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Hydrogen production performance modelling using intelligent techniques

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Becker, Steffen (2010) Hydrogen production performance modelling using intelligent techniques. PhD thesis, University of Tasmania.

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Abstract

Electrolytic hydrogen production can be seen as the binding element in utilising and
storing renewable energies towards a sustainable and environmentally compatible
energy supply.
In this thesis comprehensive literature review on hydrogen production with
emphasises on electrolysis of water and various conventional models has been
conducted. Furthermore, literature survey on applied Artificial Neural Networks
(ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) elucidates
architecture, functionality and application in detail.
This study provided predictive hydrogen production performance models for a
commercial PEM-electrolyzer. Two different approaches using intelligent techniques
have been conducted. The first employs ANN and the second uses a hybrid model
ANFIS as time series prediction combining fuzzy logic and Neural Networks.
An experimental apparatus has been developed to measure and model specific
performance parameters such as hydrogen flow rate, system-efficiency and stack-efficiency.
A comprehensive range of experimental conditions were tested as part of
the investigation that covers a wide range of input variables and their influence on
the output performance. The various parameters have been obtained using the
electrolyzers' internal software (windows diagnostic) and additional sensors
measuring power and feed water parameters, such as water quality, water pressure,
system temperature, stack current, stack voltage, system power consumption, system
pressure, product pressure and lower explosive limit. Synchronous data-acquisition
of all parameters was carried out with National Instruments LabVIEW software to
build a database. The database formed the foundation for the predictive models,
where experimental data were used to train and test the developed hydrogen
production performance models. Verification of those models was carried out by
comparison of predicted and measured data. 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.

Item Type: Thesis (PhD)
Copyright Holders: The Author
Copyright Information:

Copyright 2010 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
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Additional Information:

This thesis contains confidential information and is not to be disclosed or made available for loan or copy without the express permission of the University of Tasmania. Once released the Thesis may be available for loan and limited copying in accordance with the Copyright Act 1968. Authority from School of Engineering on 10th February 2012.;Thesis (PhD)--University of Tasmania

Date Deposited: 25 Nov 2014 00:59
Last Modified: 11 Mar 2016 05:53
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