Search Results (7,926)

Phosphorus is an essential resource for numerous industrial applications. However, its uneven global distribution makes Europe heavily dependent on imports. Recovering phosphorus from waste streams is therefore crucial for improving resource security. The FlashPhos project addresses this challenge by developing a process to recover phosphorus from sewage sludge, in which phosphorus-rich slag is produced in a flash reactor and subsequently reduced in a Submerged Arc Furnace (SAF). In this process, approximately 250 kg/h of sewage sludge is converted into slag, which is further processed in the SAF to recover about 8 kg/h of white phosphorus. This work focuses on the development of a computational model of the SAF, with particular emphasis on slag behaviour. Due to the extreme operating conditions, which severely limit experimental access, a numerically efficient three-dimensional CFD model was developed to investigate the internal flow of the three-phase, AC-powered SAF. The model accounts for multiphase interactions, dynamic bubble generation and energy sinks associated with the reduction reaction, and Joule heating. A temperature control loop adjusts electrode currents to reach and maintain a prescribed target temperature. To further reduce computational cost, a novel simulation approach is introduced, achieving a reduction in simulation time of up to 300%. This approach replaces the solution of the electric potential equation with time-averaged Joule-heating values obtained from a preceding simulation. The system requires transient simulation and reaches a pseudo-steady state after approximately 337 s. The results demonstrate effective slag mixing, with gas bubbles significantly enhancing flow velocities compared to natural convection alone, leading to maximum slag velocities of 0.9–1.0 m/s. The temperature field is largely uniform and closely matches the target temperature within ±2 K, indicating efficient mixing and control. A parameter study reveals a strong sensitivity of the flow behaviour to the slag viscosity, while electrode spacing shows no clear influence. Overall, the model provides a robust basis for further development and future coupling with the gas phase. Full article

Energy-efficient driving is essential for reducing the environmental impacts of road transport, especially for electric passenger vehicles. This research aims to build a data-driven behavioral analysis and energy-consumption evaluation model. The model relies on sensor data from the vehicle’s on-board communication network, primarily the CAN (Controller Area Network) bus. We analyze patterns of key powertrain and battery parameters—such as current, voltage, state of charge (SoC), and power—in relation to driver inputs, such as the accelerator pedal position. In the first stage, we review the literature with a focus on machine learning and clustering methods used in behavioral and energy analysis. We also examine the role of on-board telemetry systems. Next, we develop a controlled measurement architecture. It defines reference consumption maps from dynamometer data across operating points and environmental variables, including SoC, temperature, and load. The longer-term goal is a multidimensional behavioral map and profiling framework that can predict energy efficiency from real-time driver inputs. This work lays the foundation for a future system with adaptive, feedback-based driver support. Such a system can promote intelligent, sustainable, and behavior-oriented mobility solutions. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through filtering, and digital data transmission via Arduino and Bluetooth, the instantaneous line currents are visualized in MATLAB (R2023a) as time-based curves. The sensor box can also be remotely released from the transmission line once measurements are complete, allowing a fully autonomous mode of operation. Laboratory tests demonstrated promising results for real-world applications, with measurement efficiencies ranging from 92% to 98% under various test conditions, including stress tests involving harmonics and total harmonic distortion up to 40%. Future work will focus on implementing effective shielding against high electric fields to further enhance reliability and advance the sensor’s industrialization as a novel solution for power grid digitalization. Full article

Wide-bandgap (WBG) semiconductor materials such as silicon carbide (SiC) and gallium nitride (GaN) are key enablers of power-electronics converters for aerospace platforms, where high efficiency, weight reduction, and thermal robustness are critical requirements. This paper presents the main challenges associated with the use of these technologies, including protection requirements, electromagnetic compatibility, and thermal management, as well as the material advantages that enable higher switching frequencies and lower losses compared to conventional Si technologies. A comparative analysis of semiconductor technologies and suitable power-conversion topologies for the aerospace context is provided. Representative laboratory-scale experimental validation is presented, including the development of a DC–DC boost converter and a DC–AC full-bridge inverter, which are linked through the common DC-link and are used for interfacing batteries and an electrical motor, both based on GaN and SiC diodes. The results demonstrated the correct operation, with stable high-frequency performance under controlled laboratory conditions, supporting aerospace-oriented development, although evaluated in a laboratory environment, confirming the potential of WBG technologies for future power-conversion architectures. Full article

