Multi-objective optimizations of solid oxide co-electrolysis with intermittent renewable power supply via multi-physics simulation and deep learning strategy

Yi Sun, Jiong Lu, Qiuhua Liu, Wei Shuai, Anwei Sun, Nan Zheng, Yu Han, Gang Xiao, Jin Xuan, Meng Ni, Haoran Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Solid oxide electrolysis cell (SOEC) is a novel approach to utilize excess renewable power to produce fuels and chemicals. However, the intermittence and fluctuation of renewable energy requires more advanced optimization strategy to make sure its performance in safety and cost-effectiveness. Here, we propose a hybrid model for the precise and quick optimization of the co-electrolysis process in the SOEC for syngas production, based on the multi-physics simulation (MPS) and deep learning algorithm. The hybrid model fully considers electrochemical/chemical reactions, mass/momentum transport and heat transfer, and presents a small relative error (<1%) in most the cases (>96%). Various targets including the single-objective, dual-objective and multi-objective optimizations are evaluated with particular attentions on the reactant conversion rate and energy efficiency at different temperatures. The electrolysis efficiency is negatively correlated with the power supply in all strategies and thermal neutral condition (TNC) can be achieved at different temperatures, where 1023 K, 1053 K, 1083 K and 1113 K are corresponded to the TNC power range of 10–16 W, 14–23 W, 18–29 W and 22–37 W, respectively. This theory can be flexibly applied in the sustainable manufacturing and circular economy sectors and energy according to the optimization targets.

Original languageEnglish
Article number115560
JournalEnergy Conversion and Management
Publication statusPublished - 15 Apr 2022


  • Co-electrolysis
  • Deep learning
  • Numerical simulation
  • Renewable powers
  • Solid oxidation electrolysis cell

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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