Search Results (30,603)

Scalable copper catalysts for electrochemical CO₂ reduction have been prepared through precursor-directed thermal synthesis, enabling tunable conversion to CH₄ and C₂H₄ at industrial current densities. Thermal treatment of distinct copper precursor salts was found to yield nanostructured catalysts with composition- and morphology-dependent selectivity, and high Faradaic efficiencies under flow conditions. This simple, low-cost process demonstrates that precursor chemistry can control active phase formation and product distribution, providing a practical route toward scalable CO₂ electroreduction. Full article

Cogeneration system optimization in refineries confronts the challenge of simultaneously integrating design parameter selection and topological configuration. The literature typically addresses these aspects separately: parametric optimization with fixed topology or configuration optimization for specific nominal conditions. This work develops a comprehensive methodology integrating exhaustive parametric exploration with superstructure-based optimization through mixed-integer nonlinear programming (MINLP), applied to the Miguel Hidalgo refinery in Tula, Mexico. The systematic procedure generates superstructures considering all viable expansion and tempering routes under steam quality restrictions ($x\ge 0.88$), evaluating 84–105 combinations of generation pressure ($P_{\mathrm{HRSG}}=70$–140 bar) and superheater outlet temperature ($T_{s_4}=500$–560 °C). The analysis reveals three topologically distinct configurations identified as generating maximum power under different operating conditions and characterizes how transitions between high-performing configurations occur at discrete thermodynamic thresholds that correlate with constraint activation contradicting the conventional assumption of continuous parameter-configuration relationships. Multi-criteria evaluation positions Configuration 1 as the recommended design, generating 25% increase in electric generation, 11% improvement in utilization factor ($UF$: $0.640\to 0.710$) and 20% reduction in specific fuel consumption ($SFC$: $0.259\to 0.207$ kg/kWh). The methodology is directly generalizable to other refineries through universal thermodynamic principles, with a systematic five-step procedure applicable to any multi-pressure steam demand profile. The characterization of discrete transition phenomena and the associated methodology for their thermodynamic explanation challenges the conventional assumption of continuous parameter–configuration relationships in optimization approaches, with immediate implications for the design of flexible cogeneration systems in refineries worldwide. Full article

Polyether Ether Ketone (PEEK) is a high-performance polymer increasingly utilized in additive manufacturing due to its exceptional thermal, chemical, and mechanical properties. Thus, they are used to produce aerospace brackets, fuel system parts, seals, compressor valve plates, etc. This study investigates the mechanical performance of both neat PEEK and glass fiber-reinforced PEEK (PEEK + GF) composites fabricated via fused deposition modeling (FDM). The effects of print speed, print orientation, and post-heat treatment were systematically evaluated. Among the tested orientations, the 0° print direction with post-heat treatment at 250 °C yielded highest tensile strength of ~80 MPa, outperforming the 45° and 90° orientations. Print speeds ranging from 5 to 20 mm/s and annealing temperatures between 250 °C and 300 °C significantly influenced material properties. For neat PEEK, both tensile strength and microhardness improved with increasing print speed and post-heat treatment, peaking at 20 mm/s and 250 °C. However, annealing at 300 °C led to performance degradation, attributing to gas-induced porosity within the material. The PEEK + GF composites achieved a maximum ultimate tensile strength (UTS) of approximately 83 MPa under the same optimal conditions (20 mm/s print speed and 250 °C post-treatment). This enhancement is attributed to improved fiber alignment along the print path, increased crystallinity, and superior interfacial bonding. Notably, the composites did not exhibit the microstructural damage observed in neat PEEK at the higher annealing temperature. Full article

Many countries rely on coal for energy security during renewable transitions. This study conducts a technical, economic, and environmental analysis of hybridizing a supercritical coal-fired power unit with photovoltaics (PV) to create a sustainable hybrid system at a plant in Silesian Voivodeship, Poland. The goal is to assess costs and optimal operating conditions for a coal–PV hybrid under varying scenarios, using a decision-support model that integrates fuel prices, CO₂ emission charges (EUA), and technical parameters. Two main scenarios are modeled. In auxiliary-only PV (112 MW system), real-time power supplies pumps and fans, cutting coal consumption without storage; LCOE decreases with annual hours (2800–7000), outperforming conventional coal across EUA prices (20–50 EUR/t). In PV surplus export, excess generation (1300 h/year) is grid-fed for revenue, amplifying LCOE reductions—hybrid superiority emerges above 34 EUR/t EUA, per equivalence thresholds. Results show coal electricity exceeds low-emission costs above 34 EUR/t CO₂, with maximum disparity at 50 EUR/Mg. The hybrid leverages existing infrastructure, mitigates solar intermittency via auxiliary supply, ensures baseload continuity, boosts flexibility, and prolongs asset life—reducing >123,000 EUA/year at 145,000 MWh PV output. This sustainable hybrid promotes energy transition, reduces fossil fuel dependence, and aligns with global sustainability goals. Full article