While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an MPV, produced by a single manufacturer on a shared platform. Leveraging detailed bills of materials, plant-level energy data, and region-specific emission factors for a functional unit of 150,000 km, we quantify greenhouse gas emissions across the full life cycle. Results show the total emissions scale with vehicle size from 25 to 31 t CO₂-eq. However, the MPV exhibits the highest functional carbon efficiency, with the lowest emissions per unit of interior volume. Material production and operational electricity use dominate the emission profile, with end-of-life metal recycling providing a 15–20% mitigation credit. Scenario modeling reveals that grid decarbonization can slash life cycle emissions by around 30%, while advanced battery recycling offers a further 15–18% reduction. These findings highlight that the climate benefits of BEVs are closely linked to progress in power system decarbonization, and provide references for future optimization of low-carbon vehicle production and reuse. Full article

Hexagonal boron nitride nanosheets (BNNSs) have emerged as one of the most promising materials for next-generation thermal management, driven by the intensifying heat dissipation demands of highly integrated electronics. While conventional polymer-based packaging materials are lightweight and electrically insulating, their intrinsically low thermal conductivity severely limits effectiveness in high-power devices. The remarkable thermal transport, wide bandgap, chemical robustness, and mechanical strength of BNNSs offer a compelling solution. This review provides a comprehensive overview of the structural and physical foundations that underpin the anisotropic yet exceptional thermal properties of bulk h-BN and BNNSs. We examine major synthesis routes including tape exfoliation, ball milling, liquid-phase exfoliation, chemical vapor deposition, and metal–organic chemical vapor deposition, highlighting how process mechanisms govern nanosheet thickness, defect density, crystallinity, and scalability. Particular emphasis is placed on the advantages of BNNSs in thermal management systems, from their use as high-efficiency thermally conductive fillers and advanced thermal interface materials. We conclude by examining key challenges including large-area growth, filler alignment, and interfacial engineering, and by presenting future research directions that could enable the practical deployment of BNNSs-based thermal management technologies. Full article

Against the backdrop of China’s dual-carbon goals, traditional road transportation has relatively high carbon emissions and is in urgent need of a low-carbon transition. The intermittency of photovoltaic (PV) power generation and the stochastic nature of electric vehicle (EV) charging demand introduce significant uncertainty for PV-energy storage-charging systems in highway service areas. Existing approaches often struggle to balance economic efficiency and reliability. This study develops a min-max-min robust optimization model for a full-route PV-energy storage-charging system. A box uncertainty set is used to characterize uncertainties in PV output and EV load, and a tunable uncertainty parameter is introduced to regulate risk. The model is solved using a column-and-constraint generation (C&CG) algorithm that decomposes the problem into a master problem and a subproblem. Strong duality, combined with a big-M formulation, enables an alternating iterative solution between the master problem and the subproblem. Simulation results demonstrate that the proposed algorithm attains the optimal solution and, relative to deterministic optimization, achieves a desirable trade-off between economic performance and robustness. Full article

The heat generated by electrical cables during operation dissipates through the duct and surrounding encasement concrete into the adjacent soil. Consequently, the thermal conductivity of the encasement concrete is critical for the overall heat dissipation efficiency and thermal stability of the cable system. To address this, a high-thermal-conductivity concrete was developed by incorporating steel slag powder, graphite powder, and graphite particles. This study systematically investigated the effects of mineral admixture content, graphite powder dosage, and the replacement ratio of conductive aggregates on the thermal conductivity, mechanical properties, and workability of the concrete. Additionally, the economic performance was evaluated against conventional C25 concrete using value engineering methodology. The results demonstrate that the proposed concrete exhibited significantly improved thermal performance compared to ordinary concrete. Notably, with a 10% replacement of aggregates by graphite particles, the 28-day thermal conductivity increased by 106.16% relative to the control mix. Simultaneously, the compressive strength reached 25.1 MPa, ensuring sufficient mechanical integrity. Furthermore, the value coefficient of the developed concrete was 18.31% higher than that of conventional C25 concrete. These findings highlight the material’s potential in power engineering and energy-efficient construction, providing a valuable reference for material design in these fields. Full article

