Energy Conversion and Management最新文献

Aging-aware real-time multi-layer co-optimization approach for hybrid vehicles: across configuration, parameters, and control

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-24 DOI: 10.1016/j.enconman.2025.119748

Yunge Zou , Yalian Yang , Yuxin Zhang , Xiaolin Tang

{"title":"Aging-aware real-time multi-layer co-optimization approach for hybrid vehicles: across configuration, parameters, and control","authors":"Yunge Zou , Yalian Yang , Yuxin Zhang , Xiaolin Tang","doi":"10.1016/j.enconman.2025.119748","DOIUrl":"10.1016/j.enconman.2025.119748","url":null,"abstract":"<div><div>The powertrain configuration, parameters, and control of hybrid vehicles are intertwined. All of these factors have a significant impact on acceleration, fuel economy, and battery degradation. However, research on the impact of the multi-layer co-optimization of the powertrain physical configuration and control on battery life has been neglected. Therefore, to fill this gap, an innovative aging-aware real-time multi-layer co-optimization method is proposed in this study. In the topology layer, a novel and improved multi-mode multi-gear configuration is proposed, and the performance differences and the intrinsic mechanism of different powertrain types are analyzed quantitatively and qualitatively. In the control layer, an advanced aging-aware fast real-time control strategy (AFRCS) is proposed. In the AFRCS, the offline optimization layer works in combination with Pareto optimization, battery life aging optimization (BLAO), and parallel computation to speed up the computational efficiency. The online mode coordination layer is used for real-time control, which improves the computational efficiency by approximately 20,000 times, and the battery life optimization is in the range of 10.22 %–12.9 %. During real-world driving cycles, the proposed configuration improves the acceleration and the battery life by an average of 50 % and 22.67 %, respectively, compared to a Toyota Prius, and it improves fuel saving by 8.82 % compared to a Honda Accord. Finally, the proposed AFRCS is verified with a hardware-in-the-loop (HIL) experiment. This study provides guidance for the selection, optimization, and real-time control of next-generation electrified transmissions.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119748"},"PeriodicalIF":9.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678188","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}

引用次数: 0

Enhanced energy conversion in a concentrated photovoltaic/thermal system using phase change material as a spectral beam filter

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-22 DOI: 10.1016/j.enconman.2025.119751

Yue Yang , Mostafa M. Abd El-Samie , Ahmed M.I. Abutalib , Mohamed I. Hassan Ali , Lei Zhou

引用次数: 0

Integration of direct air capture with Allam cycle: Innovative pathway in negative emission technologies

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-22 DOI: 10.1016/j.enconman.2025.119746

Alireza Ghorbani , Ayat Gharehghani , Jabraeil Ahbabi Saray , Amin Mahmoudzadeh Andwari , Tohid N. Borhani

{"title":"Integration of direct air capture with Allam cycle: Innovative pathway in negative emission technologies","authors":"Alireza Ghorbani , Ayat Gharehghani , Jabraeil Ahbabi Saray , Amin Mahmoudzadeh Andwari , Tohid N. Borhani","doi":"10.1016/j.enconman.2025.119746","DOIUrl":"10.1016/j.enconman.2025.119746","url":null,"abstract":"<div><div>The advancement of negative emission technologies (NETs) is crucial for addressing climate change by reducing atmospheric carbon dioxide levels. This study presents a comprehensive evaluation of a High Temperature Direct Air Capture (HT-DAC) system integrated with a supercritical CO<sub>2</sub> (S-CO<sub>2</sub>) cycle, representing a significant advancement in carbon capture, energy optimization, and NET systems. Given to significant energy demands of HT-DAC, the primary objective of this research is to address the process’s energy intensity by focusing on the development of a more efficient power island. Specifically, this study investigates the energy demands of the Air Separation Unit (ASU) to minimize energy consumption and improve the overall efficiency of the Allam cycle when coupled with the ASU. Additionally, the study examines the thermal integration of the system using pinch analysis to assess the impact of this innovative power island on energy efficiency. Key results indicate that the proposed system is capable of capturing 0.99 million tons of CO<sub>2</sub> per year directly from the air, achieving a capture efficiency of 75 %. The specific energy requirement for the process is initially 3.19 kWh per kg of captured CO<sub>2</sub>, which is reduced to 2.21 kWh/kgCO<sub>2</sub> following process optimization and heat integration. Through this optimization, hot and cold utility demands are reduced by 69.7 % and 36.9 %, respectively, while 110.1 MW of heat is recovered through the design of heat exchangers network, resulting in an 9.66 % reduction in overall energy demand compared to the base case. Furthermore, the integration of captured and regenerated CO<sub>2</sub> (135.1 tons per hour with a purity of 98.1 mol%) offers substantial potential for synthetic fuel production and underground storage.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119746"},"PeriodicalIF":9.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678194","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}

