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Artificial Neural Networks for Aging Simulation of Electrolysis Stacks
Author(s) -
Bahr Matthias,
Gusak Andreas,
Stypka Sebastian,
Oberschachtsiek Bernd
Publication year - 2020
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.202000089
Subject(s) - stack (abstract data type) , artificial neural network , electrolysis , extrapolation , degradation (telecommunications) , work (physics) , process (computing) , computer science , polymer electrolyte membrane electrolysis , electrolysis of water , process engineering , artificial intelligence , engineering , chemistry , electrolyte , mechanical engineering , electrode , mathematics , mathematical analysis , telecommunications , programming language , operating system
This work shows the application of artificial neural networks in terms of modeling and simulating the aging process and the degradation of proton exchange membrane water electrolysis stacks. It includes the training process based on extracted measurement data, the evaluation, and the extrapolation of the network. The fundamentals of the utilized artificial neural network and the training algorithm are clarified. Next, the principle degradation effects are presented as well as the methodology of the underlying measurements. The resulting degradation of the electrolysis stack for different operation conditions is shown.