Search Results (496)

Direct Coal Liquefaction (DCL) is a promising route for converting abundant coal resources into liquid fuels, yet its efficiency remains strongly dependent on catalyst performance. In this work, we present an integrated computational framework combining density functional theory (DFT) calculations with machine learning (ML) to investigate iron–sulfur (FeS) cluster catalysts for DCL. DFT calculations were employed to examine hydrogen-donor dissociation and coal-derived radical hydrogenation on representative FeS clusters. The results indicate that the most favorable catalytic pathways arise from the cooperation between metallic Fe sites (Fe_2) and interfacial Fe sites adjacent to sulfur (Fe_1), while sulfur atoms mainly play an indirect structural and electronic modulation role. Based on these mechanistic insights, a database containing thermodynamic and kinetic data for 636 reactions across 50 FeS cluster models was constructed. This dataset was then used to train three ML classifiers, among which the Random Forest model showed the best performance, reaching accuracies of 80% for H-donor cleavage and 93% for radical hydrogenation on the held-out test sets. SHapley Additive exPlanations (SHAP) analysis further showed that descriptors associated with Fe active-site identity were among the most influential variables in both tasks. Overall, this work provides a mechanistically informed and interpretable computational framework for understanding FeS-catalyzed DCL chemistry and for the preliminary screening of catalyst motifs within the chemical space covered by the present FeS cluster library. Full article

Chemical looping is a clean, energy-efficient, and economically viable route for coal utilization. However, the pyrolysis and gasification of raw coal in chemical looping generate gaseous pollutants (SO_X, NO_X, and Hg) and ash that affect both reactor performance and the environment. This review synthesizes the current understanding of the formation, transformation, migration, and release of these pollutants in chemical looping, alongside the behavior of coal ash. It further assesses how these species interact with oxygen carriers, influencing reactivity, redox stability, sintering, agglomeration, attrition, and deactivation. Based on these insights, the review proposes research priorities for pollutant management and oxygen-carrier design, and for elucidating the coupled dynamics of the coal/oxygen-carrier/ash three-component particle system in fuel reactors. Full article

This paper presents a conceptual design for a technological installation aimed at mitigating ventilation air methane (VAM) from coal mine exhaust shafts, offering combined heat and power generation. It addresses the challenge posed by low methane concentrations (below 0.7%), which preclude direct combustion. To overcome this, the proposed concept involves diverting a portion of the VAM to a combustion chamber of the power boiler dedicated to co-combustion with flotation concentrate suspension, which is properly prepared for feeding into the combustion chamber. The heat generated in the power boiler produces steam to drive a turbine generator for electricity production. Back-pressure steam from the turbine can be utilized for district heating or as a thermal energy source for various industrial processes, optimizing the plant’s energy efficiency and reducing its environmental footprint. The feasibility of this technology hinges on its cost-effectiveness and energy efficiency. This aspect of efficiency has been outlined. An energy balance analysis, based on real emission data from a selected mine, is provided to determine power boiler efficiency, fuel consumption, and a VAM reduction rate. The forecast of the amount of energy produced was presented for a single installation with a grate boiler capable of co-firing fuels with a VAM flow participation of 25 m³/s. Such installations can be scaled to meet mine requirements, enabling the neutralization of VAM at a total capacity of up to 300 m³/s, which corresponds to emissions from a large ventilation shaft. Full article

The continuous combustion of coal releases carbon dioxide emissions, which has disrupted the Earth’s climate system and posed severe challenges to sustainable human development. As the world’s largest consumer of coal, China faces a critical challenge in curbing its dependence on this fuel. The Yangtze River Delta region, characterized by its advanced economy and high level of industrialization, accounts for a substantial share of the nation’s coal consumption. Therefore, identifying the driving factors of coal consumption changes in this region is essential for formulating targeted low-carbon transition policies. Based on panel data of the YRD region covering 2000 to 2022, this paper employs the LMDI method to decompose the changes in coal consumption from both production and residential sectors, with four driving factors for the production sector and three for the residential sector. The results show that the total coal consumption in the four provinces of the Yangtze River Delta region follows an inverted U-shaped trend, peaking in 2011, with an average annual growth rate of 4.75% before the peak and an annual decline rate of 4.64% after the peak. Production coal consumption accounts for an average of 96.2% of the region’s total consumption. The effect of production intensity and the effect of economic scale are respectively the main inhibitory and driving factors. Spatially, Shanghai was the only province with negative cumulative coal consumption growth, and its average gap with Anhui was the largest among all pairs. Finally, this paper puts forward targeted policy recommendations, focusing on improving coal utilization efficiency and strengthening inter-regional coordinated emission reduction. Full article

