Gast ::
| Hauptseite > Publikationsdatenbank > Evaluating activation strategies and their stability on PEM water electrolyzers using machine learning > print |
| Hauptseite > Publikationsdatenbank > Evaluating activation strategies and their stability on PEM water electrolyzers using machine learning > print |
| 001 | 1051608 | ||
| 005 | 20260116204431.0 | ||
| 024 | 7 | _ | |a 10.1016/j.egyai.2025.100623 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2026-00531 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2026-00531 |
| 082 | _ | _ | |a 624 |
| 100 | 1 | _ | |a Raman, K. Ashoke |0 P:(DE-Juel1)198986 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Evaluating activation strategies and their stability on PEM water electrolyzers using machine learning |
| 260 | _ | _ | |a Amsterdam |c 2025 |b Elsevier ScienceDirect |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1768572647_30552 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Pre-treatment of the proton exchange membrane water electrolyzers is a crucial procedure performed prior to its regular operation. These procedures help in catalyst activation and membrane saturation, thereby, ensuring its optimal performance. In this study, we use machine learning to investigate the impact of three distinct activation procedures on the cell performance and stability. The data set necessary to develop the surrogate models was obtained from a lab scale PEM electrolyzer cell. After evaluating the performance of the three tested models and validating them with experimental data, extreme gradient boosting is selected as the to perform parametric analysis. The modeling predictions reveal that the activation procedures mainly impact the ohmic resistance at the beginning of the cell life. These observations were further corroborated using through sensitivity analysis performed through an explainable artificial intelligence technique. Furthermore, data-driven time-series forecasting analysis to predict cell stability for different activation procedures showed a good comparison between experimental data and model predictions |
| 536 | _ | _ | |a 1231 - Electrochemistry for Hydrogen (POF4-123) |0 G:(DE-HGF)POF4-1231 |c POF4-123 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Wolf, Niklas L. |0 P:(DE-Juel1)190997 |b 1 |u fzj |
| 700 | 1 | _ | |a Javed, Ali |0 P:(DE-Juel1)196699 |b 2 |u fzj |
| 700 | 1 | _ | |a Karyofylli, Violeta |0 P:(DE-Juel1)194150 |b 3 |u fzj |
| 700 | 1 | _ | |a Kungl, Hans |0 P:(DE-Juel1)157700 |b 4 |u fzj |
| 700 | 1 | _ | |a Karl, André |0 P:(DE-Juel1)191359 |b 5 |u fzj |
| 700 | 1 | _ | |a Jodat, Eva |0 P:(DE-Juel1)161579 |b 6 |u fzj |
| 700 | 1 | _ | |a Eichel, Rüdiger-A. |0 P:(DE-Juel1)156123 |b 7 |u fzj |
| 773 | _ | _ | |a 10.1016/j.egyai.2025.100623 |g Vol. 22, p. 100623 - |0 PERI:(DE-600)3017958-0 |p 100623 |t Energy and AI |v 22 |y 2025 |x 2666-5468 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1051608/files/1-s2.0-S2666546825001557-main.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1051608 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)198986 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)190997 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)196699 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)194150 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)157700 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)191359 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)161579 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)156123 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 7 |6 P:(DE-Juel1)156123 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Energie |l Materialien und Technologien für die Energiewende (MTET) |1 G:(DE-HGF)POF4-120 |0 G:(DE-HGF)POF4-123 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-100 |4 G:(DE-HGF)POF |v Chemische Energieträger |9 G:(DE-HGF)POF4-1231 |x 0 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-06 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2023-05-02T08:55:10Z |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0112 |2 StatID |b Emerging Sources Citation Index |d 2024-12-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2023-05-02T08:55:10Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-06 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2023-05-02T08:55:10Z |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2024-12-06 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2024-12-06 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-06 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IET-1-20110218 |k IET-1 |l Grundlagen der Elektrochemie |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IET-1-20110218 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|