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Implementation of Neural Network Models for Parameter Estimation of a PEM-Electrolyzer

journal contribution
posted on 2023-05-17, 04:31 authored by Becker, S, Karri, V
Predictive models were built using neural networks for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used online for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematicalmodels are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of thesemodels did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications.

History

Publication title

Journal of Advanced Computational Intelligence

Volume

14

Issue

6

Pagination

735-740

ISSN

1343-0130

Department/School

School of Engineering

Publisher

Fuji Technology Press

Place of publication

Japan

Rights statement

Copyright 2010 Fuji Technology Press.

Repository Status

  • Restricted

Socio-economic Objectives

Network systems and services

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