Large-eddy simulations are performed to quantify the influence of nozzle geometry on combustion efficiency, local mixing, and blowout resistance in non-assist methane flares. Five canonical nozzle shapes are evaluated under relevant industrial flare conditions, including a circle, low-aspect-ratio ellipse, high-aspect-ratio ellipse, diamond, and square. Cornered geometries are shown to enhance near-field recirculation, promote mixing, and sustain flame attachment, resulting in up to a improvement in combustion efficiency compared with streamlined nozzles. Square nozzles perform best, irrespective of wind direction, and maintain combustion efficiency above even at the highest tested crosswind velocities, while streamlined designs exhibit early flame lift-off, reduced recirculation, and efficiency losses. Sharp-edged nozzles also accelerate scalar homogenization and buffer flames against crosswind-induced strain, significantly improving blowout resistance. Despite the widespread use of circular nozzles in industry, these results highlight a passive geometric modification as a practical route to enhanced flare performance.
{"title":"Effect of nozzle geometry on combustion efficiency and blowout in non-assist flares","authors":"Ashray Mohit , Jenna Stolzman , Margaret Wooldridge , Jesse Capecelatro","doi":"10.1016/j.fuel.2025.137970","DOIUrl":"10.1016/j.fuel.2025.137970","url":null,"abstract":"<div><div>Large-eddy simulations are performed to quantify the influence of nozzle geometry on combustion efficiency, local mixing, and blowout resistance in non-assist methane flares. Five canonical nozzle shapes are evaluated under relevant industrial flare conditions, including a circle, low-aspect-ratio ellipse, high-aspect-ratio ellipse, diamond, and square. Cornered geometries are shown to enhance near-field recirculation, promote mixing, and sustain flame attachment, resulting in up to a <span><math><mn>5</mn><mspace></mspace><mi>%</mi></math></span> improvement in combustion efficiency compared with streamlined nozzles. Square nozzles perform best, irrespective of wind direction, and maintain combustion efficiency above <span><math><mn>96.5</mn><mspace></mspace><mi>%</mi></math></span> even at the highest tested crosswind velocities, while streamlined designs exhibit early flame lift-off, reduced recirculation, and efficiency losses. Sharp-edged nozzles also accelerate scalar homogenization and buffer flames against crosswind-induced strain, significantly improving blowout resistance. Despite the widespread use of circular nozzles in industry, these results highlight a passive geometric modification as a practical route to enhanced flare performance.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"411 ","pages":"Article 137970"},"PeriodicalIF":7.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1016/j.fuel.2025.137957
Jian Gao , Haiquan An , Zhen Ma , Zhen Liu , Xinhui Fang , Fangcheng Tian , Weiwei Xuan
Developing a precise model is critical for the stability and optimization of gasifier systems. In this work, a mechanism and data driven hybrid model is developed for coal water slurry gasifiers. This mechanism driven model uses the kinetic gasification mechanism to calculate the flow and composition of gases. This data driven model predicts the residuals between the output values of the mechanism driven model and the historical data. The framework incorporates four machine learning models: multilayer perceptron (MLP), random forest (RF), Categorical Boosting (CatBoost), and long short-term memory (LSTM). In addition, Bayesian optimization is utilized for intelligent hyperparameter tuning. The results of the hybrid model achieved mean absolute errors consistently below 0.015 on the test set, outperforming both the pure mechanism and standalone machine learning models. Meanwhile, the hybrid model demonstrates the capability to achieve minute-level predictions. Furthermore, the correlation between the hybrid model predictions and the operating parameters was revealed by the bubble plots. This provides a foundation for the intelligent optimization of gasification systems.
