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Optimization of catalyst distribution along PEMFC channel through a numerical two-phase model and genetic algorithm

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  • Ebrahimi, Sasan
  • Ghorbani, Babak
  • Vijayaraghavan, Krishna

Abstract

In this paper, a new approach is presented to find the optimum catalyst loading distribution along the flow field. The optimization is performed by integrating a computational fluid dynamic (CFD) model and genetic algorithm optimization method. The CFD model is two-dimensional, steady state and two-phase. Multiphase mixture model (M2) is used to model two-phase transport in porous media of a Polymer Electrolyte Membrane Fuel Cell (PEMFC). Numerical domain includes channel, gas diffusion layer (GDL) and catalyst layer (CL) in the cathode side. In the next step, current density is assumed to be proportional with catalyst loading. Catalyst loading is considered as polynomial functions with unknown coefficients. Genetic algorithm optimization method is applied to find the unknown coefficients and as a result the optimum catalyst loading function along the flow field. The results indicate that catalyst loading distribution has a significant effect on the fuel cell performance and it is seen that in the optimum case, maximum PEMFC power density is increased by about 14%.

Suggested Citation

  • Ebrahimi, Sasan & Ghorbani, Babak & Vijayaraghavan, Krishna, 2017. "Optimization of catalyst distribution along PEMFC channel through a numerical two-phase model and genetic algorithm," Renewable Energy, Elsevier, vol. 113(C), pages 846-854.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:846-854
    DOI: 10.1016/j.renene.2017.06.067
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    References listed on IDEAS

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    1. Roshandel, Ramin & Ahmadi, Farzad, 2013. "Effects of catalyst loading gradient in catalyst layers on performance of polymer electrolyte membrane fuel cells," Renewable Energy, Elsevier, vol. 50(C), pages 921-931.
    2. Huang, Yu-Xian & Cheng, Chin-Hsiang & Wang, Xiao-Dong & Jang, Jiin-Yuh, 2010. "Effects of porosity gradient in gas diffusion layers on performance of proton exchange membrane fuel cells," Energy, Elsevier, vol. 35(12), pages 4786-4794.
    3. Poornesh, K.K. & Cho, Chongdu & Kim, Do-Young & Tak, Yongsug, 2010. "Effect of gas-diffusion electrode material heterogeneity on the structural integrity of polymer electrolyte fuel cell," Energy, Elsevier, vol. 35(12), pages 5241-5249.
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    1. Nassef, Ahmed M. & Fathy, Ahmed & Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Tanveer, Waqas Hassan & Olabi, A.G., 2019. "Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms," Renewable Energy, Elsevier, vol. 138(C), pages 458-464.
    2. Qian, Zhang & Hongwei, Wang & Chunlei, Liu & Yi, An, 2024. "Establishment and identification of MIMO fractional Hammerstein model with colored noise for PEMFC system," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    3. Feng, ShengSen & Huang, WenTao & Huang, Zhe & Jian, Qifei, 2022. "Optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model," Applied Energy, Elsevier, vol. 317(C).
    4. Fan, Ruijia & Chang, Guofeng & Xu, Yiming & Xu, Jiamin, 2023. "Multi-objective optimization of graded catalyst layer to improve performance and current density uniformity of a PEMFC," Energy, Elsevier, vol. 262(PB).
    5. Wei, Pengnan & Chang, Guofeng & Fan, Ruijia & Xu, Yiming & Chen, Siqi, 2023. "Investigation of output performance and temperature distribution uniformity of PEMFC based on Pt loading gradient design," Applied Energy, Elsevier, vol. 352(C).

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