Search Results (2,460)

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

This work proposes a new method to refill fuel cell electric vehicle hydrogen tanks from a storage system in hydrogen refueling stations. The new method uses the storage tanks in cascade to supply hydrogen to the refueling station dispensers. This method reduces the hydrogen compressor power requirement and the energy consumption for refilling the vehicle tank; therefore, the proposed alternative design for hydrogen refueling stations is feasible and compatible with low-intensity renewable energy sources like solar photovoltaic, wind farms, or micro-hydro plants. Additionally, the cascade method supplies higher pressure to the dispenser throughout the day, thus reducing the refueling time for specific vehicle driving ranges. The simulation shows that the energy saving using the cascade method achieves 9% to 45%, depending on the vehicle attendance. The hydrogen refueling station design supports a daily vehicle attendance of 9 to 36 with a complete refueling process coverage. The carried-out simulation proves that the vehicle tank achieves the maximum attainable pressure of 700 bars with a storage system of six tanks. The data analysis shows that the daily hourly hydrogen demand follows a sinusoidal function, providing a practical tool to predict the hydrogen demand for any vehicle attendance, allowing the planners and station designers to resize the elements to fulfill the new requirements. The proposed system is also applicable to hydrogen ICE vehicles. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

Aluminothermic reduction is an alternative processing route for the circular economy because Al is produced electrochemically in the Hall–Héroult process with minimal CO₂ emissions if the electricity input is sourced from non-fossil fuel energy sources. This circular processing option attracts increased research attention in the aluminothermic production of manganese and silicon alloys. The Al₂O₃ product must be recycled through hydrometallurgical processing, with leaching as the first step. Recent work has shown that the NaAlO₂ compound is easily leached in water. In this work, a suitable slag formulation is applied in the aluminothermic reduction of manganese ore to form a Na₂O-based slag of high Al₂O₃ solubility to effect good alloy–slag separation. The synergistic effect of carbon and silicon reductants with aluminium is illustrated and compared to the test result with only carbon reductant. The addition of small amounts of carbon reductant to MnO₂-containing ore ensures rapid pre-reduction to MnO, facilitating aluminothermic reduction. At 1350 °C, a loosely sintered mass formed when carbon was added alone. The alloy and slag chemical analyses are compared to the thermochemistry predicted phase chemistry. The alloy consists of 66% Mn, 22–28% Fe, 2–9% Si, 0.4–1.4% Al, and 2.2–3.5% C. The higher %Si alloy is formed by adding Si metal. Although the product slag has a higher Al₂O₃ content (52–55% Al₂O₃) compared to the target slag (39% Al₂O₃), the fluidity of the slags appears sufficient for good alloy separation. Full article

Sepsis remains a leading global cause of mortality, with delayed recognition and empirical antibiotic overuse fueling poor outcomes and rising antimicrobial resistance. This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. Literature from 2019 to 2025 was systematically reviewed following PRISMA-ScR guidelines. A total of 129 full-text articles were analyzed, with study quality assessed via the JBI and QUADAS-2 tools. AI-based models demonstrated robust predictive performance for early sepsis detection (AUROC 0.68–0.99), antibiotic stewardship, and resistance prediction. Notable tools, such as InSight and KI.SEP, leveraged multimodal clinical and biomarker data to provide actionable, real-time support and facilitate timely interventions. AI-driven platforms showed potential to reduce inappropriate antibiotic use and nephrotoxicity while optimizing outcomes. However, most models are limited by single-center data, variable interpretability, and insufficient real-world validation. Key challenges remain regarding data integration, algorithmic bias, and ethical implementation. Future research should prioritize multicenter validation, seamless integration with clinical workflows, and robust ethical frameworks to ensure safe, equitable, and effective adoption. AI and ML hold significant promise to transform sepsis management, but their clinical impact depends on transparent, validated, and user-centered deployment. Full article

The maritime sector faces escalating demands to minimize emissions and optimize operational efficiency under tightening environmental regulations. Although technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twins (DT) offer substantial potential, their deployment in real-time ship performance analytics is at an emerging state. This paper proposes a machine learning-driven framework for real-time ship performance management. The framework starts with data collected from onboard sensors and culminates in a decision support system that is easily interpretable, even by non-experts. It also provides a method to forecast vessel performance by extrapolating Key Performance Indicator (KPI) values. Furthermore, it offers a flexible methodology for defining KPIs for every crucial component or aspect of vessel performance, illustrated through a use case focusing on fuel oil consumption. Leveraging Artificial Neural Networks (ANNs), hybrid multivariate data fusion, and high-frequency sensor streams, the system facilitates continuous diagnostics, early fault detection, and data-driven decision-making. Unlike conventional static performance models, the framework employs dynamic KPIs that evolve with the vessel’s operational state, enabling advanced trend analysis, predictive maintenance scheduling, and compliance assurance. Experimental comparison against classical KPI models highlights superior predictive fidelity, robustness, and temporal consistency. Furthermore, the paper delineates AI and ML applications across core maritime operations and introduces a scalable, modular system architecture applicable to both commercial and naval platforms. This approach bridges advanced simulation ecosystems with in situ operational data, laying a robust foundation for digital transformation and sustainability in maritime domains. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

This paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on boron-based fuel under various flow rate conditions. Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. The proposed framework enables accurate classification and prediction of the dynamic evolution of flow structures based on learned representations from scattering images. Experimental results show that the feature vectors extracted by the SAE form clearly separable clusters in the latent space, leading to high classification accuracy under varying flow conditions. In the prediction task, the feature vectors predicted by the LSTM exhibit strong agreement with ground truth, with average mean square error, mean absolute error, and r-square values of 0.0027, 0.0398, and 0.9897, respectively. Furthermore, the reconstructed images offer a visual representation of the changing flow field, validating the model’s effectiveness in structure-level recovery. These results suggest that the proposed method provides reliable support for future real-time prediction of powder fuel mass flow rates based on optical sensing and imaging techniques. Full article

Biomass torrefaction must be self-sustaining and continuous to be commercially viable, eliminating dependence on additional fuels while achieving industrial-scale production. This study presents a predictive model of a full-scale continuous biomass torrefaction process that explicitly incorporates the radiation absorption properties of torrefaction gas, with a focus on water vapor. Previous research, primarily based on lab-scale batch processes, has not adequately addressed scale-up challenges or the dynamic evolution of torrefaction gas. Industrial insights from Perpetual Next confirm that water vapor significantly impacts reactor performance by absorbing heat and reducing radiative flux to the biomass. Simulations show that neglecting water vapor absorption in reactor design can lead to throughput deviations of 10–20%, affecting process stability and efficiency. Industrial-scale validation demonstrates that the model accurately predicts this effect, ensuring realistic energy demand and throughput expectations. By explicitly incorporating water vapor absorption into the radiation balance, the model provides a validated framework for optimizing reactor design and process scale-up. It demonstrates that failing to consider this effect can lead to operational instability and deviations from the intended torrefaction severity, ultimately affecting industrial-scale performance and self-sustaining operation. Full article

Owing to the need for continuous improvement in building energy performance standards (BEPSs), facilities must adhere to benchmark performances in their quest to achieve net-zero performance. This research explores machine learning models that leverage historical energy data from a cluster of buildings, along with relevant ambient weather data and building characteristics, with the objective of predicting the buildings’ energy performance through the year 2040. Using the forecasted emission results, the portfolio of buildings is analyzed for the incurred carbon non-compliance fees based on their on-site fossil fuel CO_2e emissions to assess and pinpoint facilities with poor energy performance that need to be prioritized for decarbonization. The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. To comply with these regulations, the building portfolio would need to reduce on-site fossil fuel CO_2e emissions by an average of 58,246 metric tons (22.10 kg/sq. ft.) annually, totaling 640,708 metric tons (22.10 kg/sq. ft.) over a period of 11 years. This study demonstrates the potential for robust machine learning models to generate accurate forecasts to evaluate carbon compliance and guide prompt action in decarbonizing the built environment. Full article

Wildfires accelerate soil erosion. Preventive fuel management and post-fire control measures are two distinct strategies that can be used to mitigate wildfire-induced soil loss with varying effectiveness and costs. Here, we quantified the impacts and effectiveness of pre- versus post-fire treatment strategies on soil loss mitigation. We coupled fire simulations with soil erosion modelling to estimate annual wildfire-induced soil loss for two watersheds in Portugal. We identified optimal treatment locations with the aim of maximizing the reduction in soil loss, and estimated treatment effectiveness using treatment leverage and cost-effectiveness. Both mitigation strategies were predicted to reduce post-fire soil loss, with effects increasing with treatment extent. Treatments had a strong mitigation effect particularly in extreme fire years. Results indicated that there was no single mitigation strategy that fits all watersheds, and the choice was largely influenced by wildfire and treatment frequency. For the most fire-prone watershed, Castelo de Bode, fuel treatments were the most effective strategy, being approximately 2-fold cheaper and more effective than post-fire treatments. Treatments were more effective and exhibited lower variability in years with higher soil loss. Our results show that the most cost-effective combinations of treatment strategies vary with the soil loss reduction objective. Relevant treatment synergies were identified that can help land managers to maximize the attainment of soil loss mitigation goals ensuring the best use of resources. This work contributes to a better understanding of how post-fire soil loss can be mitigated, contributing for better resource allocation while maximizing specific management goals. Full article

Hydrogen is a promising zero-carbon fuel for internal combustion engines; however, the geometric optimization of injectors for low-pressure direct-injection (LPDI) systems under lean-burn conditions remains underexplored. This study presents a high-fidelity optimization framework that couples a validated computational fluid dynamics (CFD) combustion model with a surrogate-assisted multi-objective genetic algorithm (MOGA). The CFD model was validated using particle image velocimetry (PIV) data from non-reacting flow experiments conducted in an optically accessible research engine developed by Sandia National Laboratories, ensuring accurate prediction of in-cylinder flow structures. The optimization focused on two critical geometric parameters: injector hole count and injection angle. Partial indicated mean effective pressure (pIMEP) and in-cylinder NO_x emissions were selected as conflicting objectives to balance performance and emissions. Adaptive mesh refinement (AMR) was employed to resolve transient in-cylinder flow and combustion dynamics with high spatial accuracy. Among 22 evaluated configurations including both capped and uncapped designs, the injector featuring three holes at a 15.24° injection angle outperformed the baseline, delivering improved mixture uniformity, reduced knock tendency, and lower NO_x emissions. These results demonstrate the potential of geometry-based optimization for advancing hydrogen-fueled LPDI engines toward cleaner and more efficient combustion strategies. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article