Coal-fired power units remain integral to electricity supply in many regions while facing increasingly stringent environmental expectations. Bridging reliable generation with sustainability requires more than end-of-pipe controls; it demands continuous intelligence embedded in plant operations. This study introduces an industry-oriented monitoring framework that transforms historical operational records into actionable foresight, enabling on-the-fly orchestration of combustion conditions to anticipate sulfur oxide (SOx) concentrations. Leveraging 919 empirical data points collected in 2019 from Unit 8 of the Taichung Thermal Power Plant, the framework integrates robust data governance, targeted feature curation, and a neural network-based analytics core. Eight process variables—sulfur content, coal feed rate, fixed carbon, grinding rate, calorific value, excess air, air flow, and boiler efficiency—emerge as the most influential drivers through systematic selection and feature importance attribution. The resulting forecasting module exhibits near-perfect alignment with observed emissions (R² = 0.99), enabling near-real-time guidance for setpoint adjustments and facilitating compliance strategies under varying load and fuel-quality conditions. Beyond accuracy, the system is architected for scalability and portability, aligning with Industry 4.0 paradigms by coupling continuous sensing, data-driven decision support, and stakeholder transparency. By reframing emission oversight as a proactive, intelligent service rather than a static reporting function, the proposed approach advances operational resilience, regulatory compliance, and community trust, with direct implications for resource efficiency and circular economy initiatives across heavy industry. The framework reduces potential SOx emissions and improves energy utilization efficiency under varying operational conditions. This approach contributes to environmental sustainability by enabling proactive emission reduction and cleaner production practices. It supports regulatory compliance and aligns with global sustainability goals, including SDG 7 and SDG 13. Full article

Industrial activities represent the primary source of anthropogenic carbon dioxide (CO₂) emissions, and accurate monitoring of industrial CO₂ emissions is critical to mitigating greenhouse gas emissions. Due to the lack of quantifiable and direct measurement technologies, industrial CO₂ emissions are typically calculated based on fuel combustion consumption and emission factors. However, the calculation method is not applicable to the quantification of fugitive emissions of CO₂. This work demonstrates the capability of remotely measuring industrial CO₂ emissions by mobile Differential Absorption Lidar (DIAL) system. The two-dimensional concentration distributions of the CO₂ plume were remotely measured using DIAL system, and the CO₂ emission rate was obtained with wind field information. The DIAL measurements were cross-validated using in-stack CEMS data and emission-factor calculations. Results show that the relative deviations of CO₂ emission rates between DIAL and CEMS range from −5.83% to +2.57% across four tests, all within ±6%, and the coefficient of variation (CV) of 27 valid datasets is 7.24%. In contrast, the emission factor method yields consistently higher estimates, with relative deviations of +4.61% compared to DIAL measurements. Furthermore, the mobile DIAL system was deployed in three industrial scenarios with different emission intensities: a natural gas-fired industrial park, a photovoltaic glass manufacturing plant (low-emission steady-state), and a coal-fired power plant (high-emission dynamic), demonstrating its preliminary adaptability under different operating conditions. This study indicates the feasibility and potential reliability of the mobile DIAL system for high spatio-temporal resolution remote measurement of industrial CO₂ emissions. Full article

The dynamic growth of global maize production results in the generation of large amounts of residues originating from both cultivation and processing, creating a need to develop efficient and sustainable management pathways. The aim of this study was to evaluate the feasibility of utilizing selected maize-derived residues (straw, cobs, technical maize, and post-fermentation DDGS) for the production of densified solid fuels based on biochar obtained through pyrolysis at 500 °C. The study included analyses of the mineral composition of biomass and biochar, determination of biochar yield, ash content, and higher heating value (HHV). The biochar yield ranged from 30.19% to 42.49%, with the highest values obtained for DDGS (dried distillers grains with solubles). The pyrolysis process led to an increase in HHV to 25.3–32.14 MJ/kg. These values are comparable to the calorific values of hard coal. The results indicate that biochar derived from maize residues may represent a promising feedstock for the production of solid fuels with increased energy density, while the ashes generated during their combustion show potential for agricultural applications. Full article

