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Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning

Author

Listed:
  • Amira Mohamed

    (School of Mechanical Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Hatem Ibrahem

    (School of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Rui Yang

    (School of Mechanical Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Kibum Kim

    (School of Mechanical Engineering, Chungbuk National University, Cheongju 28644, Korea
    Physics and Engineering Department, North Park University, Chicago, IL 60625, USA)

Abstract

We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production.

Suggested Citation

  • Amira Mohamed & Hatem Ibrahem & Rui Yang & Kibum Kim, 2022. "Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning," Energies, MDPI, vol. 15(18), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6657-:d:912880
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    References listed on IDEAS

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    1. Lee, Hye-One & Yesuraj, Johnbosco & Kim, Kibum, 2022. "Parametric study to optimize proton exchange membrane electrolyzer cells," Applied Energy, Elsevier, vol. 314(C).
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    Cited by:

    1. Mahyar Jahaninasab & Ehsan Taheran & S. Alireza Zarabadi & Mohammadreza Aghaei & Ali Rajabpour, 2023. "A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers," Energies, MDPI, vol. 16(13), pages 1-13, July.
    2. Mohsen Fallah Vostakola & Hasan Ozcan & Rami S. El-Emam & Bahman Amini Horri, 2023. "Recent Advances in High-Temperature Steam Electrolysis with Solid Oxide Electrolysers for Green Hydrogen Production," Energies, MDPI, vol. 16(8), pages 1-50, April.

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