Search Results (23,480)

Low-voltage electrical appliances, represented by circuit breakers, contactors, and proximity switches, are widely used in various electrical control systems in transportation vehicles. During vehicle operation, engine vibrations, poor workplace balance, constantly changing operating directions, unbalanced transmission systems, emergency braking, and other factors can all cause variable loads. These variable loads may decrease the effectiveness of low-voltage electrical contacts; the failure rate of the low-voltage electrical appliances used in transportation vehicles is three times that of normal indoor low-voltage electrical appliances. This study analyzes the failure mechanism of low-voltage electrical appliances used in transportation vehicles and establishes a model for their reliability evaluation and prediction based on a variable-load data-driven approach. This variable-load data comes from the constructed simulated vehicle operation test platform. Nonlinear variable-load test data generated by simulation is collected through the test platform and is then processed. An evaluation feature dataset is constructed and input into the reliability evaluation and prediction model to obtain the remaining life of low-voltage electrical appliances. The analysis and verification of the predicted evaluation values and the real values detected through platform equipment showed that the accuracy of this model for these appliances based on the variable-load data-driven approach reached 94%, meeting the requirements of practical applications. This method used in this study to derive the model can provide a theoretical basis for online evaluation and prediction of low-voltage electrical appliance reliability for transportation vehicles. This can not only prevent vehicle failures and avoid sudden accidents, but also fully utilize the remaining life of low-voltage electrical appliances and reduce the cost of replacing them. Full article

Selective depression of serpentine remains a major challenge in the flotation of low-grade nickel sulphide ores because serpentine slimes impair concentrate grade and recovery. In this study, four structurally related micromolecular depressants bearing amino and phosphonic functionalities were designed, synthesized and systematically evaluated. Micro-flotation screening (depressant range: 0–20 mg·L⁻¹) and bench-scale tests identified an operational optimum near pH 9 and a reagent dosage of ≈18 mg·L⁻¹; potassium butyl xanthate (PBX) was used as a collector and methyl isobutyl carbinol (MIBC) as a frother. Phosphonate-containing molecules (PMIDA and GLY) delivered the largest gains in pentlandite recovery and concentrate selectivity compared with carboxylate analogues and a benchmark depressant. Mechanistic studies (zeta potential, adsorption isotherms, FT-IR, and XPS) indicated that selective adsorption of amino and phosphonate groups on serpentine occurs via coordination with surface Mg sites and by altering the electrical double layer. The DLVO modelling showed that these reagents generate an increased repulsive barrier, mitigating slime coating and entrainment. Contact-angle measurements confirmed selective hydrophilization of serpentine while pentlandite remained hydrophobic. These findings demonstrate that incorporating targeted phosphonate chelation into small-molecule depressants is an effective strategy to control serpentine interference and to enhance flotation performance. Full article

Relatively uniform consumption of a large amount of electrical energy intended for the current operation of the equipment of multi-story student dormitories indicates several actions aimed at renovation of these dormitories using photovoltaic installations producing electricity to replace the energy supplied from external networks. The research allowed for parameterization of input and output data, defining several innovative parametric and discrete models used in modernization processes and constituting the basis for optimizing energy renovations in terms of the substitutability of grid energy, payback periods, and investment costs. A new method developed to renovate dormitories was supported by an application elaborated in the visual parametric Rhino/Grasshopper design environment. This application enables automatic uploading of various meteorological data files and programming the loads, properties, and operation of the designed photovoltaic installation. This method results in a single optimal solution concerning a building renovation process, which allows for fully automated execution of the above activities. The developed models were configured based on a real renovated multi-story residence student hall located on the Central European Plain, for which a 34.3% balance of the replaced grid energy was carried out. The optimizing processes concerning the geometric properties and orientation of photovoltaic panels resulted in −30° of azimuth, 210 m² of total surface area, and 14° of tilt of photovoltaic panels distributed on the south façade, with 193 m² of surface area, 42° of tilt of panels arranged on the east façade, and an optimal payback period of 99 months. The invented algorithm, parametric models, computer programs, simulations, and optimizing calculations fill the gap in variant-optimized modelling and simplify the design processes of renovations of multi-story residence halls. These objects provide a basis for expanding the method to include other types of dormitory modernizations. Full article

