Search Results (46,956)

Overhead-line sensor platforms must satisfy electrical and mechanical installation constraints that vary with the operating scenario. This study evaluates separate phase-conductor electrostatic and 9 mm optical ground wire (OPGW) vibration-test scenarios for the same platform. A full Maxwell potential-coefficient matrix provides an analytical reference for a 500 kV line-to-line RMS four-bundle conductor. A three-dimensional COMSOL Multiphysics 6.3 model compares the prototype enclosure scale and fastening-hole configurations. The matrix gives a maximum bare-conductor surface field of 14.33 kV/cm RMS. The phase-RMS values for the hole-free, single-hole, and double-hole cases are 8.05, 12.27, and 12.91 kV/cm RMS, respectively. These values quantify local field enhancement at the hole edge and support geometry comparison. Under a 16.5 kN tensile load, 47.71 Hz vibration, ±2.4 mm cable amplitude, and 1 × 10⁷ cycles, the OPGW test showed no visually detectable slippage or cable damage. The two scenarios provide electrical-geometry and mechanical-interface evidence under the specified analysis and test conditions. Full article

The production of second-generation bioethanol from lignocellulosic biomass requires efficient enzymatic hydrolysis and fermentation processes that remain effective at industrially relevant solids loadings. In this study, bleached Pinus taeda kraft pulp was evaluated as a model substrate for bioethanol production at bench scale (4 L reactor) under high-consistency conditions (12.5–13.9% solids). Three process configurations were compared: separate hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF), and pre-hydrolysis followed by simultaneous saccharification and fermentation (pSSF). Enzymatic hydrolysis in the SHF and SSF configurations stabilized between 54% and 58%, indicating that hydrolysis was the main process bottleneck under the evaluated conditions. In contrast, Saccharomyces cerevisiae efficiently fermented the available glucose, achieving nearly complete conversion of glucose. Among the evaluated strategies, pSSF showed the highest ethanol yield and volumetric productivity, achieving an ethanol yield of 61.8% and a productivity of 0.61 g L⁻¹ h⁻¹. While laboratory-scale SSF experiments conducted at 2% solids achieved complete conversion, the ethanol yield decreased to approximately 58% at the bench scale, highlighting the impact of high-solids operation on process performance. The lower performance observed at high solids may be associated with factors commonly reported during scale-up, including increased slurry viscosity, reduced mixing efficiency, limited enzyme accessibility, and mass-transfer constraints. Overall, the results manifest the need to enhance hydrolysis performance through improved reactor design, more effective mixing strategies, and optimized high-solids processing to facilitate the scale-up of lignocellulosic bioethanol production. Full article

This paper presents an artificial intelligence (AI)-based energy management and control framework for renewable-powered smart microgrids operating under dynamic conditions. The proposed system integrates photovoltaic (PV) generation, battery energy storage, and grid interaction within a MATLAB/Simulink-R2024B environment to improve operational reliability, energy efficiency, and renewable energy utilization. Seven control scenarios were investigated, including baseline operation, Rule-Based Energy Management System (EMS), Proportional–Integral–Derivative (PID), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN)-assisted forecasting, and Reinforcement Learning (RL)-based optimization. Comparative results demonstrate progressive performance improvements with increasing controller intelligence. The RL-based EMS achieved the highest operational cost reduction (95%), voltage regulation performance (95%), battery state-of-charge management (95%), renewable energy utilization (92%), grid dependency reduction (92%), overall system efficiency (95%), and an overall performance score of 95.4%. The ANN forecasting model attained a forecasting accuracy of 96.2%, corresponding to a Mean Absolute Percentage Error (MAPE) of 3.8%, while achieving a Mean Absolute Error (MAE) of 1.84 kW, Root Mean Square Error (RMSE) of 2.37 kW, and coefficient of determination ($R^2$) of 0.982. For voltage regulation, the RL controller reduced the RMSE, settling time, and overshoot to 1.50 V, 1.8 s, and 0.5%, respectively, compared with 20.0 V, 15.0 s, and 10.0% for the baseline case. Ultimately, the results demonstrate that AI-driven control strategies substantially enhance microgrid stability, battery utilization, renewable energy penetration, and operational efficiency, providing a practical and scalable solution for next-generation intelligent microgrids. Full article