{"title":"A novel mechanism-data hybrid model with minute-level dynamic response in syngas prediction of coal water slurry gasifiers","authors":"Jian Gao , Haiquan An , Zhen Ma , Zhen Liu , Xinhui Fang , Fangcheng Tian , Weiwei Xuan","doi":"10.1016/j.fuel.2025.137957","DOIUrl":"10.1016/j.fuel.2025.137957","url":null,"abstract":"<div><div>Developing a precise model is critical for the stability and optimization of gasifier systems. In this work, a mechanism and data driven hybrid model is developed for coal water slurry gasifiers. This mechanism driven model uses the kinetic gasification mechanism to calculate the flow and composition of gases. This data driven model predicts the residuals between the output values of the mechanism driven model and the historical data. The framework incorporates four machine learning models: multilayer perceptron (MLP), random forest (RF), Categorical Boosting (CatBoost), and long short-term memory (LSTM). In addition, Bayesian optimization is utilized for intelligent hyperparameter tuning. The results of the hybrid model achieved mean absolute errors consistently below 0.015 on the test set, outperforming both the pure mechanism and standalone machine learning models. Meanwhile, the hybrid model demonstrates the capability to achieve minute-level predictions. Furthermore, the correlation between the hybrid model predictions and the operating parameters was revealed by the bubble plots. This provides a foundation for the intelligent optimization of gasification systems.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"411 ","pages":"Article 137957"},"PeriodicalIF":7.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dual challenge of rising energy demand and the need for sustainable waste management has positioned waste-to-energy (WtE) technologies as a viable solution that valorizes waste while mitigating environmental impacts. This review presents a comprehensive overview of the potential of WtE technologies in harnessing diverse organic waste streams, including municipal solid waste, agricultural residues, industrial by-products, and organic waste into energy carriers. The key technologies fall under the broader domains of thermochemical, biological, and bioelectrochemical conversions, are evaluated for feedstock suitability, energy yield, environmental impact, and economics. Particular attention is directed towards transesterification of waste lipids (or oils) into biodiesel, hydrothermal liquefaction of moisture-based biomasses for biocrude generation, pyrolysis/gasification of heterogeneous plastics and lignocellulosic biomass waste for the synthesis of syngas and bio-oil, briquetting for energy-dense solid fuel, microbial fuel cell for converting wastewater into clean energy, and anaerobic digestion for organic wastes to biogas. Further, life cycle and economic evaluations show that hybrid, decentralized WtE systems can support circular economy goals with scalable energy, waste reduction, and carbon mitigation.. Moreover, the future recommendations for integrated WtE systems, decentralized infrastructure, and policy support are advised for both developed and developing regions.
{"title":"Harnessing waste for energy: feedstock, technological advancement, sustainability, life cycle evaluation, and future perspectives","authors":"Ajeet Kumar Prajapati , Syed Saim Ali , Aditya Kashyap , Khursheed B. Ansari , Sunil Singh , Anish Kumar , Rajnandani , Vinayak Awasthi , Samnyu Singh , Mohd. Shkir , Rakesh Kumar , Shakeelur Raheman A.R.","doi":"10.1016/j.fuel.2025.137965","DOIUrl":"10.1016/j.fuel.2025.137965","url":null,"abstract":"<div><div>The dual challenge of rising energy demand and the need for sustainable waste management has positioned waste-to-energy (WtE) technologies as a viable solution that valorizes waste while mitigating environmental impacts. This review presents a comprehensive overview of the potential of WtE technologies in harnessing diverse organic waste streams, including municipal solid waste, agricultural residues, industrial by-products, and organic waste into energy carriers. The key technologies fall under the broader domains of thermochemical, biological, and bioelectrochemical conversions, are evaluated for feedstock suitability, energy yield, environmental impact, and economics. Particular attention is directed towards transesterification of waste lipids (or oils) into biodiesel, hydrothermal liquefaction of moisture-based biomasses for biocrude generation, pyrolysis/gasification of heterogeneous plastics and lignocellulosic biomass waste for the synthesis of syngas and bio-oil, briquetting for energy-dense solid fuel, microbial fuel cell for converting wastewater into clean energy, and anaerobic digestion for organic wastes to biogas. Further, life cycle and economic evaluations show that hybrid, decentralized WtE systems can support circular economy goals with scalable energy, waste reduction, and carbon mitigation.. Moreover, the future recommendations for integrated WtE systems, decentralized infrastructure, and policy support are advised for both developed and developing regions.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"137965"},"PeriodicalIF":7.5,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-14DOI: 10.1016/j.fuel.2025.137920
Sumaira Naz , Ayesha Saeed , Asif Hussain Khoja , Hina Younis , Hira Azeem , Nida Naeem , Mustafa Anwar , Mutawara Mahmood Baig
Hydrogen (H2) has emerged as a viable alternative fuel for a sustainable future, with electrolysis being a key green production route. However, the efficiency of electrolysis depends on electrocatalysts to overcome kinetic barriers. Traditional electrocatalysts, despite their effectiveness, suffer from high costs, scarcity, and limited stability, hindering widespread applications. High entropy alloys (HEAs) offer a compelling alternative due to their distinctive microstructures, diverse elemental composition, and improved catalytic performance. Even though a lot of research has been conducted on HEAs for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) separately, a comprehensive assessment of their dual functionality and incorporation of machine learning (ML) remains limited. This review highlights the fundamentals of HEAs, starting from structure formation to four core effects, establishing HEA’s potential as next-generation electrocatalysts for water splitting. It also provides an overview of HER and OER reaction mechanisms, identifies key challenges, and the contribution of ML in conventional electrocatalysts, and explores the structural and morphological properties that govern HEA’s performance. The review further elaborates on the thermodynamic and electronic parameters influencing HEA phase stability and crystal structure, with ML perspective. The role of ML in optimizing HEA composition and synthesis routes is further discussed. Lastly, the importance of mitigating existing challenges in ML and the potential inclusion of unexplored transition metals to enhance HER and OER activity are highlighted.