This study estimates the number of electric vehicles (EVs) and charging stations in Sakarya Province, Türkiye, for 2030 using advanced artificial intelligence time series methods and statistical approaches. The novelty of the work lies in the application of hyperparameter-optimized LSTM and GRU models alongside Multiple Linear Regression (MLR) to a regional dataset, enabling accurate, data-driven forecasting for regional EV planning. Performance was evaluated using multiple metrics, including R², MAE, MSE, DTW, RMSE, and MAPE, with the GRU model achieving the highest reliability and lowest errors (R² = 0.99, MAE = 0.3, MSE = 2.9, DTW = 123.2, RMSE = 3.1, MAPE = 2.8%) under optimized parameters. The predicted EV counts and charging station numbers from GRU informed a neighborhood-level allocation of charging stations using Google Maps API, considering local population ratios. These results demonstrate the practical applicability of deep learning for regional infrastructure planning and provide a replicable framework for similar studies in other provinces. Full article

The transition to sustainable urban mobility requires innovative solutions optimising electric vehicle (EV) ecosystems while integrating seamlessly with smart urban grids. This paper proposes a decentralised framework leveraging adaptive algorithms, vehicle-to-grid (V2G) technology, and renewable energy prioritisation to enhance urban sustainability without requiring new infrastructure. By integrating federated learning (FL) for privacy-preserving coordination, multi-objective optimisation for load balancing, and predictive models for renewable energy integration, our approach addresses energy demand, grid stability, and environmental impact in urban areas. Validated through simulations on an IEEE 39-bus urban feeder and real-world urban mobility case studies, the framework achieves a 40% reduction in carbon emissions, improves grid reliability by 20%, and enhances renewable utilisation by 25% compared to an uncoordinated charging baseline. These outcomes support urban planning by informing smart grid design, reducing urban heat island effects, and promoting equitable mobility access. This work provides actionable strategies for policymakers, urban planners, and energy providers to advance more sustainable, electrified urban ecosystems. Full article

Buildings in Indonesia’s tropical climate face significant barriers to energy efficiency due to high cooling loads and electricity intensity. Previous studies have primarily addressed technical optimization or policy frameworks, but few have provided an integrated and data-driven evaluation model for tropical conditions. This study develops an Internet of Things (IoT) and EDGE-based hybrid framework to support the transition toward Net-Zero-Energy Buildings (NZEBs) while maintaining occupant comfort. The research combines real-time IoT monitoring at the LLDIKTI Region XIII Office Building in Banda Aceh with simulation-based assessment using Excellence in Design for Greater Efficiencies (EDGE). Baseline energy performance was established from architectural data, historical electricity use, and live monitoring of HVAC systems, lighting, temperature, humidity, and CO₂ concentration. Intervention scenarios—including building envelope enhancement, lighting optimization, and adaptive HVAC control—were simulated and validated against empirical data. Results demonstrate that integrating IoT-driven control with passive design measures achieves up to 31.49% reduction in energy use intensity, along with 24.7% improvement in water efficiency and 22.3% material resource savings. These findings enhance indoor environmental quality and enable adaptive responses to user behavior. The study concludes that the proposed IoT–EDGE framework offers a replicable and context-sensitive pathway for achieving net-zero energy operations in tropical office buildings, with quantifiable environmental benefits that support sustainable public facility management in Indonesia. Full article

Wide-Bandgap (WBG) semiconductors—silicon carbide (SiC) and gallium nitride (GaN)— enable high-power-density conversion, but performance is limited by where heat is generated and how it is removed. This review links device-level loss mechanisms (conduction and switching, including output-capacitance hysteresis and dynamic on-resistance) to structure-driven hot spots within the ultra-thin (tens of nanometers) two-dimensional electron gas (2DEG) channel of GaN HEMTs and to thermal boundary resistance at layer interfaces. We compare wire-bondless package concepts—double-sided cooling, embedded packaging, and interleaved planar layouts—and survey system-level cooling that shortens the conduction path and raises heat-transfer coefficients. The impact on reliability is discussed using temperature-sensitive electrical parameters (e.g., on-state VDS, threshold voltage, drain leakage, di/dt, and gate current) for real-time junction-temperature estimation and compact electro-thermal RC models for remaining-useful-life prediction. Evidence from recent literature points to interface resistance in GaN-on-SiC as a primary bottleneck, while near-junction cooling and advanced packages are effective mitigations. We argue for integrated co-design—devices, packaging, electromagnetic interference (EMI)-aware layout, and cooling—together with interface engineering and health monitoring to deliver reliable, high-density WBG systems. Full article

