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Machine learning in proton exchange membrane water electrolysis — A knowledge-integrated framework

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  • Chen, Xia
  • Rex, Alexander
  • Woelke, Janis
  • Eckert, Christoph
  • Bensmann, Boris
  • Hanke-Rauschenbach, Richard
  • Geyer, Philipp

Abstract

In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development. Given the significance of PEMWE in green hydrogen production and the inherent challenges in optimizing its performance, our framework aims to provide a systematic overview of incorporating data-driven models with domain-specific insights to address the domain challenges. We first identify the uncertainties originating from data acquisition conditions, data-driven model mechanisms, and domain expertise, highlighting their complementary characteristics in carrying information from different perspectives. Building upon this foundation, we showcase how to adeptly decompose knowledge and extract unique information to contribute to the data augmentation, modeling process, and knowledge discovery. We demonstrate a hierarchical three-level framework, termed the ”Ladder of Knowledge-integrated Machine Learning,” in the PEMWE context, applying it to three case studies within a context of cell degradation analysis to affirm its efficacy in interpolation, extrapolation, and information representation. Initial results demonstrate improvements in interpolation accuracy by up to 30%, robustness in extrapolation by enhancing predictive stability across varied operational conditions, and enriched information representation that supports autonomous knowledge discovery. This research lays the groundwork for more knowledge-informed enhancements in ML applications in engineering.

Suggested Citation

  • Chen, Xia & Rex, Alexander & Woelke, Janis & Eckert, Christoph & Bensmann, Boris & Hanke-Rauschenbach, Richard & Geyer, Philipp, 2024. "Machine learning in proton exchange membrane water electrolysis — A knowledge-integrated framework," Applied Energy, Elsevier, vol. 371(C).
    • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924009334
    DOI: 10.1016/j.apenergy.2024.123550
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    References listed on IDEAS

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    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Tu, Hao & Moura, Scott & Wang, Yebin & Fang, Huazhen, 2023. "Integrating physics-based modeling with machine learning for lithium-ion batteries," Applied Energy, Elsevier, vol. 329(C).
    3. Papakonstantinou, Georgios & Algara-Siller, Gerardo & Teschner, Detre & Vidaković-Koch, Tanja & Schlögl, Robert & Sundmacher, Kai, 2020. "Degradation study of a proton exchange membrane water electrolyzer under dynamic operation conditions," Applied Energy, Elsevier, vol. 280(C).
    4. Lickert, Thomas & Fischer, Stefanie & Young, James L. & Klose, Selina & Franzetti, Irene & Hahn, Daniel & Kang, Zhenye & Shviro, Meital & Scheepers, Fabian & Carmo, Marcelo & Smolinka, Tom & Bender, G, 2023. "Advances in benchmarking and round robin testing for PEM water electrolysis: Reference protocol and hardware," Applied Energy, Elsevier, vol. 352(C).
    5. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
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    1. Raman, K. Ashoke & Hammacher, Linus & Kungl, Hans & Karl, André & Jodat, Eva & Eichel, Rüdiger-A. & Karyofylli, Violeta, 2025. "Data-driven surrogate modeling for performance prediction and sensitivity analysis of transport properties in proton exchange membrane water electrolyzers," Applied Energy, Elsevier, vol. 386(C).

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