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

In the feed industry, energy efficiency is typically assessed using aggregate consumption indicators (kWh/t) or the efficiency of the electric motor in isolation, which makes it impossible to pinpoint where energy is lost along the conversion chain. This study formalizes a three-phase methodology that breaks down the useful electrical efficiency of each Significant Energy Use (SEU) into its successive stages—motor, transmission, and process—based on field-measurable variables, linking electrical conversion with the useful power model of each machine. The process efficiency of hammer mills is normalized using the Swiss Institute of Feed Technology (SFT) reference index; this constitutes a load-sensitive performance ratio, not an absolute thermodynamic efficiency. Its demonstration at the “Piensos Cienfuegos” plant (Cuba), using data from a 2015 industrial campaign, yielded overall efficiencies of 72% for the bucket elevator—conditional on the adopted nominal throughput and nameplate power factor, with a plausible range of 43–89% under coupled systematic-bias scenarios—26–28% for the hammer mills—despite motors operating at 90–92% efficiency—and 17.3% for the screw conveyor (24.7% at the processing stage). Grinding efficiency fell from 31.1% to 9.7% as the throughput of Mill III was reduced from 16 to 5 t/h, corresponding to an increase in normalized shaft-specific energy consumption from 3.50 to 11.2 kWh/t. The expanded measurement uncertainty was 11.8% ($k=2$), and systematic sources of uncertainty were quantified through a sensitivity analysis. The scope of this work is diagnostic: no retrofit or operational intervention was implemented at the plant, and consequently no before–after energy savings are measured or claimed. The reported efficiencies characterize the baseline condition and identify where intervention would be effective. Full article

Greywater treatment is an essential component of sustainable water management strategies, particularly in regions experiencing water scarcity. This study presents a quantitative comparative evaluation of three conventional inorganic coagulants—aluminum sulfate (Al₂(SO₄)₃), ferric sulfate (Fe₂(SO₄)₃), and ferric chloride (FeCl₃)—for domestic greywater treatment using standardized jar test procedures. The initial turbidity of the greywater was 186 ± 5 NTU. Coagulant performance was assessed based on final turbidity, removal efficiency, and pH variation, with dosages expressed as mg of active metal per liter to ensure comparability. Ferric chloride exhibited the highest turbidity removal efficiency, achieving a maximum removal efficiency of 96.2 ± 0.8% at 68.8 mg Fe/L and reducing turbidity to 7 ± 1 NTU. Ferric sulfate achieved a maximum removal efficiency of 92.5 ± 1.2%, while aluminum sulfate reached 84.9 ± 1.4% under the evaluated conditions. Statistical analysis confirmed significant differences among coagulants at intermediate and high dose ranges (p < 0.05). The results indicate that ferric chloride provides the most effective clarification performance for the tested greywater matrix, while maintaining final pH values within a suitable range for non-potable reuse. This study contributes to coagulant selection for decentralized greywater treatment systems through standardized, quantitative comparison under identical operational conditions. Full article

The ReFuelEU Aviation Regulation introduces mandatory targets for sustainable aviation fuels (SAF) from 2025 to 2050. However, hydrotreated esters and fatty acids (HEFA) technology based on waste oils alone is insufficient to meet targets beyond 2030, highlighting the need for alternative biocrude feedstocks to increase SAF production in the EU. Microalgae are promising feedstocks due to their biochemical composition and CO₂-utilization potential, although their high moisture content and nitrogen and oxygen levels require energy-efficient conversion technologies. Hydrothermal liquefaction (HTL) is a suitable process for converting wet microalgal biomass into biocrude, with an optimal temperature window of approximately 300–330 °C and typical biocrude yields ranging from 20 to 70 wt%, depending on feedstock composition and operating conditions. However, microalgal HTL remains at TRL 5–7 and faces challenges related to the high heteroatom content of the resulting biocrude. Hydrodeoxygenation (HDO) is a key upgrading step for converting biocrude into drop-in aviation fuels and commonly operates at approximately 250–400 °C and 10–30 MPa H₂ pressure. Nevertheless, few studies have addressed the HDO of microalgae-derived biocrude. This review examines microalgal HTL, pilot and demonstration facilities, biocrude yields and quality, and upgrading strategies for producing synthetic drop-in aviation biofuels. Full article

