Energy & Fuels最新文献

Understanding the microspecies behavior of ammonia–syngas mixtures is crucial for cutting NO_x emissions in engine applications of ammonia. Experiments were carried out at 5.0 MPa, with the mixture volume fraction varying from 0–50%. Concentrations of reactants, key intermediate species, and products were measured during the oxidation of ammonia–syngas mixtures. A detailed reaction mechanism was fully validated against the measured data. Experimental data indicated that the onset temperature of NH₃, H₂, and CO decreased with more syngas addition as well as the consumption temperature window, indicating an apparent improvement in the overall reactivity. An N-shape trend can be observed in NO concentration profiles under higher syngas blending ratios and fuel-lean conditions. The model in this paper can accurately predict the measured concentration data. Kinetic analysis showed that ammonia oxidation is first sensitive to NH₂-involved reactions and the increase in radicals due to the blending of syngas enhances the sensitivity of these reactions. Then, ammonia oxidation is more sensitive to reaction NH₂ + H₂ = NH₃ + H and reactions related to H₂O₂ at a higher syngas blending ratio. Third, reaction H + O₂(+M) = HO₂(+M) exhibits a strong promoting effect on NH₃ consumption at 10% syngas addition but instead shows an inhibiting effect at 50% syngas addition. As to NO formation, the chemical kinetic effect of syngas addition is more dominant than the dilution effect. Meanwhile, the reduction of NO is not significant. It eventually results in a significant rise of NO concentration. As for N₂O, the dilution effect and chemical kinetic effect of syngas are comparable, resulting in a slight increase in the peak concentration of N₂O with more syngas blending.

Coal electrolysis represents a paradigm shift from conventional high-emission applications by utilizing electrical current to decompose coal’s macromolecular structure into valuable chemical compounds. Despite technological advances, the relationship between particle size distribution and mechanistic pathways remains underexplored. This study systematically investigated particle size effects across three ranges (25–45, 45–75, and 75–106 μm) on charge consumption and structural evolution during repeated electrolysis cycles. Results demonstrated that 25–45 μm particles exhibited the highest charge consumption, indicating superior electrolysis performance. Ultimate analysis revealed increased carbon content and decreased oxygen content postelectrolysis across all sizes. Comprehensive characterization using Brunauer–Emmett–Teller (BET) surface area analysis, scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, and solid-state ¹³C nuclear magnetic resonance (NMR) provided mechanistic insights into structural transformations. BET and SEM analyses confirmed significant surface modifications including increased surface area, enhanced pore volume, and surface crack development. Raman and XRD revealed increased graphitic character and crystallinity with reduced structural defects. FTIR and NMR spectroscopy demonstrated substantial transformations in aliphatic and aromatic carbon groups. The most pronounced structural changes occurred in smaller particles, establishing 25–45 μm as the optimal particle size range for coal electrolysis applications.

In this study, a highly conductive composite of sustainable waste polyethylene terephthalate (PET) bottle-derived Cu-based metal–organic framework (Cu-MOF)/poly(3,4-ethylenedioxythiophene) (PEDOT) is developed using an in situ hydrothermal technique. The as-obtained Cu-MOF/PEDOT composite is electrochemically evaluated in a 0.2 M K₃[Fe (CN)₆] + 1 M Na₂SO₄ redox-additive electrolyte achieving a high specific capacitance of 2013.1 F/g at 3 A/g, and it outperformed the parent material Cu-MOF and also the composite material Cu-MOF/PEDOT in an aqueous electrolyte. This has also been corroborated by surface and diffusion charge characteristics because Cu-MOF/PEDOT in a redox electrolyte shows more diffusion contribution. Moreover, a symmetric device Cu-MOF/PEDOT//Cu-MOF/PEDOT is fabricated, which rendered an extraordinary energy density of ∼69 Wh/kg at an outstanding power density of 749 W/kg and also maintained promising cyclic stability with degradation of only 7.2% of initial capacitance over 10 000 cycles. Hence, this study can be a breakthrough for energy storage applications by making waste-derived sustainable porous MOFs coupled with conducting polymers and a redox-additive electrolyte.

