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Enabling thermal-neutral electrolysis for CO2-to-fuel conversions with a hybrid deep learning strategy

journal contribution
posted on 04.08.2021, 13:52 by Haoran Xu, Jingbo Ma, Peng Tan, Zhen Wu, Yanxiang Zhang, Meng Ni, Jin XuanJin Xuan
High-temperature co-electrolysis of CO2/H2O through the solid oxide electrolysis cells (SOECs) is a promising method to generate renewable fuels and chemical feedstocks. Applying this technology in flexible scenario, especially when combined with variable renewable powers, requires an efficient optimisation strategy to ensure its safety and cost-effective in the long-term operation. To this purpose, we present a hybrid simulation method for the accurate and fast optimisation of the co-electrolysis process in the SOECs. This method builds multi-physics models based on experimental data and extends the database to develop the deep neural network and genetic algorithm. In the case study, thermal-neutral condition (TNC) is set as the optimisation target in various operating conditions, where the SOEC generates no waste heat and needs no auxiliary heating equipment. Small peak-temperature-gradient (PTG) inside the SOEC is found at the TNC, which is vital to prevent thermal failure in the operation. For the cell operating with 1023 K and 1123 K of inlet gas temperatures, the smallest PTGs reach 0.09 and 0.31 K mm−1 at 1.13 and 1.19 V, respectively. Finally, a 4-D map is presented to show the interactions among the applied voltage, required power density, inlet gas composition, and temperature under the TNC. The proposed method can be flexibly modified based on different optimisation targets for various applications in the energy sector.

Funding

Research Grant Council, University Grant Committee, Hong Kong SAR (Project nos. PolyU 152214/17E and PolyU 152064/18E)

Royal Society Grant no. NAF\R1\180146

CAS Pioneer Hundred Talents Program (KJ 2090130001), USTC Research Funds of the Double First-Class Initiative (YD 2090002006), and USTC Tang Scholar

Natural Science Foundation of China (21673062)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Energy Conversion and Management

Volume

230

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Energy Conversion and Management and the definitive published version is available at https://doi.org/10.1016/j.enconman.2021.113827.

Acceptance date

04/01/2021

Publication date

2021-01-15

Copyright date

2021

ISSN

0196-8904

eISSN

1879-2227

Language

en

Depositor

Prof Jin Xuan. Deposit date: 29 July 2021

Article number

113827