引用次数: 0

Advanced power curve modeling for wind turbines: A multivariable approach with SGBRT and grey wolf optimization

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-20 DOI: 10.1016/j.enconman.2025.119680

Wenliang Yin , Mengqian Jia , Lin Liu , Ming Li , Youguang Guo , Gang Lei , Jian Guo Zhu

{"title":"Advanced power curve modeling for wind turbines: A multivariable approach with SGBRT and grey wolf optimization","authors":"Wenliang Yin , Mengqian Jia , Lin Liu , Ming Li , Youguang Guo , Gang Lei , Jian Guo Zhu","doi":"10.1016/j.enconman.2025.119680","DOIUrl":"10.1016/j.enconman.2025.119680","url":null,"abstract":"<div><div>Accurate power curve modeling is crucial for improving the operational efficiency and performance of grid-connected wind turbines (WTs). To enhance the modeling quality and eliminate input variable interactions, this paper proposes a novel multivariable power curve prediction approach that integrates advanced machine learning techniques, namely stochastic gradient boosting regression tree (SGBRT) and grey wolf optimization (GWO), with innovative data preprocessing and feature selection methods. The specific works and novelties are as follows. 1) The raw data is cleaned in a two-dimensional Copula space, using wind wheel speed as an auxiliary criterion and a probabilistic description, to handle data uncertainties and nonlinear dependencies. 2) A partial mutual information (PMI) method is presented for data characteristics analysis, based on which eight significant parameters are selected as modeling input variables, reducing computational complexity while enhancing prediction accuracy. 3) A power curve prediction model considering multiple input variables is established using SGBRT, and its hyperparameters are optimized through a GWO algorithm, guided by a fitness function combining the indicators of root mean square error (RMSE), mean absolute error (MAE) and R squared (R<sup>2</sup>). 4) Validated with real SCADA data from WTs in service, the proposed model achieves superior performance, with the smallest standardized residuals (6.56 %), RMSE (around 27 kW), MAE (19.27 kW), and superior average R<sup>2</sup> (98.61 %) for all speed regions. Comparative studies indicate that the proposed approach outperforms existing methods, offering significant improvements in accuracy, efficiency, robustness and adaptability for WT power curve modeling.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119680"},"PeriodicalIF":9.9,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681935","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}

引用次数: 0

Selection of oxygen carrier for chemical looping combustion of natural gas and syngas fuels – A machine learning approach

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-19 DOI: 10.1016/j.enconman.2025.119745

Bilal Ahmed , Atta Ullah , Rehan Zubair Khalid , Muhammad Shahid , Liang Zeng , Xubin Zhang , Muhammad Zaman

