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@INPROCEEDINGS{Kuppa:1044516,
      author       = {Kuppa, Raman Ashoke and Hammacher, Linus and Karyofylli,
                      Violeta and Jodat, Eva and Karl, André and Eichel,
                      Rüdiger-A.},
      title        = {{D}ata-driven approach for modeling and sensitivity
                      analysis of a {P}roton-exchange membrane water electrolyzer},
      reportid     = {FZJ-2025-03253},
      year         = {2025},
      note         = {Acknowledgment: Financial support was provided by the
                      German Federal Ministry of Education and Research (BMBF)
                      within the H2Giga project DERIEL (grant number 03HY122C)},
      abstract     = {Proton exchange membrane electrolytic cells (PEMEC) have
                      emerged as a promising technology to produce environment
                      friendly green hydrogen. These are complex systems which
                      involve a highly non-linear interplay between mass transfer,
                      fluid flow and electrochemical reactions. To improve the
                      commercial viability of PEMECs, performance optimization of
                      these systems using data-driven models has emerged as a
                      promising approach [1]. In this work, we develop data-driven
                      surrogate models using synthetic data obtained from a
                      physics-based one-dimensional numerical model of PEMEC [2].
                      The predictive performance of three unique machine learning
                      algorithms [3] (support vector regression (SVR), extreme
                      gradient boosting (XGB) and artificial neural networks
                      (ANN)) were evaluated and their ability to capture the
                      inherent non-linearities of PEMEC were compared. Important
                      transport properties of the system such as operating
                      temperature, anode catalyst and porous transport layer
                      thickness, electronic conductivity and membrane thickness
                      were selected as the input features for developing the
                      data-driven models. Both XGB and ANN models showed better
                      performance in predicting the cell current density when
                      compared to SVR model. The ANN model was deployed to conduct
                      a parametric analysis to investigate the effect of operating
                      conditions and transport properties. Furthermore, an
                      explainable artificial intelligence technique known as SHAP
                      (Shapely Additive Explanations) was employed to list out the
                      important parameters influencing the cell current density.
                      The SHAP analysis highlight that the influence of membrane
                      thickness is higher than the electronic conductivity for
                      supported catalyst layers, and conversely, for unsupported
                      catalyst layers.},
      month         = {Mar},
      date          = {2025-03-11},
      organization  = {21st Symposium on Modeling and
                       Experimental Validation, Karlsruhe
                       (Germany), 11 Mar 2025 - 12 Mar 2025},
      subtyp        = {After Call},
      cin          = {IET-1},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123) / HITEC -
                      Helmholtz Interdisciplinary Doctoral Training in Energy and
                      Climate Research (HITEC) (HITEC-20170406)},
      pid          = {G:(DE-HGF)POF4-1231 / G:(DE-Juel1)HITEC-20170406},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1044516},
}