This paper proposes a physics-informed hybrid digital CO₂ emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO₂ emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO₂ emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO₂ emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO₂ emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions. Full article

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities. Full article

Proton exchange membrane fuel cells (PEMFCs) play a key role in hydrogen-based energy systems; however, accurate and practical modelling remains challenging due to system nonlinearities, parameter variability, and degradation effects. This paper presents a low-complexity parameter estimation methodology for a simplified PEMFC equivalent circuit model using current-switching techniques. The approach enables direct extraction of key parameters, including internal resistance and capacitance, from transient voltage responses without requiring complex optimization or large datasets. Experimental validation was conducted using 100 W and 1 kW PEMFC systems under current loading and interruption conditions. The results demonstrate good agreement between measured and simulated voltage responses, with a maximum error below 10% and typical error levels in the range of ~1.4–3%. Compared to conventional mechanistic and data-driven models, the proposed method significantly reduces computational complexity and measurement requirements while maintaining high predictive accuracy. Moreover, the combination of the simplified equivalent circuit model with current-switching-based parameter estimation offers an effective and practical tool for electrical engineers, enabling real-time monitoring, control-oriented modelling, and seamless integration with power electronic systems. The proposed approach is particularly suitable for applications in DC microgrids and digital twin-based monitoring of hydrogen energy systems. Full article

Transport is one of the few major sectors in Europe where greenhouse gas emissions have not declined despite tightening climate policy. Road transport remains dominated by fossil fuels, rising travel demand, and growing freight activity. This paper develops a multi-indicator benchmarking framework to assess the extent to which recent road-transport developments in EU-27 Member States align with structural expectations derived from 1.5 °C and 2 °C mitigation pathways. A multi-indicator framework is developed combining emissions and air-quality pressures, system drivers, and urban accessibility for 2019–2023, using harmonized Eurostat, European Environment Agency, WHO, and OECD data. The analysis follows a dual-track design. First, hierarchical agglomerative clustering identifies national transport–climate profiles. Second, PROMETHEE II is applied to generate an outranking-based performance index and country ranking. Five distinct clusters emerge, ranging from carbon-intensive, car-dependent systems with limited electrification and weak accessibility to “sustainability leaders” characterized by lower emissions, higher shares of low-emission vehicles, and strong public-transport accessibility. PROMETHEE results align with this typology: Nordic and north-western countries rank highest, while several southern and eastern countries show negative net flows linked to persistent car dependence, slower fleet transition, and higher pollution exposure. The results suggest that while several countries demonstrate structural progress toward transport decarbonization, none exhibit a performance profile fully consistent with transition patterns associated with 1.5 °C-aligned mitigation pathways. Full article

Interest in isolated electrical systems powered by renewable energy has driven the development of alternatives to traditional Wind–Diesel Systems (WDS) due to their unwanted emissions and regulatory constraints. In this context, clean and efficient hybrid architectures are needed to comply with regulations and ensure stable operation under variations in user load and wind generation. This paper proposes an integrated isolated hybrid system consisting of a fuel cell replacing the Diesel Generator (DG). To fulfil the role of the synchronous generator in the diesel-group, the fuel cell operates under a Grid-Forming (GFM) control scheme, acting as a virtual synchronous machine that establishes the system’s voltage and frequency. The main aim of the hybrid system is for the wind turbine to supply most of the active power to the loads, thereby minimising hydrogen consumption. A key challenge in these systems is maintaining power balance, particularly preventing reverse flows in the fuel cell system, which has less margin than the diesel generator. In this paper, a Dump Load (DL) quickly dissipates excess power and prevents reverse power conditions. Overall, the proposed system eliminates the need for diesel generation, thereby eliminating emissions while maintaining operational stability. Simulation results demonstrate the correct functioning of the system in the presence of significant variations in load and wind power generation. Full article

Europe’s fossil-based electricity mix has shifted rapidly in recent years, raising a practical question: can we model competitive substitution among fuels with a framework that is both predictive and interpretable? We address this by combining a compact neural network (NN) with a three-dimensional Lotka–Volterra (LV) system to study monthly EU coal, natural gas, and oil-fired generation shares from the second semester of 2017 to 2023. After converting the series to row-wise shares that sum to one, we use the first $70\%$ of the sample to learn smooth trajectories and data-driven derivatives with the NN and then estimate the LV interaction coefficients through a constrained nonlinear fit. We advance the calibrated LV system over the final $30\%$ holdout with a fourth-order Runge–Kutta (RK4) scheme and evaluate forecasts using the RMSE and MAE for each fuel share series. For comparison, we report the results against both a neural network-only forecasting baseline and a classical ARIMA benchmark, both trained on the same $70\%$ window and evaluated on the same $30\%$ holdout. The hybrid NN–LV model achieves competitive forecast errors while yielding interpretable interaction patterns consistent with substitution pressures (for example, negative cross-effects between coal and gas). Finally, we run counterfactual shock experiments to illustrate how a change in one fuel’s share propagates through the mix under the learned LV dynamics, highlighting the usefulness of embedding a simple mechanistic structure within a data-driven estimator. Full article