{"title":"Selection of oxygen carrier for chemical looping combustion of natural gas and syngas fuels – A machine learning approach","authors":"Bilal Ahmed , Atta Ullah , Rehan Zubair Khalid , Muhammad Shahid , Liang Zeng , Xubin Zhang , Muhammad Zaman","doi":"10.1016/j.enconman.2025.119745","DOIUrl":"10.1016/j.enconman.2025.119745","url":null,"abstract":"<div><div>This research focuses on selecting suitable oxygen carriers (OCs) using data driven modeling in order to prevent operational issues such as agglomeration, attrition, and sintering, which are challenges in chemical looping combustion (CLC) operations. The complexity of choosing effective OCs arises from the diverse compositions of natural ores and synthetic compounds used in the process. In this work, eight machine learning techniques were employed to predict the performance of oxygen carriers using a parameter known as gas yield under different operating temperatures for gaseous fuels primarily natural gas and syngas. A comprehensive dataset including experimental data from the literature for various carriers were used to train multiple machine learning models. The models predicted gas yield with knowledge of reactor operating temperature, fuel composition, and the elemental makeup of oxygen carriers. Cross-validation and bootstrap techniques were employed to ensure model robustness and minimize prediction error. The results demonstrate that the GBR and CatBoost have been the best-performing model achieving a high coefficient of determination 0.820 and 0.822 value respectively and same low mean error value of 0.015. It was observed that Fe and Mn based mixed oxide performed as good OCs with their reactivity increasing with Fe to Mn ratio. This study highlights the potential of machine learning in optimizing oxygen carrier performance and accelerating advancements in CLC technology.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119745"},"PeriodicalIF":9.9,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681936","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}

引用次数: 0

Improving energy efficiency through forecast-driven control in hybrid heat pumps

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-18 DOI: 10.1016/j.enconman.2025.119737

Marco Bizzarri, Paolo Conti, Eva Schito, Daniele Testi

{"title":"Improving energy efficiency through forecast-driven control in hybrid heat pumps","authors":"Marco Bizzarri, Paolo Conti, Eva Schito, Daniele Testi","doi":"10.1016/j.enconman.2025.119737","DOIUrl":"10.1016/j.enconman.2025.119737","url":null,"abstract":"<div><div>Hybrid heat pumps (HHPs) are increasingly used for residential space heating, especially where stand-alone heat pumps (HPs) are inefficient. Typically, HHPs and HVAC systems controls rely on simple rule-based approaches. Smart controllers that employ building-system modeling can improve energy efficiency by determining which heat generation unit to activate and setting the supply water temperature according to actual building heat demand. Data-driven models are particularly suitable for widespread use, as they can self-learn building thermal characteristics and optimize system operation. In this study, we employed an autoregressive model to forecast short-term hourly energy demand and the corresponding water supply temperature to the heat emitters. These predictions helped to estimate generators performance and select the optimal unit to minimize energy costs while meeting heat demand. The predictive control procedure was tested on various case studies, both simulated and field-monitored, representative of the Italian housing stock. Results showed that in non-renovated buildings with radiators, the predictive control strategy can reduce operating costs by up to 20% compared to current commercial HHP controls. This improvement was mainly due to better supply temperature set-point evaluation and increased HP use. Similar benefits were observed in environmental and primary energy metrics. Conversely, in newer, well-insulated houses with low-temperature emitters, current controls are already efficient. Finally, we showed that the proposed control strategy deviates less than 3% from an ideal prediction and control in realistic on-field monitored test cases, representing a valuable trade-off between achievable benefits, data requirements, computational efforts, and implementation feasibility in real industrial HHP devices.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119737"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643348","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}

引用次数: 0

Toward a green steel production powered by a hybrid renewable energy system: Techno-economic and environmental assessment

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-18 DOI: 10.1016/j.enconman.2025.119716

