Identification of a Liquid Saturated Steam Heat Exchanger using Focused Time Lagged Recurrent Neural Network Model

Abstract

In this paper, a focused time lagged recurrent neural network (FTLRNN) with gamma memory is developed to learn the dynamics of a typical liquid saturated steam heat exchanger process. This highly nonlinear process has been a significant benchmark for non-linear control design purposes, since it is characterized by non-minimum phase behaviour. It appears from the literature review that an optimal neural network (NN) model for the identification of such a highly nonlinear complex dynamical system is not currently available. This paper compares the performance of two NN configurations, namely a well-known Multi Layer Perceptron (MLP) NN model and the proposed FTLRNN model. A standard static backpropagation algorithm with momentum term has been used for both the models. It is shown that the estimated dynamic NN based model comprising of a gamma memory filter followed by a MLP based NN clearly outperforms the static MLP NN in various performance metrics such as mean square error (MSE), normalized MSE and correlation coefficients on the testing datasets. In addition, the output of the proposed NN model closely follows the desired output of the exchanger process for the testing instances. This also means that the most of the information about the rich nonlinear dynamics of the system has been extracted successfully from the training dataset and that the proposed model approximates the given system with reasonable accuracy. It is shown that the suggested dynamic NN model has a remarkable system identification capability for the problem considered in this paper. Dynamic NN model has clearly outperformed the static NN models in respect of the performance measures. The major contribution of this paper is that the FTLRNNs can elegantly be used to learn underlying highly nonlinear dynamics of the system.