Search Results (2,516)

Melt crystallization is a promising separation technique that produces ultra-high-purity products while consuming less energy and generating lower CO₂ emissions than conventional methods. However, accurately modeling melt crystallization is challenging due to significant non-idealities and complex phase equilibria in multicomponent systems. This study develops and evaluates two neural network-based surrogate models for acrylic acid melt crystallization: a stand-alone (black-box) model and a thermodynamically guided (hybrid) model. The hybrid model incorporates UNIQUAC-based solid–liquid equilibrium constraints into the learning process. This framework combines first-principles thermodynamic knowledge—particularly activity coefficient calculations and mass balance equations—with multi-output regression to predict key process variables. Both models are rigorously tested for interpolation and extrapolation, with the hybrid approach demonstrating superior accuracy even under operating conditions significantly outside the training domain. Further analysis reveals the critical importance of accurate solid–liquid equilibrium (SLE) data for thermodynamic parameterization. A final case study illustrates how the hybrid approach can quickly explore feasible operating regions while adhering to strict product purity targets. These findings confirm that integrating mechanistic constraints into neural networks significantly enhances predictive accuracy, especially when processes deviate from nominal conditions, providing a practical framework for designing and optimizing industrial-scale melt crystallization processes. Full article

Addressing the global energy demand requires the development of sustainable and highly efficient technologies for clean energy generation. One of the primary challenges in the oxygen evolution reaction (OER) is overcoming sluggish reaction kinetics, which requires the design of electrocatalysts with greater activity and long-term stability. In this study, a precipitation method was employed to synthesize polyethylene glycol (PEG) assisted tungsten oxide (WO₃) as an effective and stable electrocatalyst for OER. PEG was incorporated at varying concentrations (1%, 3%, and 5%) to modulate the structural and electrochemical characteristics of WO₃. Among the resulting composites, the sample with 3% PEG (PEG-WO₃-2) exhibited the most favorable catalytic behavior, achieving a low overpotential of 407.7 mV at a current density of 10 mA cm⁻² and a Tafel slope of 76.2 mV dec⁻¹ in 1 M KOH electrolyte. Furthermore, long-term electrochemical stability was evaluated over 5000 consecutive cycles, revealing minimal degradation in catalytic activity. The heightened performance is attributed to the optimized composition, improved electron transport properties, and the presence of a higher density of active sites, all of which contribute to the superior catalytic activity of the PEG-WO₃-2 electrocatalyst. Full article

Effective natural lighting in university library reading areas significantly influences users’ visual comfort, task performance, and energy efficiency. However, existing library lighting designs often exhibit problems such as uneven illumination, excessive glare, and underutilization of natural daylight. To address these challenges, this study proposes a multi-objective optimization framework for library lighting design based on the NSGA-II algorithm. The framework targets the following three key objectives: improving illuminance uniformity, enhancing visual comfort, and reducing lighting energy consumption. The optimization process incorporates four critical visual comfort parameters—desktop illuminance, correlated color temperature, background reflectance, and screen luminance—whose weights were determined using the analytic hierarchy process (AHP) with input from domain experts. A parametric building information model (BIM) was developed in Revit, and lighting simulations were conducted in DIALux Evo to evaluate different design alternatives. Experimental validation was carried out in an actual library setting, with illuminance data collected from five representative measurement points. The results showed that after optimization, lighting uniformity improved from less than 0.1 to 0.6–0.75, glare values (UGR) remained below 22, and daylight area coverage increased by 25%. Moreover, lighting energy consumption was reduced by approximately 20%. Statistical analysis confirmed the significance of the improvements (p < 0.001). This study provides a systematic and reproducible method for optimizing natural lighting in educational spaces and offers practical guidance for energy-efficient and user-centered library design. Full article