Pouriya Nasseriyan , Saeed Jafari , Hossein Khajehpour , Saeed Edalati

{"title":"Toward a green steel production powered by a hybrid renewable energy system: Techno-economic and environmental assessment","authors":"Pouriya Nasseriyan , Saeed Jafari , Hossein Khajehpour , Saeed Edalati","doi":"10.1016/j.enconman.2025.119716","DOIUrl":"10.1016/j.enconman.2025.119716","url":null,"abstract":"<div><div>The steel supply chain consists of several stages, with the most energy-demanding phase being the direct reduction iron process. Syngas, used in this stage, is typically produced from fossil fuels like natural gas, which leads to substantial greenhouse gas emissions. In this study, the direct reduction iron production stage was replaced with a proposed process aimed at reducing both energy consumption and emissions while maintaining economic viability. The proposed process (Solid Oxide Electrolyzer − Direct Reduction Iron) was introduced as a potential solution and was compared technically, environmentally, and economically with the conventional (Steam Methane Reforming − Direct Reduction Iron) process. In the proposed process, solid oxide electrolysis cells are used to produce syngas, with the required electrical and thermal energy supplied from renewable sources, solar power and biogas. The results were validated after modeling the process and performing an economic analysis. The comparison between key performance indicators of the two processes highlighted three main findings: first, energy consumption per unit of production decreased by 17 %, from 3.06 MWh/ton<sub>DRI</sub> to 2.53 MWh/ton<sub>DRI</sub>, while energy efficiency improved by 33 %, rising from 52 % to 85 %. Second, the proposed process resulted in near-zero emissions (Green Steel) production due to the use of renewable energy sources. Third, from an economic perspective, considering the carbon emission penalty, the levelized cost of production for the conventional process was 245 $/ton<sub>DRI</sub>, making it comparable to the 249 $/ton<sub>DRI</sub> cost for the proposed process.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119716"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643349","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}

引用次数: 0

Exergo-environmental cost optimization of a wind-solar integrated tri-generation system through heterogeneous energy storage and carbon trading mechanisms

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-18 DOI: 10.1016/j.enconman.2025.119741

Yuzhu Chen , Kaifeng Yang , Weimin Guo , Na Du , Kun Yang , Tianhu Zhang , Liying Qi , Peter D. Lund

{"title":"Exergo-environmental cost optimization of a wind-solar integrated tri-generation system through heterogeneous energy storage and carbon trading mechanisms","authors":"Yuzhu Chen , Kaifeng Yang , Weimin Guo , Na Du , Kun Yang , Tianhu Zhang , Liying Qi , Peter D. Lund","doi":"10.1016/j.enconman.2025.119741","DOIUrl":"10.1016/j.enconman.2025.119741","url":null,"abstract":"<div><div>Global energy consumption is increasing due to rising living standards and industrial growth, leading to an escalating demand for clean energy sources. However, the inherent volatility of renewable energy coupled with fluctuating demand presents substantial challenges to system stability. To achieve energy balance between the system and users while enhancing the integration of wind and solar resources, a solar-wind-gas coupling tri-generation system is constructed that incorporates diverse energy storage solutions, including thermal, gas, electrical, and hydrogen storages. To identify optimal dispatch schedules for these devices, an exergo-environmental cost approach is employed aiming to minimize operational costs of energy products while factoring in ladder carbon trading. Results indicate that the scenario integrating wind turbines and photovoltaic/thermal units yields the best performance, with a carbon cost of $124.6, zero power cost, and the lowest specific cost per kWh at $0.029. Sensitivity analysis indicates that higher carbon pricing encourages the use of lower carbon-intensive energy sources, which leads to reduced natural gas costs but an increase in specific exergo-environmental costs. This study underscores the potential of combining renewable technologies with heterogeneous energy storage systems to optimize exergo-environmental cost performance.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119741"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642900","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}

引用次数: 0

Comparative study of different engine knock metrics for bracketing the octane number of fuels

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-18 DOI: 10.1016/j.enconman.2025.119744

Xinyang Wang , Kalim Uddeen , Tawfik Badawy , Mebin Samuel Panithasan , Jie Hu , Arjun B. Narayanamurthy , James W.G. Turner

