Search Results (637)

Widespread residential wood burning in southern Chile combined with cold climate conditions cause severe episodes of particulate matter (PM_2.5 and PM₁₀) pollution. In this study, we used logistic regression to predict daily exceedances of fine (PM_2.5) and coarse (PM₁₀) particulate levels at multiple urban sites, assessing model performance under different air quality standards. Results showed a clear latitudinal gradient in air pollution, with communities further south experiencing significantly higher PM levels and more frequent threshold exceedances, likely due to higher per capita firewood use and cooler temperatures. The logistic models achieved their best predictive accuracy under the strictest European (ESP) air quality standards (F1-scores up to ~0.72 for PM₁₀ and ~0.59 for PM_2.5), while Chile’s national (NCh) thresholds significantly underestimated pollution events. Additionally, annual per capita wood energy consumption in the far south was several times higher than in central Chile, contributing to disproportionately high emissions. These findings highlight the need to adopt more protective air quality standards and reduce wood-fueled emissions to improve early warning systems and decrease particulate exposure in southern Chile. Full article

With worsening energy and environmental issues, new energy vehicles (NEVs) have emerged as the future of the automotive industry, as they aim to address the high energy consumption and carbon emissions of traditional fuel vehicles. However, due to the industry’s short development history, limited available data, and incomplete supporting systems, most existing NEV research focuses on theoretical analysis, which hinders the achievement of accurate sales predictions. Today, online reviews influence consumer decisions and thus provide a new perspective for sales forecasting. Based on consumer behavior theory and neural network principles, our research selects factors influencing NEV sales (covering economics, technological, policy, and consumer dimensions, including preprocessed crawled online reviews), constructs an index system screened via grey relational analysis, and establishes five models (SARIMA, GRU, Seq2Seq, Attention-GRU, Attention-Seq2Seq) for training and testing. The study supports the use of online reviews in NEV sales prediction and proves that the model based on cutting-edge technology of Attention-Seq2Seq can outperform the other four methods presented above. Through this, the current contributions advance marketing innovation by helping NEV stakeholders understand relevant information using a predictive model from online reviews, which leads to precise product improvement and optimal distribution of resources as well as precise adoption of marketing strategies. Full article

Rising energy demand, fossil fuel depletion, and global warming are accelerating research into sustainable energy solutions, with growing interest in hydrogen as a promising alternative. This research presents a detailed experimental investigation and novel digital twin (DT) models for an integrated hydrogen-based energy system consisting of an Anion Exchange Membrane Electrolyzer (AEMEL), Proton Exchange Membrane Fuel Cell (PEMFC), hydrogen storage, and Battery Energy Storage System (BESS). Conducted at a real-world facility in Risavika, Norway, the study employed commercial units: the Enapter EL 4.1 AEM electrolyzer and Intelligent Energy IE-Lift 1T/1U PEMFC. Experimental tests under dynamic load conditions demonstrated stable operation, achieving hydrogen production rates of up to 512 NL/h and a specific power consumption of 4.2 kWh/Nm³, surpassing the manufacturer’s specifications. The PEMFC exhibited a unique cyclic operational mechanism addressing cathode water flooding, a critical issue in fuel cell systems, achieving steady-state efficiencies around 43.6% under prolonged (190 min) rated-power operation. Subsequently, advanced DT models were developed for both devices: a physics-informed interpolation model for the AEMEL, selected due to its linear and steady operational behavior, and an ANN-based model for the PEMFC to capture its inherently nonlinear, dynamically fluctuating characteristics. Both models were validated, showing excellent predictive accuracy (<3.8% deviation). The DTs integrated manufacturer constraints, accurately modeling transient behaviors, safety logic, and operational efficiency. The round-trip efficiency of the integrated system was calculated (~27%), highlighting the inherent efficiency trade-offs for autonomous hydrogen-based energy storage. This research significantly advances our understanding of integrated H₂ systems, providing robust DT frameworks for predictive diagnostics, operational optimization, and performance analysis, supporting the broader deployment and management of hydrogen technologies. Full article

