Search Results (15,424)

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations. Full article

Artificial intelligence (AI) often suffers from high energy consumption and complex deployment in resource-constrained environments, leading to a structural mismatch between capability and deployability. This review takes two representative scenarios—energy-first and performance-first—as the main thread, systematically comparing cloud, edge, and fog/cloudlet/mobile edge computing (MEC)/micro data center (MDC) architectures. Based on a standardized literature search and screening process, three categories of miniaturization strategies are distilled: redundancy compression (e.g., pruning, quantization, and distillation), knowledge transfer (e.g., distillation and parameter-efficient fine-tuning), and hardware–software co-design (e.g., neural architecture search (NAS), compiler-level, and operator-level optimization). The purposes of this review are threefold: (1) to unify the “architecture–strategy–implementation pathway” from a system-level perspective; (2) to establish technology–budget mapping with verifiable quantitative indicators; and (3) to summarize representative pathways for energy- and performance-prioritized scenarios, while highlighting current deficiencies in data disclosure and device-side validation. The findings indicate that, compared with single techniques, cross-layer combined optimization better balances accuracy, latency, and power consumption. Therefore, AI miniaturization should be regarded as a proactive method of structural reconfiguration for large-scale deployment. Future efforts should advance cross-scenario empirical validation and standardized benchmarking, while reinforcing hardware–software co-design. Compared with existing reviews that mostly focus on a single dimension, this review proposes a cross-level framework and design checklist, systematizing scattered optimization methods into reusable engineering pathways. Full article

Developing bifunctional electrocatalysts that simultaneously enable green hydrogen production and water purification is essential for advancing sustainable energy and environmental technologies. In this study, we present Cu@Pd core–shell nanostructures fabricated through template-assisted electrodeposition of Cu, followed by galvanic Pd modification on pyrolytic graphite electrodes (PGEs). The optimised catalyst exhibited superior hydrogen evolution reaction (HER) activity, with an onset potential of 70 mV, a low Tafel slope of 33 mV dec⁻¹ and excellent stability during prolonged HER operation. In addition to hydrogen evolution, Cu@Pd/PGE shows significantly enhanced nitrate reduction reaction (NRR) activity compared to Cu/PGE in both alkaline and neutral conditions. Under ideal conditions, the catalyst achieved 60% nitrate removal with high selectivity towards ammonia and minimal nitrite formation, emphasising its superior performance. This enhanced bifunctionality arises from the synergistic Cu–Pd interface, facilitating efficient nitrate adsorption and selective hydrogenation. Despite their high catalytic activity for both HER and NRR, the Cu@Pd nanostructures could often emerge as a versatile platform for integration into sustainable hydrogen production and an effective denitrification process. Full article

Urbanization generates large quantities of arsenic-contaminated excavated soils that pose environmental risks due to arsenic volatilization during high-temperature sintering processes. While these soils have potential for recycling into construction materials, their reuse is hindered by arsenic release. This study demonstrated calcium hydroxide (Ca(OH)₂) as a highly effective additive for suppressing arsenic volatilization during soil sintering, while simultaneously improving material properties. Through comprehensive characterization using inductively coupled plasma-mass spectrometry (ICP-MS), scanning electron microscopy (SEM) and X-ray microtomography (μCT), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS), results demonstrated that Ca(OH)₂ addition (0.5–2 wt.%) reduces arsenic volatilization by 57% through formation of thermally stable calcium arsenate (Ca₃(AsO₄)₂). Ca(OH)₂ acted via two mechanisms: (a) chemical immobilization through Ca-As-O compound formation, (b) physical encapsulation in a calcium-aluminosilicate matrix during liquid-phase sintering, and (c) pH buffering that maintains arsenic in less volatile forms. Optimal performance was achieved at 0.5% Ca(OH)₂, yielding 9.14 MPa compressive strength (29% increase) with minimal arsenic leaching (<110 ppb). Microstructural analysis showed Ca(OH)₂ promoted densification while higher doses increased porosity. This work provides a practical solution for safe reuse of arsenic-contaminated soils, addressing both environmental concerns and material performance requirements for construction applications. Full article