{"title":"High-entropy alloy electrocatalysts for water splitting: A systematic review with perspectives on machine learning and future design strategies","authors":"Sumaira Naz , Ayesha Saeed , Asif Hussain Khoja , Hina Younis , Hira Azeem , Nida Naeem , Mustafa Anwar , Mutawara Mahmood Baig","doi":"10.1016/j.fuel.2025.137920","DOIUrl":"10.1016/j.fuel.2025.137920","url":null,"abstract":"<div><div>Hydrogen (H<sub>2</sub>) has emerged as a viable alternative fuel for a sustainable future, with electrolysis being a key green production route. However, the efficiency of electrolysis depends on electrocatalysts to overcome kinetic barriers. Traditional electrocatalysts, despite their effectiveness, suffer from high costs, scarcity, and limited stability, hindering widespread applications. High entropy alloys (HEAs) offer a compelling alternative due to their distinctive microstructures, diverse elemental composition, and improved catalytic performance. Even though a lot of research has been conducted on HEAs for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) separately, a comprehensive assessment of their dual functionality and incorporation of machine learning (ML) remains limited. This review highlights the fundamentals of HEAs, starting from structure formation to four core effects, establishing HEA’s potential as next-generation electrocatalysts for water splitting. It also provides an overview of HER and OER reaction mechanisms, identifies key challenges, and the contribution of ML in conventional electrocatalysts, and explores the structural and morphological properties that govern HEA’s performance. The review further elaborates on the thermodynamic and electronic parameters influencing HEA phase stability and crystal structure, with ML perspective. The role of ML in optimizing HEA composition and synthesis routes is further discussed. Lastly, the importance of mitigating existing challenges in ML and the potential inclusion of unexplored transition metals to enhance HER and OER activity are highlighted.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"137920"},"PeriodicalIF":7.5,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.fuel.2025.138007
Zuochao Yu , Donghui Ci , Yong He , Junjie Jiang , Wubin Weng , Yanqun Zhu , Shixing Wang , Dayong Tian , Zhihua Wang
Ammonia, as a carbon-free fuel, offers great potential for reducing carbon emissions through co-combustion with hydrocarbons such as methane. However, in practical combustion systems, high concentrations of CO2 and NO resulting from staged combustion or flue gas recirculation (FGR) can significantly affect flame propagation and pollutant formation. In this work, the temperature, OH radical and NO distribution in laminar planar NH3/CH4/O2/NO/CO2 premixed flames established in a heat flux burner under various equivalence ratios and ammonia blending ratios were investigated using the ultraviolet broadband absorption spectroscopy (UVBAS) technique. The results confirm the accuracy of UVBAS in regions with mild gradients (e.g., post-flame zones) and successfully capture the dual role of NO—acting as an oxidizer in the pre-ignition zone and rapidly forming as the primary nitrogen oxide beyond the flame front. One-dimensional laminar flame simulations based on detailed chemical kinetic mechanisms were performed to validate the experimental data and analyze reaction pathways. While most mechanisms show good agreement in predicting laminar burning velocities (SL) and OH profiles, significant discrepancies remain in NO formation predictions. Based on sensitivity and rate-of-production (ROP) analyses combined with experimental measurements, key reaction sub-mechanisms were refined and improved.
