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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DOI: 10.1016/j.apenergy.2022.119046
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- Yuan, Yi & Ding, Tao & Chang, Xinyue & Jia, Wenhao & Xue, Yixun, 2024. "A distributed multi-objective optimization method for scheduling of integrated electricity and hydrogen systems," Applied Energy, Elsevier, vol. 355(C).
- Li, Zheng & Yu, Jie & Wang, Chen & Bello, Idris Temitope & Yu, Na & Chen, Xi & Zheng, Keqing & Han, Minfang & Ni, Meng, 2024. "Multi-objective optimization of protonic ceramic electrolysis cells based on a deep neural network surrogate model," Applied Energy, Elsevier, vol. 365(C).
- Fu, Quanrong & Tian, Chunyu & Hun, Lianming & Wang, Xin & Li, Zhiyi & Liu, Zhijun & Wei, Wei, 2024. "Ni agglomeration and performance degradation of solid oxide fuel cell: A model-based quantitative study and microstructure optimization," Energy, Elsevier, vol. 289(C).
- Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.
- Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
- Gong, Chengyuan & Tu, Zhengkai & Hwa Chan, Siew, 2023. "A novel flow field design with flow re-distribution for advanced thermal management in Solid oxide fuel cell," Applied Energy, Elsevier, vol. 331(C).
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Keywords
Solid oxide fuel cell; Carbon deposition; Deep learning; Multi-objective optimization; Global sensitivity analysis;All these keywords.
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