Experimental study and nonlinear modelling by artificial neural networks of a distillation column

Abstract

Chemical industries are characterised by complex nonlinear processes. A suitable class of Non-linear Auto-Regressive Moving Average with eXogenous (NARMAX) models is considered which captures most of the system dynamics. The use of this model should reflect the normal behaviour of the process and be used for developing a cost-effective Fault Detection and Diagnosis (FDD) method. An Artificial Neural Network (ANN) is used to model plant input-output data by means of a NARMAX model. Three statistical criteria are used for the validation of the experimental data. A realistic and complex application as a distillation column is presented in order to illustrate the proposed ideas concerning the dynamics modelling and model reduction. Satisfactory agreement between identified and experimental data is found and results show that the reduced neural model successfully predicts the evolution of the product composition.