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@INPROCEEDINGS{Hammacher:1039720,
      author       = {Hammacher, Linus and Karyofylli, Violeta and Kuppa, Raman
                      Ashoke and Danner, Yannik and Kungl, Hans and Karl, André
                      and Jodat, Eva and Eichel, Rüdiger-A.},
      title        = {{E}lucidating {P}arasitic {C}urrents in {P}roton-{E}xchange
                      {M}embrane {E}lectrolytic {C}ells {V}ia {P}hysics-based and
                      {D}ata-driven {M}odeling},
      reportid     = {FZJ-2025-01768},
      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 (PEM) water electrolysis plays a
                      crucial role in green hydrogen production. To accelerate
                      commercial deployment, it is pertinent to use efficient
                      computational models which capture the inherent
                      non-linearities and aid to system optimization. This poster
                      presentation focuses on understanding degradation
                      mechanisms, particularly the impact of parasitic currents on
                      the performance of a PEM electrolytic cell (PEMEC) through
                      macro-scale modeling and uncertainty quantification (UQ)
                      [1]. Parasitic currents due to electron conduction through
                      the membrane are a frequently observed but not fully
                      understood degradation effect, leading to lower Faradaic
                      efficiency. One possible cause of these parasitic currents
                      is mechanical damage in the membrane-electrode assembly
                      (MEA) [2]. To specifically address the effect of such
                      parasitic currents on Faradaic efficiency and cell
                      performance under varying design parameters, we present a
                      one-dimensional steady-state physics-based model for PEMECs.
                      A comprehensive dataset from this model is generated and
                      used to train a machine learning (ML) surrogate model. Its
                      performance is analyzed to assess the potential of ML in
                      accurately and efficiently predicting the effects of
                      parasitic currents in PEMECs. The chosen ML algorithm,
                      eXtreme Gradient Boosting (XGBoost), excels in predicting
                      the polarization behavior while significantly reducing
                      computational demands. Using this ML surrogate model, UQ and
                      sensitivity analysis (SA) [3] are applied to investigate the
                      dependence of PEMEC performance and Faradaic efficiency on
                      the electronic conductivity of the PEM, especially when
                      electronic pathways are existent within the membrane and
                      operating at low current densities.References:[1] V.
                      Karyofylli, K. A. Raman, L. Hammacher, Y. Danner, H. Kungl,
                      A. Karl, E. Jodat, R.-A. Eichel, Accepted by Electrochemical
                      Science Advances on 01/2025[2] S. P. S. Badwal, S. Giddey,
                      F.T. Ciacchi, Ionics 12 (2006), 1, 7-14 [3] V. Karyofylli,
                      Y. Danner, K. A. Raman, H. Kungl, A. Karl, E. Jodat, R.-A.
                      Eichel, J. Power Sources 600 (2024), 234209},
      month         = {Mar},
      date          = {2025-03-11},
      organization  = {21st Symposium on Modeling and
                       Experimental Validation of
                       Electrochemical Energy Technologies,
                       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)24},
      url          = {https://juser.fz-juelich.de/record/1039720},
}