Using Artificial Neural networks for the modelling of a distillation column

The main aim of this paper is to establish a reliable model both for the steady-state and unsteady-state regimes of a nonlinear process. The use of this model should reflect the true behavior of the process under its normal operating conditions and allow distinguishing a normal mode from an abnormal one. In order to obtain this reliable model for the process dynamics, the neural black-box identification by means of a NARMAX model has been chosen in this study. The modelling study shows the choice and the performance of the neural network in the training and test phases. Also an analysis of the inputs choice, time delay, hidden neurons and their influence on the behavior of the neural estimator is carried out. Three statistical criteria are used for the validation of the experimental data. The model is implemented by training a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with input-output experimental data. After describing the system architecture a realistic and complex application as a distillation column is presented in order to illustrate the proposed ideas concerning the dynamics modelling. Satisfactory agreement between identified and experimental data is found and results show that the neural model successfully predicts the evolution of the product composition.