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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.

Item Type:	Refereed Article
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Computer vision
Objective Division:	Information and Communication Services
Objective Group:	Communication technologies, systems and services
Objective Field:	Network systems and services
UTAS Author:	Becker, S (Mr Steffen Becker)
ID Code:	66904
Year Published:	2010
Deposited By:	Engineering
Deposited On:	2011-02-17
Last Modified:	2012-07-03
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