Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell
[Display omitted] •A novel framework is proposed for DIR-SOFC optimization.•A comprehensive parameter study is performed by developing a multi-physics model.•The surrogate model for fast prediction is built using a deep learning algorithm.•The Pareto fronts are obtained by the multi-objective geneti...
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| Vydáno v: | Applied energy Ročník 315; s. 119046 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2022
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Abstract | [Display omitted]
•A novel framework is proposed for DIR-SOFC optimization.•A comprehensive parameter study is performed by developing a multi-physics model.•The surrogate model for fast prediction is built using a deep learning algorithm.•The Pareto fronts are obtained by the multi-objective genetic algorithms.•A significant reduction of carbon deposition is achieved.
Direct internal reforming (DIR) operation of solid oxide fuel cell (SOFC) reduces system complexity, improves system efficiency but increases the risk of carbon deposition which can reduce the system performance and durability. In this study, a novel framework that combines a multi-physics model, deep learning, and multi-objective optimization algorithms is proposed for improving SOFC performance and minimizing carbon deposition. The sensitive operating parameters are identified by performing a global sensitivity analysis. The results of parameter analysis highlight the effects of overall temperature distribution and methane flux on carbon deposition. It is also found that the reduction of carbon deposition is accompanied by a decrease in cell performance. Besides, it is found that the coupling effects of electrochemical and chemical reactions cause a higher temperature gradient. Based on the parametric simulations, multi-objective optimization is conducted by applying a deep learning-based surrogate model as the fitness function. The optimization results are presented by the Pareto fronts under different temperature gradient constraints. The Pareto optimal solution set of operating points allows a significant reduction in carbon deposition while maintaining a high power density and a safe maximum temperature gradient, increasing cell durability. This novel approach is demonstrated to be powerful for the optimization of SOFC and other energy conversion devices. |
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| AbstractList | [Display omitted]
•A novel framework is proposed for DIR-SOFC optimization.•A comprehensive parameter study is performed by developing a multi-physics model.•The surrogate model for fast prediction is built using a deep learning algorithm.•The Pareto fronts are obtained by the multi-objective genetic algorithms.•A significant reduction of carbon deposition is achieved.
Direct internal reforming (DIR) operation of solid oxide fuel cell (SOFC) reduces system complexity, improves system efficiency but increases the risk of carbon deposition which can reduce the system performance and durability. In this study, a novel framework that combines a multi-physics model, deep learning, and multi-objective optimization algorithms is proposed for improving SOFC performance and minimizing carbon deposition. The sensitive operating parameters are identified by performing a global sensitivity analysis. The results of parameter analysis highlight the effects of overall temperature distribution and methane flux on carbon deposition. It is also found that the reduction of carbon deposition is accompanied by a decrease in cell performance. Besides, it is found that the coupling effects of electrochemical and chemical reactions cause a higher temperature gradient. Based on the parametric simulations, multi-objective optimization is conducted by applying a deep learning-based surrogate model as the fitness function. The optimization results are presented by the Pareto fronts under different temperature gradient constraints. The Pareto optimal solution set of operating points allows a significant reduction in carbon deposition while maintaining a high power density and a safe maximum temperature gradient, increasing cell durability. This novel approach is demonstrated to be powerful for the optimization of SOFC and other energy conversion devices. Direct internal reforming (DIR) operation of solid oxide fuel cell (SOFC) reduces system complexity, improves system efficiency but increases the risk of carbon deposition which can reduce the system performance and durability. In this study, a novel framework that combines a multi-physics model, deep learning, and multi-objective optimization algorithms is proposed for improving SOFC performance and minimizing carbon deposition. The sensitive operating parameters are identified by performing a global sensitivity analysis. The results of parameter analysis highlight the effects of overall temperature distribution and methane flux on carbon deposition. It is also found that the reduction of carbon deposition is accompanied by a decrease in cell performance. Besides, it is found that the coupling effects of electrochemical and chemical reactions cause a higher temperature gradient. Based on the parametric simulations, multi-objective optimization is conducted by applying a deep learning-based surrogate model as the fitness function. The optimization results are presented by the Pareto fronts under different temperature gradient constraints. The Pareto optimal solution set of operating points allows a significant reduction in carbon deposition while maintaining a high power density and a safe maximum temperature gradient, increasing cell durability. This novel approach is demonstrated to be powerful for the optimization of SOFC and other energy conversion devices. |
| ArticleNumber | 119046 |
| Author | Wang, Yang Wang, Jian Du, Qing Wu, Chengru Zu, Bingfeng Zhao, Siyuan Ni, Meng Jiao, Kui Han, Minfang |
| Author_xml | – sequence: 1 givenname: Yang surname: Wang fullname: Wang, Yang organization: State Key Laboratory of Engines, Tianjin University, 135 Yaguan Road, Tianjin, China – sequence: 2 givenname: Chengru surname: Wu fullname: Wu, Chengru organization: State Key Laboratory of Engines, Tianjin University, 135 Yaguan Road, Tianjin, China – sequence: 3 givenname: Siyuan surname: Zhao fullname: Zhao, Siyuan organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD) & Research Institute for Smart Energy (RISE), Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 4 givenname: Jian surname: Wang fullname: Wang, Jian organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD) & Research Institute for Smart Energy (RISE), Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 5 givenname: Bingfeng surname: Zu fullname: Zu, Bingfeng organization: Internal Combustion Engine Research Institute, Tianjin University, 92 Weijin Road, Tianjin, China – sequence: 6 givenname: Minfang surname: Han fullname: Han, Minfang organization: Department of Energy and Power Engineering, Tsinghua University, Beijing, Beijing 100084, China – sequence: 7 givenname: Qing surname: Du fullname: Du, Qing organization: State Key Laboratory of Engines, Tianjin University, 135 Yaguan Road, Tianjin, China – sequence: 8 givenname: Meng surname: Ni fullname: Ni, Meng organization: Department of Building and Real Estate, Research Institute for Sustainable Urban Development (RISUD) & Research Institute for Smart Energy (RISE), Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China – sequence: 9 givenname: Kui surname: Jiao fullname: Jiao, Kui organization: State Key Laboratory of Engines, Tianjin University, 135 Yaguan Road, Tianjin, China |
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| Keywords | Deep learning Global sensitivity analysis Carbon deposition Multi-objective optimization Solid oxide fuel cell |
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•A novel framework is proposed for DIR-SOFC optimization.•A comprehensive parameter study is performed by developing a multi-physics... Direct internal reforming (DIR) operation of solid oxide fuel cell (SOFC) reduces system complexity, improves system efficiency but increases the risk of... |
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| SubjectTerms | carbon Carbon deposition Deep learning durability electrochemistry energy conversion fuel cells Global sensitivity analysis methane production Multi-objective optimization risk Solid oxide fuel cell temperature |
| Title | Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell |
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