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

Henderson, Douglas; Armitage, Alistair; Pearson, W N


Douglas Henderson

Alistair Armitage

W N Pearson


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].

Conference Name Proceedings of ASME Turbo Expo 2002, Amsterdam, The Netherlands
Start Date Jun 3, 2002
End Date Jun 6, 2002
Publication Date Jun 3, 2002
Deposit Date Jun 6, 2008
Peer Reviewed Peer Reviewed
Keywords neural networks; gas transmission compressor; compressor speed; expert system; fuel gas;
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