{"title":"Experimental and kinetic study of temperature and OH/NO profiles in laminar NH3/CH4/O2/NO/CO2 premixed flames","authors":"Zuochao Yu , Donghui Ci , Yong He , Junjie Jiang , Wubin Weng , Yanqun Zhu , Shixing Wang , Dayong Tian , Zhihua Wang","doi":"10.1016/j.fuel.2025.138007","DOIUrl":"10.1016/j.fuel.2025.138007","url":null,"abstract":"<div><div>Ammonia, as a carbon-free fuel, offers great potential for reducing carbon emissions through co-combustion with hydrocarbons such as methane. However, in practical combustion systems, high concentrations of CO<sub>2</sub> and NO resulting from staged combustion or flue gas recirculation (FGR) can significantly affect flame propagation and pollutant formation. In this work, the temperature, OH radical and NO distribution in laminar planar NH<sub>3</sub>/CH<sub>4</sub>/O<sub>2</sub>/NO/CO<sub>2</sub> premixed flames established in a heat flux burner under various equivalence ratios and ammonia blending ratios were investigated using the ultraviolet broadband absorption spectroscopy (UVBAS) technique. The results confirm the accuracy of UVBAS in regions with mild gradients (e.g., post-flame zones) and successfully capture the dual role of NO—acting as an oxidizer in the pre-ignition zone and rapidly forming as the primary nitrogen oxide beyond the flame front. One-dimensional laminar flame simulations based on detailed chemical kinetic mechanisms were performed to validate the experimental data and analyze reaction pathways. While most mechanisms show good agreement in predicting laminar burning velocities (<em>S<sub>L</sub></em>) and OH profiles, significant discrepancies remain in NO formation predictions. Based on sensitivity and rate-of-production (ROP) analyses combined with experimental measurements, key reaction sub-mechanisms were refined and improved.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 138007"},"PeriodicalIF":7.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.fuel.2025.137989
Abdelmola Albadwi , Saltuk Buğra Selçuklu , Mehmet Fatih Kaya
Proton exchange membrane water electrolyzers (PEMWEs) are among the most promising technologies for sustainable hydrogen production. However, optimizing their operational performance remains a critical challenge. This study investigates the combined effects of anode and cathode electrocatalyst loadings on PEMWE performance using explainable machine learning (ML) approaches. A comprehensive experimental dataset of 1344 samples, incorporating parameters such as catalyst loadings (3-4 mgIrO2 cm−2 for anode and 0.4-0.7 mgPt/C cm−2 for cathode), membrane type (Nafion 115 and Aquivion E98-09S), temperature (50-80 °C), flow rate (50-100 mL min−1), torque (2-2.5 N·m), and current density were analyzed. Four ML models, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Categorical Boosting (CatBoost), were trained to predict current density under varying conditions. Bayesian hyperparameter optimization was applied to enhance predictive accuracy, with the DT model achieving the best performance (R2 = 0.9594), as validated by the Wilcoxon signed-rank test. SHapley Additive exPlanations (SHAP) analysis was used to interpret model outputs, identifying temperature and cathode catalyst loading as the most influential features. A nonlinear correlation was observed between catalyst loadings and current density. Best electrochemical performance was achieved with catalyst loadings of 0.6 mgPt/C cm−2 for platinum-carbon (Pt/C) composite at the cathode and optimized IrO2 loading at the anode. Furthermore, a cost-performance trade-off analysis revealed the most efficient configuration, offering a 14.8 % improvement in performance at reduced material cost. This study demonstrates the potential of explainable ML in guiding the design and optimization of PEMWEs, providing a data-driven framework for enhancing hydrogen production efficiency.