{"title":"Comparative study of different engine knock metrics for bracketing the octane number of fuels","authors":"Xinyang Wang , Kalim Uddeen , Tawfik Badawy , Mebin Samuel Panithasan , Jie Hu , Arjun B. Narayanamurthy , James W.G. Turner","doi":"10.1016/j.enconman.2025.119744","DOIUrl":"10.1016/j.enconman.2025.119744","url":null,"abstract":"<div><div>This study presents a comparative analysis of different engine knock metrics used to evaluate the octane number (ON) of fuels in a Cooperative Fuel Research (CFR) engine. The knock metrics examined include knock intensity 20 (KI20), the maximum amplitude of pressure oscillations (MAPO), the maximum pressure rise rate (MPRR), the cumulative knock intensity (CKI), and the wavelet decomposition energy (WDE). Modified versions of standard CFR engine tests were conducted using both liquid and gaseous fuels, covering a range of research octane number (RON) from 60 to 100. The knock data were collected using both a detonation meter and an in-cylinder pressure transducer to compare traditional and pressure-based knock measurement methods. Results indicate that of the metrics investigated, MPRR is the most effective for bracketing octane numbers, showing higher validity and a closer resemblance to knockmeter readings compared to the others analyzed. Furthermore, the study explores the knock resistance of hydrogen, revealing discrepancies with standard RON evaluations. The findings of this work indicates that hydrogen’s RON, evaluated based on MPRR, falls within the range of 98–100. The results provide valuable insights for improving knock measurement accuracy, especially when evaluating fuels with high knock resistance, and for optimizing modern engine designs to meet emerging fuel standards.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119744"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642999","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}

引用次数: 0

Modelling of biomass gasification for fluidized bed in Aspen Plus: Using machine learning for fast pyrolysis prediction

IF 9.9 1区工程技术

Energy Conversion and Management Pub Date : 2025-03-18 DOI: 10.1016/j.enconman.2025.119695

Hao Shi, Yaji Huang, Yizhuo Qiu, Jun Zhang, Zhiyuan Li, Huikang Song, Tianhang Tang, Yixuan Xiao, Hao Liu

{"title":"Modelling of biomass gasification for fluidized bed in Aspen Plus: Using machine learning for fast pyrolysis prediction","authors":"Hao Shi, Yaji Huang, Yizhuo Qiu, Jun Zhang, Zhiyuan Li, Huikang Song, Tianhang Tang, Yixuan Xiao, Hao Liu","doi":"10.1016/j.enconman.2025.119695","DOIUrl":"10.1016/j.enconman.2025.119695","url":null,"abstract":"<div><div>The potential offered by biomass to upgrade into more valuable products via gasification is now being widely recognized globally. Due to difference of pyrolysis conditions, conventional Aspen modelling is challenged for bubbling fluidized bed(BFB) biomass gasification. In this work, a novel approach is developed for Aspen biomass gasification in BFB, combined with machine learning. Machine learning is utilized for biomass fast pyrolysis char and gas prediction. A sub-model for pyrolysis products evolution lumping equilibrium is then established via element balance calculation for predicted fast pyrolysis products compositions. Subsequent gasification in gasifier is controlled kinetically. Evaluation and discussion have been carried out on the method feasibility and precision of gasification products prediction in current model. Comparative analysis with six sets of experimental data reveals that most relative errors of syngas composition are controlled within ± 20 %, with half of them falling within ± 10 %. New model demonstrates satisfying accuracy and adaptability for different feedstock, attributed to application of machine learning in fast pyrolysis products prediction. Sensitivity analysis confirms current model’s capability to simulate trends of syngas compositions under varying gasification conditions correctly. Modules contribution analysis indicates that further promotion of accuracy can be achieved by refining tar cracking prediction and element equilibrium. Through present method, modelling for feedstock whose pyrolysis kinetics are unknown is not limited to thermodynamic equilibrium and can obtain higher accuracy and feedstock scalability. It provides original insight for more reasonable Aspen modelling and comprehensive usage of Aspen-machine learning combination.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"332 ","pages":"Article 119695"},"PeriodicalIF":9.9,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643397","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}

引用次数: 0