To improve the measurement accuracy of direct current (DC) electric field measurement devices in ion flow fields, it is necessary to consider the impact of space charges on the calibration of DC electric field measurements. In this paper, a simulation model of a DC electric field generation apparatus involving space charges is established based on the dilute material transfer principle, and the influence of the applied voltage and gap distance of the apparatus on the calibration results of the DC electric field measurement device is analyzed. Limitations on the electric field calibration of the generation apparatus in the presence of high electric intensities are identified and discussed. The electric field intensity relative error at different gap distances increases as the electric field intensity increases, and an optimal calibration electric field level and an optimal calibration position are identified, both of which increase with an increase in the gap distance. Full article

The aluminum electrolysis industry is a typical high-energy-consumption and high-carbon-emission sector, and its low-carbon transformation is crucial for achieving “dual-carbon” goals. However, aluminum electrolysis is constrained by thermodynamic safety limits, and conventional dispatch models also often overlook carbon emission trading and the integrated utilization of waste heat. To address these challenges, a low-carbon economic dispatch method considering production safety constraints is proposed in the paper for integrated energy systems in aluminum electrolysis, aiming to enhance wind power utilization and ensure operational safety. First, a load model incorporating thermodynamic safety constraints is developed, and a thermal dynamics equation of electrolytic cells is established to characterize the temperature dynamics of aluminum loads. Then, a bi-level optimization framework for the power–aluminum system is constructed: the upper level minimizes grid power-supply costs by coordinating thermal, wind, and photovoltaic generation, while the lower level maximizes enterprise profit, balancing production safety and economic efficiency to achieve coordination between the system and enterprise layers. Finally, a tiered carbon trading mechanism and waste heat heating model are integrated into the framework, combined with a second-order RC building thermal inertia model to realize coordinated optimization among electricity, heat, and carbon flows. The simulation results demonstrate that the proposed method effectively reduces carbon emissions while ensuring electrolytic cell safety: with carbon trading, emissions decrease by 7.2%; when incorporating waste heat utilization reduces boiler heating emissions, they decrease by 74.7%; and further considering building thermal inertia increases wind power utilization to 99.6%, achieving the coordinated optimization of electricity–heat–carbon systems. Full article

With the increasing penetration of distributed energy resources (DERs) in distribution networks, traditional passive distribution systems are evolving into active and flexible systems capable of participating in the transmission-level energy market. Integrating distribution networks into a transmission-centric market-clearing model introduces challenges, such as capturing internal operational constraints and reflecting the economic features of distribution systems. To this end, this paper proposes a market integration method for distribution networks based on a feasible region and an accompanying bidding strategic bidding method to enable their efficient participation in the transmission-level electricity market. With a two-stage adaptive robust optimization framework, the feasible region that preserves operational characteristics of the distribution system and ensures the satisfaction of operational constraints within the distribution system is first depicted. The feasible region appears as time-coupled box-shaped regions. On this basis, a strategic bidding method is proposed based on the nested segmentation of the feasible region, jointly considering power and reserve. With it, the bidding prices of energy and reserve can be prepared, and then, together with the feasible region, can be smoothly integrated into the transmission-level market model. Numerical case studies demonstrate the effectiveness of the proposed method. Full article

This study investigates the thermoelastic frictional contact and wear behavior during reciprocating sliding of a conductive cylindrical punch on a functionally graded piezoelectric material (FGPM)-coated half-plane. The thermo-electro-elastic properties of the coating vary continuously along the thickness direction according to arbitrary gradient functions, with thermal parameters being temperature-dependence. A theoretical framework for the coupled thermo-electro-elastic frictional contact problem is developed and solved using the finite element method. A sequential coupling approach is employed to integrate thermoelastic frictional contact with piezoelectric effects. Furthermore, wear on the coating surface is modeled using an improved Archard formulation, accounting for its impact on thermal sliding frictional contact characteristics. Numerical simulations examine the influence of wear, cycle number, friction coefficient, gradient index and gradient form on the coupled thermo-electro-elastic response of the FGPM coating structure. The numerical results demonstrate the gradient index and gradient form can effectively mitigate thermo-electrical contact-induced damage and reduce friction and wear in piezoelectric materials. Full article