We propose a gold-graphene-vanadium dioxide (VO₂) hybrid metasurface capable of reversibly switching among three operational modes in the terahertz regime. The device achieves flexible polarization control by combining the insulator-to-metal phase transition of VO₂ with the electrical tunability of graphene. Simulation results reveal three distinct behaviors depending on the biasing conditions of the materials. With graphene held at a chemical potential of 0 eV and VO₂ in the insulating state, the metasurface acts as a linear-to-linear polarization converter. The polarization conversion ratio (PCR) exceeds 0.9 over the frequency range from 5.5 to 8.6 THz. When the graphene chemical potential is raised to 0.9 eV while VO₂ remains insulating, the metasurface switches to linear-to-circular conversion. Notably, the handedness of the outgoing wave depends on the polarization of the incoming signal. Over the 6.45–8.36 THz band, the axial ratio (AR) remains below 3 dB. A third functional state emerges when VO₂ switches to its metallic phase. In this state, the device simply operates as a broadband co-polarized reflector, covering the terahertz communication band from 0.1 to 10 THz. This switchable, multifunctional behavior should prove useful for terahertz communications, polarization imaging, and sensing applications. Full article

Addressing the safety limitations and operational challenges associated with online particulate matter sampling in high-pressure natural gas pipelines, this paper proposes and systematically investigates a non-intrusive, safe sampling alternative utilizing existing pressure gauge tapping ports. Through experimental and Computational Fluid Dynamics (CFD) numerical analyses, the influence of velocity ratio R (ratio of sampling velocity to pipeline gas velocity), pipeline flow velocity V₀, and port orientation on sampling efficiency η (ratio of sampled concentration to true concentration) was examined. Results show that sampling via a pressure gauge port exhibits a trend contrary to traditional isokinetic sampling: efficiency increases significantly with R. At R = 2, particles below 10 μm are effectively collected, with notably improved capture for larger particles. Flow field analysis reveals that efficiency enhancement originates from a recirculation zone upstream, expansion of the high-pressure zone downstream, and a “wall impaction–bounce–recapture” mechanism under high R conditions. An empirical efficiency formula was established based on experimental data, with prediction errors below 15%. In field tests at a gas transmission station, applying this correction increased the evaluated filtration efficiency from 9.4% to 50.2%, effectively restoring true operating conditions. This study provides a reliable theoretical basis and practical technical solution for safe, convenient, and accurate particulate monitoring in natural gas pipelines. Full article

Ornamental aquaculture represents a high-value segment of global aquaculture and plays an important role in income diversification for small-scale producers. Despite its economic relevance, investment decisions in ornamental fish farming are often supported by limited economic information and rarely incorporate risk and uncertainty into economic assessments. This study developed and applied a stochastic framework to evaluate the economic performance and investment risk of intensive ornamental aquaculture using Betta splendens production as a representative case study. A representative production system was developed from technical and economic surveys conducted on 20 commercial family operated farms located in one of Brazil’s major ornamental fish production clusters. The production system comprised three greenhouse units with an estimated annual output of 162,288 marketable fish. Production costs were estimated using conventional cost-accounting procedures, whereas economic risk was assessed through Monte Carlo simulation incorporating uncertainty in biological, productive, and market variables. The estimated total production cost was US$0.14 fish⁻¹, while the weighted average selling price reached US$0.18 fish⁻¹, resulting in a benefit–cost ratio of 1.33, a profitability of 25.09%, and an annual return on invested capital (ROIC) of 23.90% under the deterministic baseline scenario. Monte Carlo simulation estimated a mean annual ROIC of 22.46%, with a 98.53% probability of positive economic returns. Sensitivity analysis identified labor demand, the selling price of premium males, and reproductive productivity as the principal drivers of economic risk. The results indicate that economic returns in B. splendens farming are primarily influenced by managerial efficiency, labor requirements, and market conditions rather than production volume alone. The proposed framework provides a robust tool for evaluating economic viability and investment risk in ornamental fish farming under uncertainty and may support decision-making in small-scale aquaculture systems. Full article

This study examines how government policy shapes the relationship between specialization strategy and business resilience in Indonesia’s cosmetic raw-materials industry. Drawing on the Resource-Based View and resilience theory, cross-sectional survey data from 189 key informants, each representing one supplier firm, were analyzed using partial least squares structural equation modeling. Specialization Strategy was positively and significantly associated with Business Resilience (β = 0.271, t = 5.286, p < 0.001), and Government Policy also showed a positive direct association with Business Resilience (β = 0.240, t = 4.421, p < 0.001). The interaction between Government Policy and Specialization Strategy was negative and significant (β = −0.089, t = 4.605, p < 0.001), indicating that stronger perceived policy support attenuated, rather than reversed, the positive relationship between Specialization Strategy and Business Resilience. The findings therefore indicate a dual policy role: Government Policy was positively associated with Business Resilience overall, while higher perceived policy support reduced the marginal contribution of Specialization Strategy to resilience. The study contributes to strategic management and administrative science by integrating firm-specific capabilities with external policy conditions in an import-dependent emerging economy. The findings also suggest that industrial policy may be more effective when support instruments are differentiated, adaptive, and aligned with the strategic and operational characteristics of affected firms. Full article