Deep retrofits are one of the few pathways to decarbonize the existing building stock while simultaneously improving climate resilience. These retrofits improve insulation, airtightness, and mechanical equipment efficiency. NRCan’s Prefabricated Exterior Energy Retrofit (PEER) project developed prefabricated building envelope retrofit solutions to enable net-zero performance. The PEER process was demonstrated on two different pilot projects completed between 2017 and 2023. In 2024, in partnership with industry partners, NRCan developed new low-carbon retrofit panel designs and completed a pilot project to evaluate their performance and better understand resiliency and occupant comfort post-retrofit. The Low-Carbon Climate-Resilient (LCCR) Living Lab pilot retrofit was completed in 2024 in Ottawa, Canada, using low-carbon PEER panels. This paper outlines the design and construction for the pilot, including panel designs, the retrofitting process, and post-retrofit building and envelope commissioning. The retrofitting process included the design and installation of new prefabricated exterior retrofitted panels for the walls and the roof. These panels were insulated with cellulose, wood fibre, hemp, and chopped straw. During construction, blower door testing and infrared imaging were conducted to identify air leakage paths and thermal bridges in the enclosure. The retrofit envelope thermal resistance is RSI 7.0 walls, RSI 10.5 roof, and an RSI 3.5 floor with 0.80 W/m²·K U-factor high-gain windows. The measured normalized leakage area @10Pa was 0.074 cm²/m². The net carbon stored during retrofitting was over 1480 kg CO₂. Monitoring equipment was placed within the LCCR to enable the validation of hygrothermal models for heat, air, and moisture transport, and energy, comfort, and climate resilience models. Full article

Practical applications of piezoelectric vibration energy harvesting systems are required to produce a stable DC output through the nonlinear process of AC-DC rectification. In most simulation studies of such systems, the diodes have been idealised as switches, an assumption that is valid only if the vibration-induced voltage is high enough, which is frequently not the case in practice. This paper presents an experimentally validated simulation of a base excited vibration energy harvester connected to a full wave rectified load, combining the analytical modal transformation of the Euler–Bernoulli model of a piezoelectric beam with the nonlinear current-voltage characteristic of a real (non-ideal) diode. Three types of diodes with significantly different model parameters sourced from industry-standard datasets are considered. Discrepancies between simulated and measured resonant voltage levels are found to be less than 10% on average, and the discrepancy in resonant frequency is less than 1%, demonstrating the reliability of the Shockley diode model despite its omission of the dynamic behaviour of the diode. Full article

Conventional separation methods often prove ineffective for complex, refractory high-phosphorus iron ores. Recent advances propose a coal-based direct reduction dephosphorization-magnetic separation process, achieving significant dephosphorization efficiency. This review systematically analyzes phosphorus occurrence states in high-phosphorus oolitic iron ores across global deposits, particularly within iron minerals. We categorize contemporary research and elucidate dephosphorization mechanisms during coal-based direct reduction. Key factors influencing iron mineral phase transformation, iron enrichment, and phosphorus removal are comprehensively evaluated. Phosphorus primarily exists as apatite and collophane gangue m horization agents function by: (1) inhibiting phosphorus-bearing mineral reactions or binding phosphorus into soluble salts to prevent incorporation into metallic iron; (2) enhancing iron oxide reduction and coal gasification; (3) disrupting oolitic structures, promoting metallic iron particle growth, and improving the intergrowth relationship between metallic iron and gangue. Iron mineral phase transformations follow the sequence: Fe₂O₃ → Fe₃O₄ → FeO (FeAl₂O₄, Fe₂SiO₄) → Fe. Critical parameters for effective dephosphorization under non-reductive phosphorus conditions include reduction temperature, duration, reductant/dephosphorization agent types/dosages. Future research should focus on: (1) investigating phosphorus forms in iron minerals for targeted ore utilization; (2) reducing dephosphorization agent consumption and developing sustainable alternatives; (3) refining models for metallic iron growth and improving energy efficiency; (4) optimizing reduction atmosphere control; (5) implementing low-carbon emission strategies. Full article

