Modeling and prediction of the dynamic behavior in a reactor-exchanger using NARMAX neural structure

The main aim of this article is to establish a reliable model of a process behavior under its normal operating conditions. The use of this model should reflect the true behavior of the process and allow distinguishing a normal mode from an abnormal one. In order to obtain a reliable model for the process dynamics, black-box identification by means of a NARMAX model has been chosen in this study. It is based on the neural network approach. This article shows the choice and the performance of the neural network in the training and test phases. An analysis of the inputs number and hidden neurons and their influence on the behavior of the neural predictor is carried out. Three statistical criterions, Aikeke's information criterion (AIC), Rissanen's minimum description length (MDL), and Bayesian information criteria (BIC), are used for the validation of the experimental data. A reactor-exchanger is used to illustrate the proposed ideas concerning the dynamics modeling. The outlet temperature is modeled according to the inlet temperature. The model is implemented by training a multilayer perceptron artificial neural network with input-output experimental data. Satisfactory agreement between identified and experimental data is found, and results show that the model successfully predicts the evolution of the outlet temperature of the process. A comparison with an ARMAX model based on the least-squares criterion is carried out.