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A novel method for the performance modelling of a gas transmission compressor.

Henderson, Douglas, Armitage, Alistair and Pearson, W N (2002) A novel method for the performance modelling of a gas transmission compressor. In: Proceedings of ASME Turbo Expo 2002, Amsterdam, The Netherlands, 3-6 june 2002, Amsterdam, The Netherlands.

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Abstract/Description

This paper presents the application of feed forward neural
networks to the performance modeling of a gas transmission
compressor. Results of compressor model testing suggest that
compressor speed can be estimated to within ± 2.5 %. The neural
network property of function approximation is used to predict
compressor speed for given process constraints and instrument
input sets. The effects of training set size, instrument noise,
reduced input sets and extrapolation from the training domain,
are quantified. Various neural network architectures and training
schema were examined. The embedding of a neural network into
an expert system is also discussed. A neural network can be retrained
to reflect changing compressor characteristics. A global
saving in compressor fuel gas of 1% could prevent the
production of 6 million tonnes of CO2 per year, [1].

Item Type: Conference or Workshop Item (Paper)
Print ISSN: 0791836010
Uncontrolled Keywords: neural networks; gas transmission compressor; compressor speed; expert system; fuel gas;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Engineering and the Built Environment
Dewey Decimal Subjects: 600 Technology > 620 Engineering > 621 Electronic & mechanical engineering
Library of Congress Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Item ID: 1997
Depositing User: RAE Import
Date Deposited: 06 Jun 2008 11:14
Last Modified: 03 Mar 2009 13:33
URI: http://researchrepository.napier.ac.uk/id/eprint/1997

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