Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning

Optimization of Proton Exchange Membrane Electrolyzer Cell Design Using Machine Learning

We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different inp...

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Bibliographic Details
Main Authors: Amira Mohamed, Hatem Ibrahem, Rui Yang, Kibum Kim
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Energies
Subjects:
PEM water electrolysis
machine learning
cell design
hydrogen production
Online Access:https://www.mdpi.com/1996-1073/15/18/6657

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