质子交换膜水电解槽（PEMWEs）是最有前途的可持续制氢技术之一。然而，优化它们的运行性能仍然是一个关键的挑战。本研究使用可解释的机器学习（ML）方法研究了阳极和阴极电催化剂负载对PEMWE性能的综合影响。对1344个样品的综合实验数据集进行了分析，包括催化剂负载（阳极为3-4 mgIrO2 cm -2，阴极为0.4-0.7 mgPt/C cm -2）、膜类型（Nafion 115和aquvion E98-09S）、温度（50-80°C）、流速（50-100 mL min -1）、扭矩（2-2.5 N·m）和电流密度等参数。四个ML模型，极端梯度增强（XGBoost），随机森林（RF），决策树（DT）和分类增强（CatBoost），被训练来预测不同条件下的电流密度。采用贝叶斯超参数优化方法提高预测精度，DT模型的预测效果最佳（R2 = 0.9594），经Wilcoxon有符号秩检验验证。SHapley加性解释（SHAP）分析用于解释模型输出，确定温度和阴极催化剂负载是最具影响力的特征。催化剂负载与电流密度之间存在非线性关系。当铂碳（Pt/C）复合材料的阴极催化剂负载为0.6 mgPt/C cm−2，阳极催化剂负载为优化后的IrO2时，电化学性能最佳。此外，成本-性能权衡分析揭示了最有效的配置，在降低材料成本的情况下，性能提高了14.8%。该研究证明了可解释ML在指导PEMWEs设计和优化方面的潜力，为提高氢气生产效率提供了数据驱动的框架。
{"title":"Performance prediction of proton exchange membrane water electrolyzers using explainable machine learning: effects of varying anode and cathode catalyst loadings","authors":"Abdelmola Albadwi , Saltuk Buğra Selçuklu , Mehmet Fatih Kaya","doi":"10.1016/j.fuel.2025.137989","DOIUrl":"10.1016/j.fuel.2025.137989","url":null,"abstract":"<div><div>Proton exchange membrane water electrolyzers (PEMWEs) are among the most promising technologies for sustainable hydrogen production. However, optimizing their operational performance remains a critical challenge. This study investigates the combined effects of anode and cathode electrocatalyst loadings on PEMWE performance using explainable machine learning (ML) approaches. A comprehensive experimental dataset of 1344 samples, incorporating parameters such as catalyst loadings (3-4 mg<sub>IrO2</sub> cm<sup>−2</sup> for anode and 0.4-0.7 mg<sub>Pt/C</sub> cm<sup>−2</sup> for cathode), membrane type (Nafion 115 and Aquivion E98-09S), temperature (50-80 °C), flow rate (50-100 mL min<sup>−1</sup>), torque (2-2.5 N·m), and current density were analyzed. Four ML models, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Categorical Boosting (CatBoost), were trained to predict current density under varying conditions. Bayesian hyperparameter optimization was applied to enhance predictive accuracy, with the DT model achieving the best performance (R<sup>2</sup> = 0.9594), as validated by the Wilcoxon signed-rank test. SHapley Additive exPlanations (SHAP) analysis was used to interpret model outputs, identifying temperature and cathode catalyst loading as the most influential features. A nonlinear correlation was observed between catalyst loadings and current density. Best electrochemical performance was achieved with catalyst loadings of 0.6 mg<sub>Pt/C</sub> cm<sup>−2</sup> for platinum-carbon (Pt/C) composite at the cathode and optimized IrO<sub>2</sub> loading at the anode. Furthermore, a cost-performance trade-off analysis revealed the most efficient configuration, offering a 14.8 % improvement in performance at reduced material cost. This study demonstrates the potential of explainable ML in guiding the design and optimization of PEMWEs, providing a data-driven framework for enhancing hydrogen production efficiency.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 137989"},"PeriodicalIF":7.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.fuel.2025.137969
Qi Liu , Minglu Dai , Tao Zhang , Bin Shi , Hui Zhang , Tianren Fu , Lin Li , Lunbo Duan
Real-time and accurate dynamic temperature distribution reconstruction is crucial for industrial processes. To address the challenges of improving both temporal and spatial resolution in acoustic tomography temperature reconstruction, this study introduces a virtual microphone array (VM) method. This method expands the acoustic path matrix and Time-of-Flight (TOF) matrix, effectively mitigating the issue of underdetermined equations caused by enhanced discretization in reconstruction, thereby improving spatial resolution in the reconstructed region. Additionally, a hybrid deep neural network model, the TOF Temperature Reconstruction Network (TTR-Net), is developed to overcome the prolonged reconstruction time associated with matrix expansion. Numerical simulations and experimental results demonstrate that the VM method significantly enhances reconstruction quality and spatial resolution. Compared to the original approach, the average reconstruction error is reduced by 2.405%, and spatial resolution is improved by a factor of 49. The TTR-Net, built on the VM array, demonstrates superior noise resistance and stability, achieving an average reconstruction error of 4.694% when compared to a 64-channel thermocouple array, with a more concentrated error distribution. The integrated acoustic tomography framework, combining the VM array with TTR-Net, reduces reconstruction time by two orders of magnitude and lowers reconstruction error by a factor of 2.51 compared to conventional methods. This methodology enables industrial-grade, high spatiotemporal resolution temperature field reconstruction.