Battery electric vehicles (BEVs) play a key role in reducing CO₂ emissions and enabling a climate-neutral economy. However, they suffer from reduced range in cold conditions due to electric cabin heating. Electrically heated thermal energy storage (TES) systems can decouple heat generation from demand, thereby preventing a loss of range. For this purpose, a novel concept based on a directly electrically heated ceramic solid media TES is investigated, aiming to achieve high storage density while enabling both high charging and discharging powers. To assess the feasibility of the proposed TES concept in BEVs, a holistic evaluation of central aspects is conducted, including experimental characterization for material selection, experimental investigations on electrical contacting, and simulations of the electrothermal charging and thermal discharging processes under vehicle-relevant conditions. As a result of the material characterization, a promising material—a silicon carbide-based composite—was identified, which meets the electrothermal requirements under typical household charging conditions and allows reliable operation with silver-metallized electrodes. Design studies with this material show gravimetric energy densities—including thermal insulation demand—exceeding 100 Wh/kg, storage utilization of up to 90%, and fast charging within 25 min, while offering 5 kW at flexible temperature levels for cabin heating during thermal discharging. These results show that the basic prerequisites for such storage systems are met, while further development—particularly in terms of material improvements—remains necessary. Full article

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article

The electrical arcs generated by high-speed dynamic separation between pantograph and catenary systems pose a significant threat to the operational safety of high-speed railways. Environmental factors, particularly relative humidity and airflow, critically influence arc characteristics. This study establishes a two-dimensional pantograph–catenary arc model based on magnetohydrodynamic theory, validated through a self-developed experimental platform. Research findings demonstrate that as relative humidity increases from 25% to 100%, the core arc temperature decreases from 10,500 K to 9000 K due to enhanced heat dissipation in humid air and electron capture by water molecules; the peak arc voltage rises from 37.25 V to 48.17 V resulting from accelerated deionization processes under high humidity conditions; the average arc energy in polar regions increases from 2.5 × 10⁻⁴ J/m³ to 3.5 × 10⁻⁴ J/m³, exhibiting a saddle-shaped distribution; and the maximum arc pressure declines from 5.3 Pa to 3.7 Pa. Under airflow conditions of 10–30 m/s, synergistic effects between airflow and humidity further modify arc behavior. The most pronounced temperature fluctuations and most frequent arc root migration occur at 100% humidity with 30 m/s airflow, while the shortest travel distance and longest persistence are observed at 25% humidity with 10 m/s airflow, as airflow accelerates heat dissipation and promotes arc root alternation. Experimental measurements of arc radiation intensity and temperature distribution show excellent agreement with simulation results, verifying the model’s reliability. This study quantitatively elucidates the influence patterns of humidity and airflow on arc characteristics, providing a theoretical foundation for enhancing pantograph–catenary system reliability. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

The Chang 7 member of the Ordos Basin hosts abundant shale oil and gas resources and plays a vital role in the development of unconventional energy. This study investigates differences in damage evolution and underlying mechanisms between representative shale oil and shale gas reservoir cores from the Chang 7 member under fracturing fluid hydration. A combination of high-temperature expansion tests, nuclear magnetic resonance (NMR), and micro-computed tomography (Micro-CT) was used to systematically characterize macroscopic expansion behavior and microscopic pore structure evolution. Results indicate that shale gas cores undergo faster expansion and higher imbibition rates during hydration (reaching stability in 10 h vs. 23 h for shale oil cores), making them more vulnerable to water-lock damage, while shale oil cores exhibit slower hydration but more pronounced pore structure reconstruction. After 72 h of immersion in fracturing fluid, both core types experienced reduced pore volumes and structural reorganization; however, shale oil cores demonstrated greater capacity for pore reconstruction, with a newly formed pore volume fraction of 34.5% compared to 24.6% for shale gas cores. NMR and Micro-CT analyses reveal that hydration is not merely a destructive process but a dynamic “damage–reconstruction” evolution. Furthermore, the addition of clay stabilizers effectively mitigates water sensitivity and preserves pore structure, with 0.7% identified as the optimal concentration. The research results not only reveal the differential response law of fracturing fluid damage in the Chang 7 shale reservoir but also provide a theoretical basis and technical support for optimizing fracturing fluid systems and achieving differential production increases. Full article