The objective of this study is to investigate the effects of Mn₂O₃ nanoparticle additives on the performance and emission characteristics of biodiesel fuels produced from vegetable- and waste-based oils. Biodiesel fuels were synthesized via the transesterification process, after which Mn₂O₃ nanoparticles were blended in different concentrations (50, 75, and 100 ppm). The prepared fuels were tested in a single-cylinder diesel engine operating under constant speed and variable load conditions. Engine performance parameters such as specific fuel consumption (SFC) and thermal efficiency, along with emission indicators including CO, HC, NOx, smoke opacity, and exhaust gas temperature, were systematically analyzed. Additionally, the experimental findings were modeled and validated using the machine learning-based linear regression method. The addition of Mn₂O₃ nanoparticles significantly improved combustion and emission performance. Among all samples, the COB10+ 100 ppm Mn₂O₃ fuel exhibited the best overall performance, achieving a 37.50% reduction in CO, 38.8% reduction in HC, and 33.84% reduction in smoke (soot) emissions compared to conventional diesel. This fuel also demonstrated an increase in thermal efficiency comparable to that of diesel. The improvement in thermal efficiency was attributed to enhanced the in-cylinder temperature, reduced ignition delay, and shorter combustion duration. Furthermore, the use of waste-derived vegetable oils contributed to lower production costs and a reduction in environmental impact. The linear regression model yielded an optimum prediction accuracy with a mean squared error of 5.86 × 10⁻⁶ for CO emission data. These findings indicate that Mn₂O₃ nanoparticles can effectively enhance the performance and sustainability of biodiesel fuels while maintaining economic and ecological advantages. Full article

Online performance optimization improves fuel economy in modern aero-engines, but shifts in the steady operating point require authority handovers between steady and transient controllers. This study develops a bumpless transfer strategy within a multivariable performance-seeking architecture that couples a valve-position controller (VPC) for steady optimization with a model predictive controller (MPC) for transients. Coordination combines control-input mismatch compensation, feedforward set-point pre-shaping, and hysteretic switching to ensure manipulated-variable continuity and constraint compliance. High-fidelity nonlinear simulations show that, when the operating point is moved, thrust deviation at handover is ≤1.5%, fuel-flow commands remain continuous and rate-limited, and specific fuel consumption decreases by ≈5.03% at the optimized steady state. Turbine-inlet-temperature and compressor surge-margin limits are rigorously satisfied throughout the maneuver. The results indicate that the proposed strategy preserves closed-loop smoothness while delivering measurable fuel-economy gains, providing a practical path to integrate online performance optimization with constraint-aware transient control in aero-engines. Full article

Current research on the performance and emissions of vehicles and internal combustion engines should include analysis of efficiency-enhancing technologies and emission reduction strategies across a variety of vehicle systems. To improve both performance and emission control, it is necessary to examine advanced heavy-duty driveline technologies, considering their real-world impact on fuel economy and emission reduction under various driving conditions. This article will deal with predictive cruise control (PCC) and its influence on the operating characteristics of a truck, specifically a semi-trailer combination. The measurement was carried out using dynamic driving tests of a truck on a selected road. The use of electronic systems for automatically maintaining the vehicle’s motion states (especially speed) based on the specified conditions most often has several benefits for the driver not only from the point of view of vehicle operation but also from the point of view of transport companies (cost reduction). It is generally known that the use of these electronic systems reduces the vehicle’s fuel consumption and therefore also reduces the amount of exhaust gases. Comparing the individual directions of the road tests, the difference in relative maximum power utilization between the driver and the PCC system was 26.42% in the ST-MY direction and 23.81% in the MY-ST direction. The use of PCC also results in fuel savings of up to 17.11%. This study provides new insights into the quantification of the impact of PCC on fuel consumption in real operating conditions and highlights the potential for integrating PCC into driver assistance systems and logistics planning to reduce costs and emissions in freight transport. Further research could focus on applying this system in specific road conditions. Full article

In current practice, the deployment of artificial intelligence models for the optimization of construction processes is highly complex and limited, primarily due to the lack of data available for training models. Collecting real-world data is both time-consuming and resource-intensive. This paper focuses on the development of a methodology and a model for generating synthetic data intended for the subsequent training of artificial intelligence models for optimizing construction machinery assemblies. The proposed synthetic data generation process is based on simulation principles that employ queuing theory and the stochastic Monte Carlo method. This approach enables the rapid creation of large-scale synthetic datasets. The developed model and generator are specifically focused on the use of construction machinery in earthworks. Selected generated data were compared with and validated against real construction projects. The synthetic data demonstrated very good agreement with the observed data across key performance indicators. For Total Cost, CO₂ Emissions, Fuel Consumption, and Completion Time, deviations between synthetic and real project data were generally within 5–7%, which is considered acceptable for construction process simulations. In contrast, the Number of Failures exhibited noticeably higher deviations (approximately 10–15%), indicating the current model’s weaker predictive capability for this metric. The outcomes of this study can benefit contractors and construction equipment manufacturers by improving design efficiency, reducing costs, and enhancing machine performance. Full article

