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Ni coarsening and performance attenuation prediction of biomass syngas fueled SOFC by combining multi-physics field modeling and artificial neural network

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Listed:
  • Zhu, Pengfei
  • Wu, Zhen
  • Wang, Huan
  • Yan, Hongli
  • Li, Bo
  • Yang, Fusheng
  • Zhang, Zaoxiao

Abstract

Ni particle coarsening is an important factor in deteriorating the durability of solid oxide fuel cell (SOFC) operations. In order to investigate the influence of Ni coarsening on SOFC performance, the transient multi-physical field model of SOFC was developed in this paper. The high operating temperature accelerates Ni particle growth and increases the attenuation rate of SOFC current density from 0.23%/kh at 650 °C to 2.6%/kh at 800 °C. The increase in the ratio of steam to carbon also intensifies the Ni particle coarsening process and deteriorates the transient performance of SOFC. Increasing YSZ particle diameter could hinder the growth of Ni particles and slowing down the increase rate of Ni particle diameter. Within the range of preset YSZ diameter dYSZ, increasing dYSZ reduces the attenuation rate and increases the average current density. Improving Ni phase fraction helps to reduce the attenuation rate of current density. Since multi-physical field (MPF) simulation needs long calculation time and it is difficult to achieve fast prediction, artificial neural network (ANN) is trained by the database generated by MPF. The mapping relationship between operating parameters, structural parameters and attenuation indexes is obtained. Finally, the attenuation performance of SOFC is optimized by genetic algorithm (GA) through data-driven method. The absolute average relative errors of all parameters in predicting attenuation rate and average current density are as low as 0.767% and 0.248%, which indicates the reliability of the ANN prediction. After optimization, the maximum current density is 5848 A·m−2 under operating voltage at 0.6 V when the attenuation rate requirement not exceeding 1% is satisfied. The combination of MPF simulation, ANN and GA provides a framework for fast performance prediction and optimization of strong nonlinear system.

Suggested Citation

  • Zhu, Pengfei & Wu, Zhen & Wang, Huan & Yan, Hongli & Li, Bo & Yang, Fusheng & Zhang, Zaoxiao, 2022. "Ni coarsening and performance attenuation prediction of biomass syngas fueled SOFC by combining multi-physics field modeling and artificial neural network," Applied Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922008303
    DOI: 10.1016/j.apenergy.2022.119508
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    1. Zhu, Pengfei & Wu, Zhen & Yang, Yuchen & Wang, Huan & Li, Ruiqing & Yang, Fusheng & Zhang, Zaoxiao, 2023. "The dynamic response of solid oxide fuel cell fueled by syngas during the operating condition variations," Applied Energy, Elsevier, vol. 349(C).
    2. Ran, Peng & Ou, YiFan & Zhang, ChunYu & Chen, YuTong, 2024. "Energy, exergy, economic, and life cycle environmental analysis of a novel biogas-fueled solid oxide fuel cell hybrid power generation system assisted with solar thermal energy storage unit," Applied Energy, Elsevier, vol. 358(C).
    3. Jingxuan Peng & Dongqi Zhao & Yuanwu Xu & Xiaolong Wu & Xi Li, 2023. "Comprehensive Analysis of Solid Oxide Fuel Cell Performance Degradation Mechanism, Prediction, and Optimization Studies," Energies, MDPI, vol. 16(2), pages 1-23, January.

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