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TECHNICAL PAPERS: Gas Turbines: Oil and Gas Applications

A Demonstration of Artificial Neural-Networks-Based Data Mining for Gas-Turbine-Driven Compressor Stations

[+] Author and Article Information
K. K. Botros

Nova Research and Technology Corporation, Calgary, Alberta, Canada

G. Kibrya, A. Glover

TransCanada Pipelines, Ltd., Calgary, Alberta, Canada

J. Eng. Gas Turbines Power 124(2), 284-297 (Mar 26, 2002) (14 pages) doi:10.1115/1.1414130 History: Received November 01, 1999; Revised February 01, 2000; Online March 26, 2002
Copyright © 2002 by ASME
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References

Figures

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Schematic of a three-spool gas turbine driving a gas compressor
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(a) MLP (7-14-8) architecture, (b) MLP (7-28-8) architecture, (c) MLP (7-14-14-8) architecture, (d) RBF (7-128-8) architecture, (e) GRNN (7-1844-9-8) architecture  
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Results of testing the RBF network architecture of Fig. 2(d) using 1996 data set for training and then testing
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Prediction of Qse for a period in 1997 versus measurements
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Detection of CDP sensor failure up to Case 720
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Estimate of the fuel gas flow for 1998 when the flow sensor failed in the entire year
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(a) Comparison between predicted versus measured power turbine inlet pressure for 1997 year based on 1996 trained RBF NN; (b) comparison between predicted versus measured GG exhaust temperature for 1997 year based on 1996 trained RBF NN; (c) comparison between predicted versus measured power turbine exhaust temperature for 1997 year based on 1996 trained RBF NN; (d) comparison between predicted versus measured shaft power for 1997 year based on 1996 trained RBF NN; (e) comparison between predicted versus measured HP spool speed for 1997 year based on 1996 trained RBF NN; (f) comparison between predicted versus measured HP fuel gas flow for 1997 year based on 1996 trained RBF NN
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Hybrid NN–FPM scheme for estimation of parameters that cannot be measured
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RBF architecture used in TIT training/prediction
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NN predicted versus estimated TIT from FPM for the same year
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NN predicted versus estimated TIT from FPM for a subsequent year
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Kohenen SOM of 15 input units and 3×15 output units
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Winning unit activation level for each case of 1996 data set
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Activation levels of the input units for cases 326 and 327 of 1996 data set
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Activation levels of the input units for cases 410 and 415 of 1996 data set
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Activation levels of the input units for cases 561 and 562 of 1996 data set
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Winning unit activation level for each case of 1997 data set
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Winning unit activation level for each case of 1998 data set

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