Abstract
The objective of the paper is to assess the feasibility of the neural network (NN) approach in power plant process evaluations. A “feedforward” 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.
Published Version
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