Due to their improved performance and advantages for the environment, fuel cell hybrid electric cars, or FCEVs, have garnered a lot of attention. Establishing an energy management strategy (EMS) for fuel cell electric vehicles (FCEVs) is essential for optimizing power distribution among various energy sources. This method addresses concerns regarding hydrogen utilization and efficiency. The Arithmetic Optimization Algorithm is employed in the proposed energy management system to enhance the strategy of maximizing external energy, leading to decreased hydrogen consumption and increased system efficiency. The performance of the proposed EMS is evaluated using the Federal Test Procedure (FTP-75) to replicate city driving situations and is compared with existing algorithms through a comparison co-simulation. The co-simulation findings indicate that the suggested EMS surpasses current approaches in reducing fuel consumption, potentially decreasing it by 59.28%. The proposed energy management strategy demonstrates an 8.43% improvement in system efficiency. This enhancement may reduce dependence on fossil fuels and mitigate the adverse environmental effects associated with automobile emissions. To assess the feasibility and effectiveness of the proposed EMS, the system is tested within a Processor-in-the-Loop (PIL) co-simulation environment using the C2000 launchxl-f28379d Digital Signal Processing (DSP) board. Full article

Background: The transition to low-carbon economies has become a global priority, particularly in sectors with high greenhouse gas emissions, such as maritime transport. Renewable fuels, especially methanol, have emerged as promising alternatives to conventional fossil fuels due to their potential to reduce carbon footprints and contribute to sustainable logistics systems. Methods: This study employs a combined qualitative and quantitative approach to assess the impact of renewable fuel production on maritime transport decarbonization. The analysis integrates economic feasibility, energy efficiency, and environmental benefits, providing a comprehensive evaluation of methanol’s role in reducing emissions. Results: Findings indicate that methanol offers significant potential for the decarbonization of maritime transport. Its relatively low production costs and high energy density position it as a viable alternative to traditional fuels. Additionally, the study highlights the substantial reduction in greenhouse gas emissions that methanol adoption could achieve, reinforcing its role in mitigating climate change effects. Conclusions: The study concludes that integrating methanol as a primary fuel in maritime transport can accelerate the sector’s decarbonization. However, successful implementation depends on supportive policy regulations and further research to optimize production and supply chain integration. The findings emphasize the strategic importance of renewable fuels in developing sustainable and resilient logistics systems. Full article

With the widespread adoption of highways in the mountainous regions of southwestern China, the electricity load of numerous tunnels and service areas has increased rapidly. Constructing photovoltaic (PV) microgrids in service areas has become an important means of energy conservation, consumption reduction, and carbon emission mitigation. However, constrained by mountainous terrain, the PV power generation conditions in highway service areas exhibit significant micro-terrain variations, making it difficult to effectively evaluate PV utilization efficiency. This paper proposes a dynamic block optimization model for PV microgrids that considers regional layout constraints. The model utilizes an intelligent adjustment mechanism to plan PV panel layouts in highway service areas, optimizing energy utilization efficiency and economic benefits. Additionally, long short-term memory (LSTM) networks are employed for short-term PV output prediction to address the challenges posed by varying weather and seasonal changes. This approach comprehensively considers the intermittency and instability of PV power generation, enabling dynamic block optimization to autonomously adjust the PV power output in response to load fluctuations. Through simulation case studies, the model is validated to effectively improve the utilization rate and economic performance of PV microgrids under various environmental conditions and demonstrates superior performance compared with traditional static block methods. Full article

To address the performance and power concerns in Double-Data-Rate SDRAM (DDR) subsystems, this paper presents an innovative method for the DDR memory controller scheduler. This design aims to strike a balance between power consumption and performance for the DDR subsystem. Our approach entails a critical reassessment of established mechanisms and the introduction of a quasi-static arbitration protocol for the DDR’s low-power mode (LPM) transition processes. Central to our proposed DDR power-management framework is the Low-Power-Management Engine (LPME), complemented by a suite of statistical algorithms tailored for implementation within the architecture. Our research strategy encompasses real-time monitoring of the DDR subsystem’s operational states, traffic intervals, and Quality of Service (QoS) metrics. By dynamically fine-tuning the DDR subsystem’s power-management protocols to transition in and out of identical power modes, our method promises substantial enhancements in both energy efficiency and operational performance across a spectrum of practical scenarios. To substantiate the efficacy of our proposed design, an array of experiments was conducted. These rigorous tests evaluated the DDR subsystem’s performance and energy consumption under a diverse set of workloads and system configurations. The findings are compelling: the LPME-driven architecture delivers significant power savings of over 41%, concurrently optimizing performance metrics like latency increase by no more than 22% in a high-performance operational context. Full article

