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

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

Abstract

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].

Citation

Henderson, D., Armitage, A., & Pearson, W. N. (2002). A novel method for the performance modelling of a gas transmission compressor.

• 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
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/1997