Microorganisms exhibit metabolic versatility, which enables their multifaceted application, including in pollutant detoxification, waste recycling, and environmental restoration. Agricultural processing generates substantial byproducts rich in carbon, nitrogen, and sulfur, which require proper handling to mitigate ecological challenges and reduce carbon footprints. The generation of recalcitrant keratinous biomass and its slow degradation in the environment have prompted technological interventions for sustainable solutions. Fundamentally, chemical, thermal and mechanical processing methods have been utilized in managing keratinous waste. These approaches are not only energy-intensive but also yield low-quality products and exacerbate environmental challenges. Multidimensional research on the microbial-assisted conversion of keratinous waste into valuable products, which aligns with circular economy principles, is underway. The biodegradation of keratinous resources has predominantly employed culturable single microbial strains; however, few studies have recently investigated microbial consortia as a promising strategy. The use of microbial consortia leverages the high cultural stability and complementary metabolic pathways of microbes to achieve excellent keratin biodegradation. Therefore, this study examined the latest advancements in transforming keratinous waste into high-quality protein hydrolysates using microbial strains. It detailed various types of microbial consortia and their roles in the valorization of keratinous biomass, while highlighting some knowledge gaps for future studies. The study also explored the role of ancillary microbial enzymes in facilitating the conversion of keratinous biomass into value-added products. Full article

In response to the challenges faced by high-energy-consuming enterprises in utilizing renewable energy and implementing low-carbon operations, this paper proposes a multi-objective optimization strategy based on source–storage–load collaborative scheduling. The strategy establishes a refined model of aluminum electrolysis load, thoroughly considering the coupling relationship between temperature, production output, and power consumption. Additionally, it develops a dynamic coupling model between multi-functional crane loads and aluminum electrolysis production to reveal the influence mechanism of auxiliary equipment on the main production process. Based on this foundation, this paper constructs a multi-objective optimization model that targets the minimization of operating costs, the minimization of carbon emissions, and the maximization of the renewable energy consumption rate. An improved heuristic intelligent optimization algorithm is employed to solve the model. The simulation results demonstrate that, under a renewable energy penetration of 67.8%, the proposed multi-objective optimization strategy achieves a maximum reduction in carbon emissions of 1677.35 t and an increase in renewable energy consumption rate of 12.11%, compared to the conventional single-objective economic optimization approach, while ensuring the stability of aluminum electrolysis production. Furthermore, when the renewable energy penetration is increased to 76.2%, the maximum reduction in carbon emissions reaches 8260.97 t, and the renewable energy consumption rate is improved by 18.86%. Full article

In the production of stainless steel, chromium losses, particularly in the electric arc furnace (EAF) phase, pose a challenge. This study addresses these issues by reviewing and analyzing the thermodynamics of the Fe-Cr-C-O-(Si) system, highlighting discrepancies in existing literature regarding Gibbs free energies, interaction parameters, and other thermodynamic data. We developed a simple to use thermodynamic model to simulate the oxidation process using established data from scientific literature. The model calculates the equilibrium solubilities of chromium and carbon, showing how process variables like temperature, partial pressure of carbon monoxide, and silicon concentration influence chromium oxidation. The findings confirm that higher temperatures and the presence of silicon significantly reduce chromium loss by favoring carbon oxidation over chromium. The research concludes by providing practical guidelines for minimizing chromium losses in EAFs, such as protecting scrap with carbon, silicon, and aluminum; controlling oxygen intake; and ensuring a high melt temperature during decarburization. These guidelines aim to improve the economic efficiency and sustainability of stainless steel production. The paper is an expanded version of a prior conference paper. Full article