The steel industry, as a large-scale equipment-intensive sector, emphasizes the importance of maintaining and managing equipment without failure. In line with the recent Fourth Industrial Revolution, there is a growing shift from preventive to predictive maintenance (PdM) strategies for cost-effective equipment management. This study aims to develop a PdM model for the Run-Out Table (ROT) equipment in hot rolling mills of steel plants, utilizing artificial intelligence (AI) technology, and to propose methods for contributing to energy efficiency through this model. Considering the operational data characteristics of the ROT equipment, an autoencoder (AE), capable of detecting anomalies using only normal data, was selected as the base model. Furthermore, Long Short-Term Memory (LSTM) networks were chosen to address the time-series nature of the data. By integrating the technical advantages of these two algorithms, a predictive maintenance model based on the LSTM-AE algorithm, named the Run-Out Table Predictive Maintenance Model (ROT-PMM), was developed. Additionally, the concept of an anomaly ratio was applied to identify equipment anomalies for each coil production. The performance evaluation of the ROT-PMM demonstrated an F1-score of 91%. This study differentiates itself by developing an optimized model that considers the specific environment and large-scale equipment operation of steel plants, and by enhancing its applicability through performance verification using actual failure data. Furthermore, it emphasizes the importance of PdM strategies in contributing to energy efficiency. It is expected that this research will contribute to increased energy efficiency and productivity in industrial settings, including the steel industry. Full article

Background/Objectives: Calcific tendinopathy of the shoulder is a degenerative condition characterized by calcium deposits within the rotator cuff tendons, particularly the supraspinatus. It is a frequent cause of chronic shoulder pain and functional limitation, adversely affecting quality of life. While conservative treatments such as nonsteroidal anti-inflammatory drugs (NSAIDs), physiotherapy, and corticosteroid injections are commonly used, extracorporeal shock wave therapy (ESWT) has emerged as a promising non-invasive alternative. This interventional clinical trial compared the efficacy of ultrasound-guided versus landmark-based ESWT in treating calcific tendinopathy. Methods: Eighty-four patients with ultrasound-confirmed calcific tendinopathy were randomized into two groups. Group 1 received ultrasound-guided ESWT with real-time targeting of the deposit; Group 2 received landmark-based ESWT based on anatomical palpation. Both groups underwent three sessions (2000 impulses at 2.2 bars, energy level 5, 8 Hz). Clinical outcomes were assessed using the Constant–Murley score (CMS) at baseline, 12 weeks, and 6 months. Calcific deposit resorption was evaluated via ultrasound imaging. Results: The ultrasound-guided group showed a significant improvement in CMS from a median of 50 (range: 30–75) at baseline to 97 (52–100) at 6 months. The landmark-based group also improved, from 48 (32–74) to 79 (40–96). At 6 months post-treatment, 90.9% of patients in the ultrasound-guided group achieved successful outcomes (CMS ≥ 86), compared to 50% in the landmark-based group. Complete calcific resorption occurred in 65.9% of patients in Group 1, compared to 50% in Group 2; 15% of patients in Group 2 showed no resorption. Conclusions: Ultrasound-guided ESWT was significantly more effective than landmark-based ESWT in improving shoulder function, reducing pain, and promoting calcific deposit resorption. These findings support ultrasound guidance as a preferred approach for optimizing ESWT outcomes in patients with calcific tendinopathy of the shoulder. Full article

Water scarcity under climate change and increasingly stringent water conservation policies may trigger energy security concerns. The current study develops an optimization model to investigate the impacts of water conservation policies and hydrological uncertainties on the regional energy transition process in Shanxi Province, China. The dual-control policies on total water consumption and water intensity are systematically examined for their differential constraints and stimulative effects on various power generation types. Hydrological time series analysis methods are employed to project future water resource variations in Shanxi Province and evaluate their implications for power system optimization. The results indicate that (1) total water constraint policies are more stringent than water intensity constraint policies; (2) changes in water resource availability impose greater restrictions on coal power development than those imposed by current water conservation policies; and (3) when total water resources decrease by approximately 43.5% compared with 2020 levels, Shanxi Province may face electricity shortages. These findings suggest that water conservation policy formulation should be coordinated with regional power sector development planning, while also considering potential energy security risks posed by potential future reductions in water resources. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