Precise forecasting of shale oil production is essential for optimizing reservoir development and guiding field-scale operational decisions. This study presents an integrated machine learning framework combining multistage cumulative production prediction, robust feature selection, and model interpretation. Specifically, a cascaded prediction architecture is employed to capture temporal dependencies: it leverages early-stage production as auxiliary inputs to improve forecasts for 90-, 180-, and 360 day cumulative production. To ensure model stability under limited data conditions, multiple regression algorithms (Ridge, CatBoost, Random Forest, and Lasso) are benchmarked across seven production horizons. Meanwhile, an ensemble-based consensus feature selection strategy is applied to identify a compact set of temporally stable variables. The proposed cascaded modeling strategy delivers substantial performance gains, improving predictive accuracy by approximately 30% in R² (from 0.45 to 0.75) and reducing RMSE by 28% relative to independent single-stage models, demonstrating enhanced robustness. Model interpretation using SHapley additive explanations and partial dependence plots reveals that early production is primarily controlled by operational parameters, while geological characteristics increasingly govern long-term output. Nonlinear effects and key feature interactions reflect physically meaningful mechanisms, including diminishing returns from increased fracture length and pressure-sensitive production behavior. Overall, the proposed workflow provides a reusable and field-adaptable predictive framework, offering practical guidance for optimizing the completion design and production management in shale oil reservoirs.

With the increase in the power density of proton exchange membrane fuel cells, the requirements for cell heat dissipation are also increasing. Raising the cell operating temperature to the boiling point temperature range (100–120 °C) can increase the temperature difference between the cells and the external environment, which is an effective method to enhance the cell heat dissipation capacity. However, a high temperature promotes the formation of peroxides on the surface of Pt catalysts and carbon supports, which react with each other to release carbon dioxide, causing carbon support corrosion. Carbon corrosion can exacerbate Pt degradation and reduce the fuel cell life. Therefore, taking into account the characteristics of a cross-temperature (C-T) fuel cell operating at high temperature, a Pt degradation model considering the carbon corrosion effect under dynamic loading conditions is constructed. In this model, the coupling effects of electrochemical dissolution/redeposition, Pt precipitation in the membrane, and Pt particle detachment/agglomeration are simultaneously considered, which can accurately describe the Pt degradation processes of normal-temperature (N-T) and C-T fuel cells under dynamic loading conditions. The characteristic parameters of Pt catalysts after degradation can also be obtained. Based on this model, this study found that the Pt degradation processes in N-T and C-T fuel cells are dominated by dissolution/redeposition and detachment/agglomeration, respectively. In addition, through the analysis of carbon corrosion dynamics, it was found that carbon corrosion mainly occurs during the rapid voltage change period, and the carbon corrosion rate is inversely proportional to the voltage change rate and the Pt particle size on the surface of the carbon support.

Precise forecasting of shale oil production is essential for optimizing reservoir development and guiding field-scale operational decisions. This study presents an integrated machine learning framework combining multistage cumulative production prediction, robust feature selection, and model interpretation. Specifically, a cascaded prediction architecture is employed to capture temporal dependencies: it leverages early-stage production as auxiliary inputs to improve forecasts for 90-, 180-, and 360 day cumulative production. To ensure model stability under limited data conditions, multiple regression algorithms (Ridge, CatBoost, Random Forest, and Lasso) are benchmarked across seven production horizons. Meanwhile, an ensemble-based consensus feature selection strategy is applied to identify a compact set of temporally stable variables. The proposed cascaded modeling strategy delivers substantial performance gains, improving predictive accuracy by approximately 30% in R² (from 0.45 to 0.75) and reducing RMSE by 28% relative to independent single-stage models, demonstrating enhanced robustness. Model interpretation using SHapley additive explanations and partial dependence plots reveals that early production is primarily controlled by operational parameters, while geological characteristics increasingly govern long-term output. Nonlinear effects and key feature interactions reflect physically meaningful mechanisms, including diminishing returns from increased fracture length and pressure-sensitive production behavior. Overall, the proposed workflow provides a reusable and field-adaptable predictive framework, offering practical guidance for optimizing the completion design and production management in shale oil reservoirs.

Gas injection is a crucial method for enhancing oil recovery in current oil reservoir development. Among these methods, the CO₂ miscible gas flooding technology holds significant potential for widespread application. During the gas flooding process, the area near the injection well typically exhibits circular fluid flow characteristics. Moreover, dynamic changes in the bottom-hole pressure of the injection well are essential for well test research and the analysis of gas injection development parameters. In this study, the analytical solution for the two-zone radial composite model near the miscible gas flooding injection well is solved based on previous research. The type curve of dimensionless log–log bottom-hole pressure dynamic response characteristics under constant-pressure boundary conditions is obtained, and the effects of eight key parameters, including constant-pressure boundary conditions, the mobility ratio of the transition-zone fluid to crude-oil zone, and the variation index in the transition zone, are systematically analyzed. A numerical simulation model for miscible gas flooding in CO₂ is also established. By comparing the analytical solution with numerical simulation results under identical conditions, it is found that both approaches are in strong agreement. The study reveals that factors such as oil–gas viscosity ratio, reservoir permeability, porosity, and gas injection rate significantly affect the dimensionless pressure and pressure-derivative curves, which ultimately affect the gas flooding sweep range and bottom-hole pressure. This study provides a foundation for optimizing the design of gas injection development parameters, interpreting well test data for miscible flooding injection wells, and conducting radial composite pressure dynamic analysis.