The modular multi-active bridge (MMAB) converter—characterized by electrical isolation, modular design, high power density, and high efficiency—can be readily scaled to multiple DC ports through an internal shared high-frequency bus (HFB), establishing it as a viable topology for DC transformer (DCT) applications. However, its interconnection to a DC grid via low-damping inductors may provoke low-frequency oscillations and instability. To mitigate this issue, this paper employs a pole-zero cancellation approach to model the conventional constant-power control (CPC) loop as a second-order system, thereby elucidating the relationship between equivalent line impedance and stability. An active damping control strategy based on virtual impedance is then introduced, supported by systematic design guidelines for the damping compensation stage. Simulation and experimental results confirm that under weak damping conditions, the proposed method raises the damping coefficient to 0.707 and effectively suppresses low-frequency oscillations—all without altering physical line impedance, introducing additional power losses or requiring extra sensing devices—thereby markedly improving grid-connected stability. Full article

A vertical Ni/β-Ga₂O₃ Schottky barrier diode was fabricated on an unintentionally doped bulk (−201)-oriented β-Ga₂O₃ single crystal and investigated with a focus on the underlying photoresponse mechanisms. The device exhibits well-defined rectifying behavior, characterized by a Schottky barrier height of 1.63 eV, an ideality factor of 1.39, and a high rectification ratio of ~9.7 × 10⁶ arb. un. at an applied bias of ±2 V. The structures demonstrate pronounced sensitivity to deep-ultraviolet radiation (λ ≤ 280 nm), with maximum responsivity observed at 255 nm, consistent with the wide bandgap of β-Ga₂O₃. Under 254 nm illumination at a power density of 620 μW/cm², the device operates in a self-powered mode, generating an open-circuit voltage of 50 mV and a short-circuit current of 47 pA, confirming efficient separation of photogenerated carriers by the built-in electric field of the Schottky junction. The responsivity and detectivity of the structures increase from 0.18 to 3.87 A/W and from 9.8 × 10⁸ to 4.3 × 10¹¹ Hz^0.5cmW⁻¹, respectively, as the reverse bias rises from 0 to −45 V. The detectors exhibit high-speed performance, with rise and decay times not exceeding 29 ms and 59 ms, respectively, at an applied voltage of 10 V. The studied structures demonstrate internal gain, with the external quantum efficiency reaching 1.8 × 10³%. Full article

This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods. Full article

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%. Full article

Microbial fuel cell (MFC) technology represents a promising bioelectrochemical approach for the simultaneous generation of electricity and treatment of high-strength wastewater. However, the performance of MFCs strongly depends on the metabolic potential and synergistic interactions of the inoculated microbial community. This study evaluated the electrochemical activity and COD removal efficiency of three individual bacterial strains, Lysinibacillus sphericus A1, Bacillus cereus A2 and Lactococcus lactis A4, compared with a developed consortium under long-term operation using poultry slaughterhouse wastewater as a substrate. All inocula were tested in dual-chamber MFCs for 30 days, and performance indicators included power output, voltage, and removal of chemical oxygen demand (COD). The consortium showed the highest power of 170 mW/m² and the optimal voltage–current ratio at a current of 900 mA/m² and 245 mV under decreasing external resistance from 1000 to 50 Ω. The highest COD removal (84.4%) was also recorded, surpassing all pure cultures and demonstrating a significant improvement compared with B. cereus A2 and L. lactis A4. Meanwhile, the lowest power of 52 mA/m² was recorded during testing of L. lactis A4, at 650 mA/m² and 120 mV. Compared with single cultures, the consortium produced approximately 15% higher power density than L. sphericus A1, about 29% higher than B. cereus A2, and more than threefold higher than L. lactis A4. This study highlights the potential of a consortium as an efficient biocatalyst for MFC-mediated wastewater treatment and suggests that selecting complementary strains with diverse metabolic functions can substantially improve system performance. Full article