In this article operational problems associated with the use of landfill biogas as an alternative fuel in cogeneration systems, with particular emphasis on micro-installations based on the Perkins 4008-30 TRS2 combustion engine are presented. Such installations are commonly used in cogeneration systems, whose importance in obtaining stable electric and thermal energy is growing, especially when taking into account the additional reduction in environmental impact through biogas combustion. Reducing emissions of biogas, which consists of approximately 60% methane and approximately 35% carbon dioxide, directly reduces emissions of a greenhouse gas (GHG) with a high global warming potential (GWP). In this study the characteristics of the landfill, the biogas purification system, the measurement system and the energy balance of the entire process, biogas production → electric energy → thermal energy, are presented and the importance of this type of installation in the context of a low-carbon economy is discussed. Attention is also drawn to the operational problems of the cogeneration system, which led to its failure, requiring comprehensive repairs of the internal combustion engine. Full article

Electricity demand in the United States is steadily increasing due to rapid technological growth, especially the expansion of AI data centers and electric vehicles, which are becoming major power consumers. At the same time, rising renewable energy integration, changing weather patterns, and the deployment of battery energy storage systems are increasing variability and complexity in grid operations. These evolving conditions require advanced forecasting methods to ensure reliability and efficiency, as traditional statistical and machine learning models struggle with nonlinear and temporal dependencies. To address these challenges, this study proposes a hybrid deep learning framework that combines convolutional neural networks, long short-term memory, and bidirectional LSTM models to forecast electricity generation across both conventional and renewable energy sources. The framework incorporates seasonal-trend decomposition using loess to extract trend, seasonal, and residual components, enhancing the learning of multi-scale temporal patterns. A key contribution of this work is the development of a unified, source-specific forecasting system in which each energy source is assigned its best-performing hybrid architecture. The proposed framework achieves superior accuracy, with the CNN-Bi-LSTM model yielding the best total power results (MAPE 2.60%, RMSE 13,745 MWh, MAE 9542 MWh), while Bi-LSTM models excel for wind, biomass, geothermal, and nuclear. This enables scalable, high-precision national-level forecasting. Full article

This study investigates the vertical variation and temporal characteristics and indicates the sources of black carbon (BC) and brown carbon (BrC) within particulate matter fraction PM₁ during light (November–December 2024) and heavy (January–February 2025) haze episodes in Bangkok, Thailand, a topic where data are still limited data regarding Southeast Asian megacities. Continuous measurements were conducted at 30 and 110 m above ground level, together with particle size distribution measurement, micrometeorological observations, and backward air mass trajectory analysis. During the haze periods, the highest particle number concentrations occurred in the 0.3–0.4 µm size range, indicating dominant contributions from combustion-related emissions and secondary aerosol formation. Mean PM₁ mass concentrations during the heavy haze episodes were more than 2.5 times higher than those during light haze. BC concentrations increased substantially during heavy haze, while the BC fraction of PM₁ remained relatively constant (~10%). In contrast, the BrC fraction reached nearly 20%, reflecting an increasing influence of biomass burning emissions associated with regional transport. Combined analyses of BC/BrC relationships, wind-direction dependence, and air mass trajectories demonstrate mixed contributions from local fossil fuel combustion and long-range transport of biomass burning aerosols during severe haze events. Full article

In this study, the contamination of two drinking water catchments in Australia by per- and polyfluoroalkyl substances (PFAS) was investigated. PFASs in water and sediment were found at hazardous concentrations in waterways affected by transport accidents 24 and 33 years earlier. The exact cause(s) of the PFAS pollution remains unclear due to large data gaps. Both locations experienced burning fuel tankers suppressed using PFAS foam. PFAS contamination of a Blue Mountains water supply triggered the closure of two drinking water reservoirs 3–5 km downstream of the accident site. PFAS contamination of Central Coast’s Ourimbah Creek was concentrated in two floodplain wetlands adjacent to the accident site. The Ourimbah PFAS-affected wetlands are within 500 m of a drinking water groundwater bore field and 1.2 km from a raw water offtake used as part of Central Coast’s drinking water supply. The Blue Mountains contamination has impaired the Blue Mountains World Heritage Area, with perfluorooctane sulfonate (PFOS) exceeding aquatic ecosystem protection guidelines by 100 times. The mean PFOSs in stream water near the area of the Blue Mountains road accident were 2.16 µg L⁻¹ and 213.3 µg kg⁻¹ in stream sediment. This research demonstrates how spillages of small quantities of PFASs can cause major harm due to their extreme persistence, and their levels have exceedance of environmental and health guidelines for decades, with major adverse implications for drinking water supplies and conservation areas. Full article