In this study, a highly conductive composite of sustainable waste polyethylene terephthalate (PET) bottle-derived Cu-based metal–organic framework (Cu-MOF)/poly(3,4-ethylenedioxythiophene) (PEDOT) is developed using an in situ hydrothermal technique. The as-obtained Cu-MOF/PEDOT composite is electrochemically evaluated in a 0.2 M K₃[Fe (CN)₆] + 1 M Na₂SO₄ redox-additive electrolyte achieving a high specific capacitance of 2013.1 F/g at 3 A/g, and it outperformed the parent material Cu-MOF and also the composite material Cu-MOF/PEDOT in an aqueous electrolyte. This has also been corroborated by surface and diffusion charge characteristics because Cu-MOF/PEDOT in a redox electrolyte shows more diffusion contribution. Moreover, a symmetric device Cu-MOF/PEDOT//Cu-MOF/PEDOT is fabricated, which rendered an extraordinary energy density of ∼69 Wh/kg at an outstanding power density of 749 W/kg and also maintained promising cyclic stability with degradation of only 7.2% of initial capacitance over 10 000 cycles. Hence, this study can be a breakthrough for energy storage applications by making waste-derived sustainable porous MOFs coupled with conducting polymers and a redox-additive electrolyte.

Gas injection is a crucial method for enhancing oil recovery in current oil reservoir development. Among these methods, the CO₂ miscible gas flooding technology holds significant potential for widespread application. During the gas flooding process, the area near the injection well typically exhibits circular fluid flow characteristics. Moreover, dynamic changes in the bottom-hole pressure of the injection well are essential for well test research and the analysis of gas injection development parameters. In this study, the analytical solution for the two-zone radial composite model near the miscible gas flooding injection well is solved based on previous research. The type curve of dimensionless log–log bottom-hole pressure dynamic response characteristics under constant-pressure boundary conditions is obtained, and the effects of eight key parameters, including constant-pressure boundary conditions, the mobility ratio of the transition-zone fluid to crude-oil zone, and the variation index in the transition zone, are systematically analyzed. A numerical simulation model for miscible gas flooding in CO₂ is also established. By comparing the analytical solution with numerical simulation results under identical conditions, it is found that both approaches are in strong agreement. The study reveals that factors such as oil–gas viscosity ratio, reservoir permeability, porosity, and gas injection rate significantly affect the dimensionless pressure and pressure-derivative curves, which ultimately affect the gas flooding sweep range and bottom-hole pressure. This study provides a foundation for optimizing the design of gas injection development parameters, interpreting well test data for miscible flooding injection wells, and conducting radial composite pressure dynamic analysis.

Understanding the microspecies behavior of ammonia–syngas mixtures is crucial for cutting NO_x emissions in engine applications of ammonia. Experiments were carried out at 5.0 MPa, with the mixture volume fraction varying from 0–50%. Concentrations of reactants, key intermediate species, and products were measured during the oxidation of ammonia–syngas mixtures. A detailed reaction mechanism was fully validated against the measured data. Experimental data indicated that the onset temperature of NH₃, H₂, and CO decreased with more syngas addition as well as the consumption temperature window, indicating an apparent improvement in the overall reactivity. An N-shape trend can be observed in NO concentration profiles under higher syngas blending ratios and fuel-lean conditions. The model in this paper can accurately predict the measured concentration data. Kinetic analysis showed that ammonia oxidation is first sensitive to NH₂-involved reactions and the increase in radicals due to the blending of syngas enhances the sensitivity of these reactions. Then, ammonia oxidation is more sensitive to reaction NH₂ + H₂ = NH₃ + H and reactions related to H₂O₂ at a higher syngas blending ratio. Third, reaction H + O₂(+M) = HO₂(+M) exhibits a strong promoting effect on NH₃ consumption at 10% syngas addition but instead shows an inhibiting effect at 50% syngas addition. As to NO formation, the chemical kinetic effect of syngas addition is more dominant than the dilution effect. Meanwhile, the reduction of NO is not significant. It eventually results in a significant rise of NO concentration. As for N₂O, the dilution effect and chemical kinetic effect of syngas are comparable, resulting in a slight increase in the peak concentration of N₂O with more syngas blending.