energy management strategies, and component availability analysis, the PSO-GWO reduced 25-year LCC to $2.024 million, LPSP to 0.05, and cost of energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CEs, operational carbon emissions only) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.981 million and LPSP at 0.01. Thirty independent runs of each algorithm were conducted for statistical validation, and sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, wind speed fluctuations, battery price volatility, and carbon tax changes. The study also expanded the analysis to multiple climatic scenarios, providing an economical, reliable, low-carbon solution with strong generalizability. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article