Real-time prediction of the instantaneous fuel consumption rate (FCR) of any vehicle is the key to improving energy efficiency and reducing emissions. The conventional prediction methods, which include an on-board diagnostic (OBD) system, require the specific vehicle parameters and environmental conditions such as air density. We propose a data-driven Bayesian optimization and Monte Carlo (MC) Dropout methods-based long short-term memory (BMC-LSTM) network FCR prediction model using only the vehicle’s throttle position, velocity, and acceleration data. The cost-effective LSTM network-based solution enhances the high-resolution prediction accuracy within a deep learning framework. The network is integrated with the Bayesian optimization and MC-Dropout methods to ensure a probabilistically optimal hyperparameter set and robust networks. The proposed method presents an FCR model that provides calibrated predictions and reliability against distribution drift by probabilistically tuning hyperparameters with Bayesian optimization and quantifying epistemic uncertainty with the MC-Dropout. Our approach requires only vehicle speed, longitudinal acceleration, and throttle position at inference time. Note, however, that the reference FCR used to train and validate the models was obtained from OBD during data acquisition. The performance of the proposed method is compared with a conventional LSTM and Bidirectional LSTM-based multidimensional models, XGBoost and support vector regression-based models, and first- and fourth-order polynomials, which are derived using the least-squares method. The prediction performance of the method is evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute, and R-squared statistical metrics. The proposed method achieves a superior $R^2$ score and substantially reduces the conventional error metrics. Full article

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

To enhance the operational efficiency of fuel cell engineering vehicles in transportation, reliable energy management strategies (EMSs) are essential for optimizing fuel consumption and power distribution. In this paper, we propose a novel energy management framework that utilizes a reinforcement learning-based adaptive hierarchical equivalent consumption minimization strategy (ECMS) to regulate fuel cell/battery hybrid system. The structure integrates deep Q-network (DQN), fuzzy logic, and ECMS algorithms and employs a long short-term memory neural network for working condition prediction. By combining DQN with the equivalence factor obtained using the battery state of charge penalty function and adjusting it using a fuzzy logic controller, the stability of the subsequent ECMS is enhanced. In a simulation environment, the proposed EMS achieves a 97.44% fuel economy compared to the dynamic programming-based global optimized EMS. Experimental findings indicate that the hierarchical ECMS effectively decreases the equivalent hydrogen consumption by 3.38%, 9.12%, and 16.39% compared to the adaptive ECMS, DQN-based ECMS, and classic ECMS, respectively. Therefore, the proposed methodology offers superior economic benefits. Full article

The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NO_x and SO_x concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R² = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NO_x emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

Ten-field crop rotation systems reduce the environmental impact of sustainable agriculture by reducing pollutant emissions, helping to reduce the agricultural sector’s dependence on imported natural gas, and increasing overall crop yields through more efficient use of recycled organic fertilizers. This study aims to comprehensively analyze the feasibility and effectiveness of implementing 10-field crop rotation for biofuel production in Ukraine to ensure energy and food security. The study was conducted in Ukraine, which is characterized by a predominantly temperate continental climate. An analysis conducted between 2020 and 2024 showed that, despite a reduced cultivated area in Ukraine, the yield of major agricultural crops increased by an average of 10–20% due to the adoption of intensive farming methods. Based on the conducted research and the justification for using a 10-field crop rotation for biofuel production, the annual productivity of the planned areas was predicted. The significant potential for biofuel production was estimated at 11.1 million tons of bioethanol, 3.16 million tons of biodiesel, 6.18 billion m³ of biogas, and 3.87 million tons of solid biofuel, which would cover Ukraine’s domestic needs for gasoline and diesel fuel many times over and could potentially replace approximately 31% of Ukraine’s annual natural gas consumption. Scientific research has shown that using digestate as an organic fertilizer increases the yield of major crops by 53–83% and helps to normalize soil acidity. Full article

The fuel consumption of ships is an important component of shipping operation costs and also a significant source of greenhouse gas emissions. Accurate fuel consumption prediction is of great significance for optimizing the energy efficiency management of ships, reducing operating costs, and minimizing environmental pollution. In addition, we have also observed that the fuel consumption data of ships usually have a strong temporal correlation. Therefore, in order to study whether the time-series factors of ship fuel data are helpful for SFC prediction and the performance of various deep learning models in ship fuel consumption prediction, this paper proposes three classes of models for comparative study: RNN-based models, attention-based models such as Transformer and Informer, which are applied to the field of ship fuel consumption for the first time, and RNN–attention mixed models. The experimental results show that there is indeed a lag in ship navigation data, and the processing of time-series data is of great significance for fuel consumption prediction. Moreover, we have found that on real ship operation datasets, Informer is the best-performing model with 1.46 and 0.969 for MSE and $R^2$ scores. The prediction performance of Informer is significantly better than that of other methods, which provides a new direction for future ship fuel consumption prediction. Full article

Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context. Full article