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A multiphysics model of the compactly-assembled industrial alkaline water electrolysis cell

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  • Huang, Danji
  • Xiong, Binyu
  • Fang, Jiakun
  • Hu, Kewei
  • Zhong, Zhiyao
  • Ying, Yuheng
  • Ai, Xiaomeng
  • Chen, Zhe

Abstract

Electrolysis occupies a dominant position in the long-term application of hydrogen energy, as it can use the power surplus directly from renewable energies to produce hydrogen. Alkaline water electrolysis (AWE) is a mature and reliable technology standing out from other types of electrolysis because of its simplicity and low cost. Several multiphysics processes inside the AWE cell, such as the electrochemical, thermal, and fluidic processes. Developing the multiphysics model to quantify the relationship between these physics fields is essential for cell design. This paper establishes a three-dimensional numerical model to consider the quantitative relationship between the electrochemical process and fluidic process inside the cell of industrial AWE. The model considers the structural design of industrial AWE equipment, revealing that the shunting current effect introduced by the structure design cannot be ignored in the model. The simulation results present that the multiphysics model considering the bubble effect can estimate the current–voltage (I-V) characteristic curve more accurately with a relative error smaller than 5%, especially at a current density higher than 2500 A/m2. The model established is supposed to advance the development of water electrolysis models and guide the electrolyzer design of industrial AWE cell.

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  • Huang, Danji & Xiong, Binyu & Fang, Jiakun & Hu, Kewei & Zhong, Zhiyao & Ying, Yuheng & Ai, Xiaomeng & Chen, Zhe, 2022. "A multiphysics model of the compactly-assembled industrial alkaline water electrolysis cell," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003968
    DOI: 10.1016/j.apenergy.2022.118987
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    References listed on IDEAS

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    1. Qiu, Xiaoyan & Zhang, Hang & Qiu, Yiwei & Zhou, Yi & Zang, Tianlei & Zhou, Buxiang & Qi, Ruomei & Lin, Jin & Wang, Jiepeng, 2023. "Dynamic parameter estimation of the alkaline electrolysis system combining Bayesian inference and adaptive polynomial surrogate models," Applied Energy, Elsevier, vol. 348(C).
    2. Sakas, Georgios & Ibáñez-Rioja, Alejandro & Pöyhönen, Santeri & Järvinen, Lauri & Kosonen, Antti & Ruuskanen, Vesa & Kauranen, Pertti & Ahola, Jero, 2024. "Sensitivity analysis of the process conditions affecting the shunt currents and the SEC in an industrial-scale alkaline water electrolyzer plant," Applied Energy, Elsevier, vol. 359(C).

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