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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Bibliographic Details
Published in:	Applied energy Vol. 315; p. 119046
Main Authors:	Wang, Yang, Wu, Chengru, Zhao, Siyuan, Wang, Jian, Zu, Bingfeng, Han, Minfang, Du, Qing, Ni, Meng, Jiao, Kui
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.06.2022
Subjects:	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 Deep learning Global sensitivity analysis Carbon deposition Multi-objective optimization Solid oxide fuel cell
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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