In collaborative operations of multiple autonomous underwater vehicles (AUVs), the complexity of underwater environments and limited onboard energy make environmental adaptation and energy efficiency critical metrics for evaluating path quality. This paper addresses path conflict resolution in multi-AUV path planning by proposing an equal-time waypoint planning method. The approach involves randomly selecting equal-time waypoints in free space and generating path encoding sequences for each AUV. These path encodings are then optimized through four modules, considering both path smoothness and adaptability to ocean currents. The resulting paths comply with kinematic constraints while achieving reduced energy consumption. The method enables velocity adjustments across different segments to prevent conflicts. Simulation results demonstrate the feasibility of this approach in resolving multi-AUV path conflicts with low energy expenditure. Full article

Quaternary NiCoMoB electrocatalysts exhibited significantly enhanced OER performance compared to their ternary NiCoMo and NiCoB counterparts. An optimal Mo/B ratio of 1 (NiCoMo_yB_y) demonstrated a superior OER activity, attributed to a balance between the electronic and structural contributions from Mo and B, maximizing the electrocatalytic site density and activity. NiCoMo_yB_y-SA, a nanoparticle version synthesized via a surfactant-assisted method, showed further improved performance. The OER activity was evaluated by comparing overpotentials at 10 mA/cm², with NiCoMo_xB_1−x, NiCoMo_yB_y, and NiCoMo_yB_y-SA exhibiting 293, 284, and 270 mV, respectively. NiCoMo_yB_y-SA also demonstrated the lowest onset potential (1.45 V), reflecting a superior efficiency. Chronoamperometry in 1 M pre-electrolyzed KOH at 30 °C highlighted NiCoMo_yB_y-SA’s stability, activating within hours at 10 mA/cm² and stabilizing over 7 days. At 50 mA/cm², the overpotential increased minimally (0.02 mV/h over 2 days), and even at 100 mA/cm² for 10 days, the activity declined only slightly, affirming a high stability. These findings demonstrate NiCoMoB electrocatalysts as cost-effective, efficient OER electrocatalysts, advancing sustainable energy technologies. Full article

This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, including slurry flow rate, temperature, pressure, residence time, and the external heat transfer coefficient, on the overall efficiency of biocrude oil production. A detailed thermodynamic evaluation was conducted using process simulation principles and a kinetic model to assess mass and energy balances within the HTL reaction, considering heat and mass momentum exchange in a multiphase system using UDF. The reactor’s receiver, a copper absorber tube, has a total length of 20 m and is designed in a coiled configuration from the base to enhance heat absorption efficiency. To optimize the thermal performance of biomass conversion in the HTL process, a Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) coupling numerical method approach was employed to investigate improved thermal performance by obtaining a heat source solely through solar energy. This numerical modeling approach allows for an in-depth assessment of heat transfer mechanisms and fluid-particle interactions, ensuring efficient energy utilization and sustainable process development. The findings contribute to advancing solar-driven HTL technologies by maximizing thermal efficiency and minimizing external energy requirements. Full article

The current world is increasingly focusing on renewable energy sources with strong emphasis on the economically viable use of renewable energy to reduce carbon emissions and safeguard human health. Solid-state hydrogen (H₂) storage materials offer a higher density compared to traditional gaseous and liquid storage methods. In this context, this review evaluates recent advancements in binary, ternary, and complex metal hydrides integrated with 2D Ti₃C₂ MXene for enhancing H₂ storage performance. This perspective highlights the progress made in H₂ storage through the development of active sites, created by interactions between multilayers, few-layers, and internal edge sites of Ti₃C₂ MXene with metal hydrides. Specifically, the selective incorporation of Ti₃C₂ MXene content has significantly contributed to improvements in the H₂ storage performance of various metal hydrides. Key benefits include low operating temperatures and enhanced H₂ storage capacity observed in Ti₃C₂ MXene/metal hydride composites. The versatility of titanium multiple valence states (Ti⁰, Ti²⁺, Ti³⁺, and Ti⁴⁺) and Ti-C bonding in Ti₃C₂ plays a crucial role in optimizing the H₂ absorption and desorption processes. Based on these promising developments, we emphasize the potential of solid-state Ti₃C₂ MXene interfaces with various metal hydrides for fuel cell applications. Overall, 2D Ti₃C₂ MXenes represent a significant advancement in realizing efficient H₂ storage. Finally, we discuss the challenges and future directions for advancing 2D Ti₃C₂ MXenes toward commercial-scale H₂ storage solutions. Full article