{"title":"High spatiotemporal resolution acoustic tomography based on virtual microphone array and hybrid deep learning","authors":"Qi Liu , Minglu Dai , Tao Zhang , Bin Shi , Hui Zhang , Tianren Fu , Lin Li , Lunbo Duan","doi":"10.1016/j.fuel.2025.137969","DOIUrl":"10.1016/j.fuel.2025.137969","url":null,"abstract":"<div><div>Real-time and accurate dynamic temperature distribution reconstruction is crucial for industrial processes. To address the challenges of improving both temporal and spatial resolution in acoustic tomography temperature reconstruction, this study introduces a virtual microphone array (VM) method. This method expands the acoustic path matrix and Time-of-Flight (TOF) matrix, effectively mitigating the issue of underdetermined equations caused by enhanced discretization in reconstruction, thereby improving spatial resolution in the reconstructed region. Additionally, a hybrid deep neural network model, the TOF Temperature Reconstruction Network (TTR-Net), is developed to overcome the prolonged reconstruction time associated with matrix expansion. Numerical simulations and experimental results demonstrate that the VM method significantly enhances reconstruction quality and spatial resolution. Compared to the original approach, the average reconstruction error is reduced by 2.405%, and spatial resolution is improved by a factor of 49. The TTR-Net, built on the VM array, demonstrates superior noise resistance and stability, achieving an average reconstruction error of 4.694% when compared to a 64-channel thermocouple array, with a more concentrated error distribution. The integrated acoustic tomography framework, combining the VM array with TTR-Net, reduces reconstruction time by two orders of magnitude and lowers reconstruction error by a factor of 2.51 compared to conventional methods. This methodology enables industrial-grade, high spatiotemporal resolution temperature field reconstruction.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 137969"},"PeriodicalIF":7.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.fuel.2025.138001
Junxuan Huang, Yanfen Liao, Hailong Yang, Zejie Zheng, Xiaoqian Ma
Red mud (RM), an iron-based industrial solid waste, holds potential as an oxygen carriers (OCs) for biomass chemical looping gasification due to its low cost. To enhance the reactivity of red mud, this paper proposed a modification scheme involving co-doping with spent lithium-ion battery cathode materials. The study investigated the optimal doping amount to achieve maximum gasification reactivity under specific conditions for the modified red mud. The steam/carbon ratio for steam reforming was optimized to achieve maximum syngas yield and stability of the oxygen carrier during redox cycling. Compared to raw RM, the modified RM-20CM exhibited increased specific surface area and greater numbers of active sites. A nickel–iron bimetallic synergistic effect enhanced oxygen vacancy concentration within the oxygen carriers, thereby improving oxygen mobility. The stable lattice template enabled the OCs to maintain high OCs activity throughout gasification cycles, while added cobalt-manganese oxide catalyzed light hydrocarbon cracking. Over 10 gasification cycles, RM-20CM sustained consistent syngas yield (1470.5 mL/g), carbon conversion efficiency (82.7 %), and cold gas efficiency (93.8 %).
{"title":"Improvement of red mud oxygen carriers in biomass chemical looping gasification using battery cathode materials doping","authors":"Junxuan Huang, Yanfen Liao, Hailong Yang, Zejie Zheng, Xiaoqian Ma","doi":"10.1016/j.fuel.2025.138001","DOIUrl":"10.1016/j.fuel.2025.138001","url":null,"abstract":"<div><div>Red mud (RM), an iron-based industrial solid waste, holds potential as an oxygen carriers (OCs) for biomass chemical looping gasification due to its low cost. To enhance the reactivity of red mud, this paper proposed a modification scheme involving co-doping with spent lithium-ion battery cathode materials. The study investigated the optimal doping amount to achieve maximum gasification reactivity under specific conditions for the modified red mud. The steam/carbon ratio for steam reforming was optimized to achieve maximum syngas yield and stability of the oxygen carrier during redox cycling. Compared to raw RM, the modified RM-20CM exhibited increased specific surface area and greater numbers of active sites. A nickel–iron bimetallic synergistic effect enhanced oxygen vacancy concentration within the oxygen carriers, thereby improving oxygen mobility. The stable lattice template enabled the OCs to maintain high OCs activity throughout gasification cycles, while added cobalt-manganese oxide catalyzed light hydrocarbon cracking. Over 10 gasification cycles, RM-20CM sustained consistent syngas yield (1470.5 mL/g), carbon conversion efficiency (82.7 %), and cold gas efficiency (93.8 %).</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 138001"},"PeriodicalIF":7.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.fuel.2025.137976
Jiexin Hou , Yunkai Ji , Ermeng Zhao