A fluid–structure interaction (FSI) model of the guide vane and runner of a pump-turbine was developed by applying unsteady hydraulic pressure loads obtained from CFD simulations to the structural surfaces. The deformation behavior, stress distribution, and modal response of the stay vane, movable guide vane, and runner were investigated under different operating conditions. The maximum deformation of the stay vane occurs at the middle section of the blade leading edge, whereas the maximum deformation of the movable guide vane is located near the trailing edge. The maximum deformation of the runner occurs at the junction between the blade leading edge and the band. Modal analysis shows that the fourth natural frequency of the movable guide vane approaches the eighth-order guide-vane passing frequency, while the sixth natural frequency of the runner approaches the fourth-order runner blade-passing frequency, suggesting a potential risk of vibration amplification. Full article

The Controllable Line-Commutated Converter (CLCC) integrates fully controlled and semi-controlled devices to mitigate commutation failure. However, its application in large-capacity HVDC systems is constrained by the limited current-carrying capability of fully controlled valves in the main branch. To address the HVDC requirements under high-current conditions, this paper proposes a high-reliability cascaded H-bridge CLCC (H-CLCC) valve topology. The proposed topology employs a dual-path conduction mode for H-bridge sub-valves, reducing electrical stress on devices and enabling modular scalability. A redundant configuration, in which cascaded H-bridges are paralleled with bypass thyristors, allows faulty sub-modules to be rapidly bypassed, ensuring continuous operation. An analytical model based on the Laplace transform is developed to reveal the relationship between capacitor voltage and turn-off current, providing guidance for capacitance design. PSCAD/EMTDC simulations verify that the H-CLCC valve effectively suppresses commutation failure via active commutation, even under severe AC-side faults with currents up to 8 kA. Device-failure simulations further demonstrate strong self-healing capability, ensuring sustained forced commutation under local faults. This work provides a foundation for high-reliability UHVDC converter valve design. Full article

The development of titanium-based surface alloys and coatings that combine extreme hardness with sufficient toughness remains a major challenge for components operating under severe friction and wear conditions. In this work, titanium–boride composite coatings were synthesized on commercially pure titanium by scanning electron beam surface alloying of preplaced boron. The influence of beam power (900, 1200, and 1500 W) on phase formation, microstructural evolution, and mechanical performance was systematically investigated. At 900 W, insufficient melting resulted in chemically and structurally heterogeneous coatings containing unreacted boron. Increasing the beam power to 1200 W promoted the formation of TiB and TiB₂ phases, leading to a maximum microhardness of approximately 5500 HV_0.2. At 1500 W, complete boron incorporation produced a graded architecture consisting of a Ti/TiB surface layer and a TiB₂-rich sublayer. This hierarchical microstructure exhibited a favorable combination of high hardness and the lowest coefficient of friction (0.21), representing a reduction of more than 50% compared with the untreated titanium substrate. These findings establish a clear relationship between electron beam processing conditions, microstructural development, and mechanical performance, demonstrating that scanning electron beam surface alloying is an effective strategy for tailoring high-performance Ti–B composite surfaces. The developed coatings show strong potential for aerospace and other advanced engineering applications requiring lightweight materials with high hardness and low friction. Full article

Accurately predicting transportation accident severity is critical for targeted risk governance, yet research based on accident investigation reports is often hampered by small sample sizes and skewed class distributions. This study develops a dual-track feature fusion and interpretable prediction framework to overcome these constraints. More than 1000 candidate documents were screened, yielding a reconstructed analytical sample of 157 eligible accident investigation reports for a three-class accident severity classification task. The methodology integrates HFACS-Lite vertical hierarchy and DEMATEL-Lite horizontal coupling to construct high-order fused features, employing the TabPFN foundation model as the backbone learner alongside SMOTENC and post hoc dual-threshold adjustments. Empirical results show that the final SMOTENC-enhanced dual-track TabPFN achieved an accuracy of 0.783 and a Macro-F1 of 0.717, while post hoc dual-threshold adjustment increased major-and-above recall to 0.6429. SHAP attribution indicates that high-consequence accidents are associated with joint patterns of micro-level operations, operating scenarios, and safety governance. The proposed framework supports association-based severity classification and risk screening under constrained data conditions. Full article