The objective of the paper is to assess the feasibility of the neural network (NN) approach in power plant process evaluations. A “feed-forward” technique with a back propagation algorithm was applied to a gas turbine equipped with waste heat boiler and water heater. Data from physical or empirical simulators of plant components were used to train such a NN model. Results obtained using a conventional computing technique are compared with those of the direct method based on a NN approach. The NN simulator was able to perform calculations in a really short computing time with a high degree of accuracy, predicting various steady-state operating conditions on the basis of inputs that can be easily obtained with existing plant instrumentation. The optimization of NN parameters like number of hidden neurons, training sample size, and learning rate is discussed in the paper.
A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section
Contributed by the International Gas Turbine Institute (IGTI) of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Paper presented at the International Gas Turbine and Aeroengine Congress and Exhibition, Munich, Germany, May 8–11, 2000; Paper 00-GT-185. Manuscript received by IGTI February 2000; final revision received by ASME Headquarters January 2001. Associate Editor: M. Magnolet.
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Boccaletti , C., Cerri , G., and Seyedan, B. (January 1, 2001). "A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section ." ASME. J. Eng. Gas Turbines Power. April 2001; 123(2): 371–376. https://doi.org/10.1115/1.1361062
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