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A study of neural network applications to aluminium manufacturing

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Frost, FF (2000) A study of neural network applications to aluminium manufacturing. PhD thesis, University of Tasmania.

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Abstract

Process behaviour in the aluminium smelting industry is typically highly dynamic
and unstable and involves non-linear, highly dimensional relationships among
process parameters. Further, with the presence of noise associated with most of the
measured parameters of the aluminium production technique, process modelling in
the aluminium industry is often a complex task. However, the advancement of both
knowledge and technique has resulted in significant changes to industrial processing
techniques and process control methodologies. One such advancement is the
development of artificial neural networks, which are a well-suited computational
paradigm for use in monitoring and controlling complex dynamic processes. Neural
networks offer a powerful mathematical technique for modelling, control and
optimisation of dynamic processes that are developed using process data, without the
need for a priori knowledge or understanding the associated scientific principles and
underlying relationships among process parameters. Generally, when a neural
network is initially trained for a particular task, some of the features of the training
data will have no significant effect on the networks decision, while other features
will be critical. In addition, there exist many networks for a particular task that may
perform similarly, however, they may use different features of the training data to
make their decision. This work presents an evaluation and empirical performance
comparison of various neural networks in an important and actual application
domain. Such studies are valuable to understanding the strengths and weaknesses of
various problem solving models as well as the characteristics of various application
domains. As neural networks are an advanced control technique that are often used as
an opportunity to maximise corporate revenue, it becomes necessary to develop a set
of selection criteria for selecting a particular neural network that produces optimum
performance when applied to a specific application. Neural network selection can be
completed based on economic considerations, such as cost associated with neural
network accuracy, cost associated with measuring process parameters used as input
variables in the model and cost associated with neural network computation time. In
this work, evaluation of neural networks for three industrial applications, involving
process modelling of reduction cells for aluminium production at Comalco
Aluminium (Bell Bay) Limited, or CABBL, is completed. The performance of six
distinct models of the neural network paradigm is assessed using specific assessment
criteria. The decision of which neural network model is most suitable for a specific
application is complex, requiring quantitative decision logic, particularly as the
assessment criteria are not fundamentally of equal significance. e. It is shown that
optimisation techniques are necessary to select an optimum neural network model for
a specific application. While it is noted that available operations research techniques
are capable of neural network optimisation and selection, such optimisation
techniques are inappropriate for application in this instance. This work reports a
systematic technique that optimises a neural network efficiently on command using
precise mathematical models. It is shown in this work that the influence of each input
parameter on prediction error is analysed to determine an optimum neural network
model for each studied application. Moreover, while a feasible solution for the neural
network model is identified in the first instance, an optimal solution is subsequently
obtained and implemented to achieve maximum economic benefit. It is noted that the
developed optimisation strategy is a unique and novel methodology for neural
network optimisation and selection and has been carefully developed to facilitate its
ease of application in industry.

Item Type: Thesis (PhD)
Keywords: Aluminum industry and trade, Neural networks (Computer science)
Copyright Holders: The Author
Copyright Information:

Copyright 2000 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
would be pleased to hear from the copyright owner(s)

Additional Information:

This thesis has been made openly accessible except for the for zipped folder of publications which cannot be made available for copyright and proprietary reasons.

Date Deposited: 09 Dec 2014 00:06
Last Modified: 11 Mar 2016 05:54
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