The higher heating value (HHV), sometimes referred to as the gross calorific value, is a crucial metric for determining a fuel’s primary energy potential in energy production systems. By combining extreme gradient boosting (XGBoost) with the differential evolution (DE) optimizer, an innovative machine learning-based model was created in this study to forecast the HHV (dependent variable). As input variables, the model included the constituents of the coal’s ultimate analysis: carbon (C), oxygen (O), hydrogen (H), nitrogen (N), and sulfur (S). For comparative purposes, random forest regression (RFR), M5 model tree, multivariate linear regression (MLR), and previously reported empirical correlations were also applied to the experimental dataset. The results showed that the XGBoost strategy produced the most accurate predictions. An initial XGBoost analysis was carried out to identify the relative contribution of the input variables to coal HHV prediction. In particular, for coal HHV estimates reliant on experimental samples, the XGBoost regression produced a correlation coefficient of 0.9858 and a coefficient of determination of 0.9691. The excellent agreement between observed and anticipated values shows that the DE/XGBoost-based approximation performed satisfactorily. Lastly, a synopsis of the investigation’s key conclusions is provided. Full article

This study systematically quantified the carbon footprint generated by China’s consumption of eight major fossil energy sources (coal, coke, crude oil, petrol, kerosene, diesel, fuel oil, and natural gas), alongside the carbon carrying capacity of four vegetation ecosystems (forest, grassland, wetland, and crop), based on the IPCC inventory methodology. ArcGIS spatial analysis was employed to reveal the spatiotemporal distribution, while the STIRPAT model identified drivers of energy carbon footprint pressure (ECFP). Concurrently, the GM (1,1) model predicted evolution trends for both energy carbon footprint (ECF) and vegetation carbon carrying capacity. Results indicated that: (1) ECF increased from 12,039.89 million tons in 2015 to 13,896.41 million tons in 2022, representing a cumulative growth of 15.42%; (2) vegetation carbon carrying capacity increased from 4710.54 million tons in 2015 to 5300.76 million tons in 2022, representing a cumulative growth of 12.53%; (3) STIRPAT model analysis indicated that economic growth and technological progress were the dominant factors influencing ECFP; and (4) GM (1,1) predicted that the ECF would continue to grow at a slower pace by 2026, while vegetation carbon carrying capacity would steadily increase. It was concluded that optimizing the energy structure and strengthening vegetation conservation could effectively alleviate ECFP, providing crucial support for the carbon neutrality objectives of China. 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

The urgent need to mitigate carbon dioxide (CO₂) emissions from fossil-fuel-based electricity generation has driven research into advanced materials for post-combustion carbon capture. This paper presents a modified solvothermal technique to synthesize zinc (Zn) and magnesium (Mg) based MOF-74 suitable for CO₂ capture from coal-fired power plants. The materials were synthesized through a solvothermal method using N,N-dimethylformamide (DMF) as the primary solvent, and subsequently characterized using Brunauer–Emmett–Teller (BET) surface area analysis, Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), and thermogravimetric analysis (TGA). Both MOFs contained oxygen-containing functional groups and were thermally stable up to 430 °C and 600 °C respectively, making them ideal for carbon capture. The low-pressure N₂-BET surface areas were 55 m²/g and 24.73 m²/g. In conclusion, the Zn material had a mesoporous structure, making it more favorable for carbon capture. It was found that prolonged synthesis time weakened the MOF structure. Future work should experimentally evaluate CO₂ capture from coal-derived flue gas using Zn/Mg-MOF-74 materials, investigating adsorption behavior and kinetics through isotherm and kinetic models, while also assessing the effect of varying Zn: Mg ratios under optimized synthesis conditions. Full article

This paper presents a thermodynamic modelling and performance analysis of a 217 MW coal-fired steam power plant, based on operating data from the only currently active coal-fired unit in Croatia. The study provides a concise technical description of the plant and a detailed thermodynamic analysis of energy flows across all major components of the steam cycle. The analysis was carried out using two complementary approaches: analytical calculations based on standard thermodynamic balance equations and numerical simulations performed with the commercial software Ebsilon Professional Version 17.00. The results obtained by both methods were validated against data reported in the literature and showed deviations within acceptable limits. Using the validated model, the influence of the number of regenerative feedwater heaters on overall plant efficiency was analysed. Additionally, sensitivity analyses were conducted to evaluate the influence of selected parameters, including the fuel net calorific value (NCV), the terminal temperature difference (TTD) of feedwater heaters, and pressure drops within the regenerative system. The results show that increasing the TTD from 2 K to 8 K reduces the net thermal efficiency from approximately 37.01% to 36.79%, while variations in pressure drop have a negligible effect on plant performance. Finally, a CO₂ emission cost analysis was conducted for each configuration, and conclusions regarding efficiency improvement and emission reduction were drawn. It was found that removing any regenerative feedwater heat exchanger decreases the observed overall plant efficiency by approximately 0.55% on average and increases plant CO₂ emissions by approximately 0.025 Mt per year on average. Full article