Low reservoir permeability and insufficient thermal energy are the main factors limiting the gas production rate of low-permeability hydrate bearing layers (HBLs) developed by depressurization. In this paper, a method combining reservoir stimulation with low-frequency electric field heating is proposed for the development of such reservoirs. On this basis, a mathematical model for reservoir stimulation-assisted electric heating development was constructed by integrating the discrete fracture method, the current continuity equation, and hydrate phase equilibrium and kinetic equations. The production capacity and the evolution of physical fields under different development methods were compared based on numerical simulation results. The study indicates that reservoir stimulation can significantly accelerate the depressurization rate of the reservoir and the dissociation rate of hydrates, increasing production by up to 170.1 % compared to depressurization alone. Electric heating can rapidly supplement thermal energy in the later stages of depressurization when heat is largely depleted, promoting hydrate dissociation and further increasing production by 39.4 % compared to reservoir stimulation-assisted depressurization. The energy consumption analysis shows that the energy efficiency of implementing low-frequency electric field heating can reach 14.4. The use of electric heating for developing low-permeability HBLs can not only substantially increase production capacity but also achieve excellent energy utilization efficiency.
{"title":"A study on the numerical simulation method for developing low-permeability natural gas hydrate reservoirs with reservoir stimulation-assisted electric heating","authors":"Jiexin Hou , Yunkai Ji , Ermeng Zhao","doi":"10.1016/j.fuel.2025.137976","DOIUrl":"10.1016/j.fuel.2025.137976","url":null,"abstract":"<div><div>Low reservoir permeability and insufficient thermal energy are the main factors limiting the gas production rate of low-permeability hydrate bearing layers (HBLs) developed by depressurization. In this paper, a method combining reservoir stimulation with low-frequency electric field heating is proposed for the development of such reservoirs. On this basis, a mathematical model for reservoir stimulation-assisted electric heating development was constructed by integrating the discrete fracture method, the current continuity equation, and hydrate phase equilibrium and kinetic equations. The production capacity and the evolution of physical fields under different development methods were compared based on numerical simulation results. The study indicates that reservoir stimulation can significantly accelerate the depressurization rate of the reservoir and the dissociation rate of hydrates, increasing production by up to 170.1 % compared to depressurization alone. Electric heating can rapidly supplement thermal energy in the later stages of depressurization when heat is largely depleted, promoting hydrate dissociation and further increasing production by 39.4 % compared to reservoir stimulation-assisted depressurization. The energy consumption analysis shows that the energy efficiency of implementing low-frequency electric field heating can reach 14.4. The use of electric heating for developing low-permeability HBLs can not only substantially increase production capacity but also achieve excellent energy utilization efficiency.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 137976"},"PeriodicalIF":7.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.fuel.2025.138000
Zhaohui Sui , Qian Xu , Jing Cheng , Cheng Zhang , Yilin Chen , Kankan Liu , Shiwen Lei , Lixin Zhang , Fengbo Guo
Hydrogen energy, due to its renewable and sustainable nature, has become an inevitable choice for addressing energy shortages and ensuring sustainable human development. By regulating the electronic structure and self-adjusting mechanisms between transition metal atoms, electrocatalytic activity can be effectively enhanced. Here, Mn-CoFeSe2 was synthesized as a highly efficient bifunctional electrocatalyst with accelerated charge transfer kinetics by Mn doped and selenization reaction of layered double hydroxide CoFe-LDH. Results demonstrate that Mn-CoFeSe2 exhibits outstanding electrocatalytic activity and stability under alkaline conditions: HER overpotentials at 10 and 100 mA cm−2 are 141 mV and 286 mV, respectively, while OER overpotentials are 174 mV and 261 mV. When assembled into overall water splitting electrolyzer, it required only 1.54 V cell potential at a current density of 10 mA cm−2, with current density remaining essentially unchanged during 200 h of stability testing. The introduction of Mn induces a localized electronic restructuring in Mn-CoFeSe2 due to Mn’s strong electron-withdrawing effect, causing electron transfer from Fe to Mn. This electronic rearrangement between Co, Fe, and Mn atoms modifies the electronic structure of Mn-CoFeSe2, thereby enhancing charge transport. Following selenization, Se atoms transfer electrons to the Co, Fe, and Mn metal centers, strengthening the covalent nature of the metal-selenium bonds and thereby optimizing the electronic structure of the catalytic active sites. Thin-striped nanosheets expose more active sites, while the interwoven cluster structures formed by these sheets facilitate charge transport and transfer, significantly enhancing electrochemical activity for overall water splitting. This study provides an effective approach for developing highly efficient, environmentally friendly, a transition metal-based layered selenide catalyst for sustained and stable overall water splitting.