Climate change is one of the most pressing global environmental challenges, driven by the accumulation of greenhouse gases in the atmosphere. Industrial processes, particularly plastic injection molding, are major contributors due to their high energy demand, raw material use, and waste generation. This study quantifies the carbon footprint of plastic injection molding operations and identifies emission hotspots to support alignment with sustainability objectives. A greenhouse gas inventory was developed for the production processes of Petka Mold Industry in Adana, Türkiye, covering 1 January–31 December 2023. The assessment followed the ISO 14064-1:2019 standard and included emissions from direct fuel consumption, purchased electricity, refrigerant leaks, company vehicles, employee commuting, business travel, purchased goods, and waste transportation. Carbon dioxide, methane, and nitrous oxide were calculated in carbon dioxide equivalent units. This research represents the first comprehensive carbon footprint study in the plastic mold sector integrating all categories (Categories 1–6). In addition, uncertainty and materiality analyses were applied to ensure robustness and transparency, an approach rarely adopted in similar industrial contexts. While most previous studies are limited to Categories 1–3, this work expands the boundaries to all categories, offering a pioneering model for industrial applications. The total corporate GHG emissions for 2023 were calculated as 3922.75 metric tons of CO₂e. Among the categories, purchased raw materials and end-of-life product stages were the most significant contributors, whereas transport and auxiliary services had smaller shares. The results provide a reliable baseline for developing action plans and pursuing emission reduction targets. By combining full category coverage with rigorous assessment tools, this study contributes methodological novelty to corporate carbon accounting and establishes a foundation for future progress toward carbon neutrality. Full article

Plant electrical signals are closely related to light conditions, and changes in light intensity lead to variations in the amplitude, frequency, and other characteristics of plant electrical signals. Therefore, real-time analysis of the relationship between plant electrical signals and light factors is crucial for monitoring plant growth status. In this study, Aloe Vera was chosen as the experimental subject, and electrical signal data were collected under different light intensities, followed by preprocessing including wavelet threshold denoising. Furthermore, a hybrid model architecture combining one-dimensional convolutional neural networks (1D-CNNs) and lightweight gated recurrent units (GRUs) was proposed to address the temporal signal characteristics of plant electrical signals and edge computing requirements. The 1D-CNN module extracts local spatial features, which are then modeled in time by the optimized GRU module with channel pruning. Model compression was achieved through parameter quantization. Finally, the computational and storage modules of the model were deployed on an FPGA development board using hardware description language for simulation verification. The results indicate that the system achieved a classification accuracy of 90.1%, a detection time of 43.2 ms, and a power consumption of 4.95 W, demonstrating the comprehensive advantages in terms of accuracy, response speed, and power consumption. This approach effectively improves data processing speed and reduces system power consumption while maintaining high classification accuracy, thereby providing technical support for the development of plant growth monitoring technologies. Full article

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty. Full article

Sesame seed oil is a bioactive-rich lipid source, notable for lignans, tocopherols, and unsaturated fatty acids that underpin its antioxidant and cardioprotective properties. This study optimized two innovative, non-thermal extraction techniques—pulsed electric field (PEF) and ultrasound bath-assisted extraction (UBAE)—to maximize yield and preserve oil quality for functional food applications. A blocked definitive screening design combined with response surface methodology modeled the effects of energy power (X₁, 60–100%), liquid-to-solid ratio (X₂, 10–20 mL/g), and extraction time (X₃, 10–30 min) on fat content, DPPH antiradical activity, and oxidative stability indices (Conjugated Dienes, CDs/Conjugated Trienes, CTs). UBAE achieved the highest fat yield—59.0% at low energy (60%), high X₂ (20 mL/g), and short X₃ (10 min)—while PEF maximized DPPH to 36.0 μmol TEAC/kg oil at high energy (100%), moderate X₂ (17 mL/g), and short X₃ (10 min). CDs were minimized to 19.78 mmol/kg (UBAE, 60%, 10 mL/g, 10 min) and CTs to 3.34 mmol/kg (UBAE, 60%, 12 mL/g, 10 min). Partial least squares analysis identified X₂ and X₃ as the most influential variables (VIP > 0.8), with energy–time interplay (X₁ × X₃) being critical for antioxidant capacity. Compared to cold-pressing and Soxhlet extraction, PEF and cold-pressing retained higher antioxidant activity (~19 μmol TEAC/kg) and oxidative stability (TBARS ≤ 0.30 mmol MDAE/kg), while Soxhlet—though yielding 55.65% fat—showed the poorest quality profile (Totox value > 560). Both non-thermal techniques can deliver bioactive-rich sesame oil with lower oxidative degradation, supporting their application in functional foods aimed at improving dietary antioxidant intake and mitigating lipid oxidation burden. PEF at high energy/short time and UBAE at low energy/short time present complementary, scalable options for producing high-value edible oils aligned with human health priorities. As a limitation, we did not directly quantify lignans or tocopherols in this study, and future work will address their measurement and bioaccessibility. Full article