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), to evaluate heating-system performance and operational cost across different climatic regions. Sensitivity analysis with ±10% variations in fuel and electricity prices and system efficiencies demonstrates that geothermal heating remains the most stable and emission-efficient option under all scenarios. Results indicate that geothermal systems, despite higher reported initial investment, achieve lower operational and emissions-related costs and offer a robust and sustainable solution for decarbonizing building-heating systems. For example, the estimated seasonal geothermal heating cost is $370.59 in Anchorage compared with $646.48 for coal heating and $3375.65 for diesel systems. Furthermore, policy evaluation indicates that federal and state incentives, such as investment tax credit under the Inflation Reduction Act and rebate programs, can reduce installation costs by 25–40%, improving economic feasibility, particularly in colder regions. The analysis focuses exclusively on energy and emissions-related costs and does not explicitly model capital investment or levelized cost metrics. Full article

Nuclear energy has become a promising substitute for traditional fossil fuels (e.g., coal, oil, and natural gas) by reason of its ultra-high energy density, firm power generation, and near-zero carbon emissions. However, the shortage of uranium resources is threatening the sustainable development of nuclear power, and meanwhile the nuclear fuel front-end cycle inevitably causes radioactive uranium-bearing wastewater discharge, resulting in severe environmental pollution. Nowadays, the extraction and enrichment of uranium in seawater and uranium-containing wastewater offer a prospective avenue to secure the long-term viability of nuclear power with environmental conservation. Among numerous strategies, photocatalytic extraction of soluble hexavalent uranyl (U(VI)) over graphitic carbon nitride (g-C₃N₄), a conjugated polymer semiconductor, is increasingly attracting widespread attention due to its high solar energy utilization, environmental friendliness, high selectivity, good stability, and low cost. A comprehensive overview that pinpoints research directions for novice researchers is urgently required. Herein, the development progress of g-C₃N₄-mediated photocatalytic U(VI) extraction is briefly introduced. Subsequently, the possible mechanisms are discussed with the assistance of advanced characterization techniques, and the influential factors for catalytic efficiency are also discussed. Moreover, multiple applications of g-C₃N₄-based catalysts on photocatalytic U(VI) reduction and extraction are elaborated, especially for modularization approaches on a large scale. At length, the future challenges and prospects in photocatalytic uranium extraction from water bodies are proposed. This review aims to offer fundamental insights into designing and exploring novel g-C₃N₄-based photocatalysts for soluble U(VI) enrichment in water bodies, especially opening up new avenues for the future development of sustainable uranium extraction technologies in practice. Full article

Waste is increasingly recognized as misplaced biomass, underscoring its potential for reintegration into sustainable environmental management strategies. Biomass pyrolysis has emerged as a promising value-adding process capable of enhancing material properties for diverse applications. In this study, discarded Pili (Canarium ovatum Engl.) nutshells (PS) were utilized as a pyrolysis feedstock to upgrade their fuel characteristics. Pyrolysis conditions were optimized using response surface methodology (RSM) based on a central composite design (CCD) to maximize fixed-carbon content and higher heating value (HHV). The optimized biochar achieved a maximum fixed-carbon content of 86.15% and an HHV of 32.10 MJ/kg at a pyrolysis temperature of 600 °C and a residence time of 60 min, values comparable to those of conventional coal. Under these optimized conditions, the fixed-carbon content and HHV of the precursor biomass were enhanced by up to 254.7% and 58.4%, respectively. Statistical analysis indicated that pyrolysis temperature was the most significant factor influencing both fixed-carbon content and HHV (p < 0.05). The optimized biochar exhibited low volatile matter (8.88%), low ash content (4.97%), and low atomic ratios (H:C = 0.291; O:C = 0.077), indicating a high degree of carbonization and thermal stability. Energy-dispersive X-ray (EDX) analysis identified alkali and alkaline earth metals (Ca, Mg, Na), which contributed to the ash fraction, with minor heavy metals present, predominantly Pb. Hence, these findings enhance understanding of how pyrolysis conditions affect PS–biochar properties, improving fuel quality indicators. Full article