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.

Citation

Henderson, D., Armitage, A., & Pearson, W. N. (2002, June). A novel method for the performance modelling of a gas transmission compressor. Presented at Proceedings of ASME Turbo Expo 2002, Amsterdam, The Netherlands

• Conference Name: Proceedings of ASME Turbo Expo 2002
• 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