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JACIII Vol.14 No.6 pp. 735-745
doi: 10.20965/jaciii.2010.p0735
(2010)

Paper:

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

Steffen Becker* and Vishy Karri**

*University of Tasmania, GPO Box 252-65, Hobart 7001, Tasmania, Australia

**Australian College of Kuwait, P.O.Box 1411, Safat-13015, Kuwait

Received:
February 10, 2010
Accepted:
April 10, 2010
Published:
September 20, 2010
Creative Commons License
Keywords:
neural networks, predictive modelling, optimisation layer-by-layer, hydrogen production, PEMelectrolyzer
Abstract
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 mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models 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.
Cite this article as:
S. Becker and V. Karri, “Implementation of Neural Network Models for Parameter Estimation of a PEM-Electrolyzer,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.6, pp. 735-745, 2010.
Data files:
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