@inproceedings { , title = {A novel method for the performance control of a gas transmission compressor.}, abstract = {This paper presents the application of feed forward neural networks to the performance control of a gas transmission compressor. It is estimated that a global saving in compressor fuel gas of 1\% could prevent the production of 6 million tonnes of CO2, per year, [1]. 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.}, conference = {ASME TURBO EXPO 2002: Controls, Diagnostics and Instrumentation, Cycle Innovations, Marine, Oil and Gas Applications}, isbn = {0791836010 CD of Proceedings}, note = {Note: In: ASME TURBO EXPO 2002: Controls, Diagnostics and Instrumentation, Cycle Innovations, Marine, Oil and Gas Applications. American Society of Mechanical Engineers, International Gas Turbine Institute, Turbo Expo (Publication) IGTI, v 2 B, 2002, p 1173-1183 Conference dates: 3rd-6th June 2002 School: sch\_comp}, organization = {Amsterdam, Netherlands}, pages = {1173-1183}, publicationstatus = {Published}, url = {http://researchrepository.napier.ac.uk/id/eprint/1808}, keyword = {006 Special Computer Methods, 621 Electronic & mechanical engineering, QA76 Computer software, TJ Mechanical engineering and machinery, Compressors, Control systems, Carbon dioxide, Emission control, Approximation theory, Neural networks, Computer architecture;Uncontrolled terms: Performance control - Gas transmission compressor}, year = {2002}, author = {Pearson, W N and Armitage, Alistair and Henderson, Douglas} }