氢能具有可再生、可持续的特点，已成为解决能源短缺、保障人类可持续发展的必然选择。通过调节过渡金属原子间的电子结构和自调节机制，可以有效地提高电催化活性。本文通过层状双氢氧化物fe - ldh的Mn掺杂和硒化反应，合成了具有加速电荷转移动力学的高效双功能电催化剂Mn- cofese2。结果表明，Mn-CoFeSe2在碱性条件下表现出良好的电催化活性和稳定性：10和100 mA cm−2下的HER过电位分别为141 mV和286 mV，而OER过电位分别为174 mV和261 mV。当组装成整体的水分解电解槽时，仅需1.54 V的电池电位，电流密度为10 mA cm - 2，在200 h的稳定性测试中电流密度基本保持不变。Mn的引入引起了Mn- cofese2的局部电子重构，这是由于Mn的强吸电子效应，导致电子从Fe转移到Mn。Co， Fe和Mn原子之间的电子重排改变了Mn- cofese2的电子结构，从而增强了电荷输运。硒化后，Se原子将电子转移到Co、Fe和Mn金属中心，加强了金属-硒键的共价键性质，从而优化了催化活性位点的电子结构。薄条纹纳米片暴露出更多的活性位点，而这些纳米片形成的相互交织的团簇结构促进了电荷的传递和转移，显著提高了整体水分解的电化学活性。该研究为开发高效、环保的过渡金属基层状硒化物催化剂提供了有效途径。
{"title":"Coupling interface constructions of clustered Mn-CoFeSe2 derived from CoFe-LDH for efficient overall water splitting","authors":"Zhaohui Sui , Qian Xu , Jing Cheng , Cheng Zhang , Yilin Chen , Kankan Liu , Shiwen Lei , Lixin Zhang , Fengbo Guo","doi":"10.1016/j.fuel.2025.138000","DOIUrl":"10.1016/j.fuel.2025.138000","url":null,"abstract":"<div><div>Hydrogen energy, due to its renewable and sustainable nature, has become an inevitable choice for addressing energy shortages and ensuring sustainable human development. By regulating the electronic structure and self-adjusting mechanisms between transition metal atoms, electrocatalytic activity can be effectively enhanced. Here, Mn-CoFeSe<sub>2</sub> was synthesized as a highly efficient bifunctional electrocatalyst with accelerated charge transfer kinetics by Mn doped and selenization reaction of layered double hydroxide CoFe-LDH. Results demonstrate that Mn-CoFeSe<sub>2</sub> exhibits outstanding electrocatalytic activity and stability under alkaline conditions: HER overpotentials at 10 and 100 mA cm<sup>−2</sup> are 141 mV and 286 mV, respectively, while OER overpotentials are 174 mV and 261 mV. When assembled into overall water splitting electrolyzer, it required only 1.54 V cell potential at a current density of 10 mA cm<sup>−2</sup>, with current density remaining essentially unchanged during 200 h of stability testing. The introduction of Mn induces a localized electronic restructuring in Mn-CoFeSe<sub>2</sub> due to Mn’s strong electron-withdrawing effect, causing electron transfer from Fe to Mn. This electronic rearrangement between Co, Fe, and Mn atoms modifies the electronic structure of Mn-CoFeSe<sub>2</sub>, thereby enhancing charge transport. Following selenization, Se atoms transfer electrons to the Co, Fe, and Mn metal centers, strengthening the covalent nature of the metal-selenium bonds and thereby optimizing the electronic structure of the catalytic active sites. Thin-striped nanosheets expose more active sites, while the interwoven cluster structures formed by these sheets facilitate charge transport and transfer, significantly enhancing electrochemical activity for overall water splitting. This study provides an effective approach for developing highly efficient, environmentally friendly, a transition metal-based layered selenide catalyst for sustained and stable overall water splitting.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"410 ","pages":"Article 138000"},"PeriodicalIF":7.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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