Modeling and Simulation of a Reactive Packed Distillation Column Using Delayed Neural Networks

Abstract

The complex nature of a reactive packed distillation system due to the occurrence of both reactions and separations in a single unit demanded the need for a very robust tool of representing the process. In view of this, delayed neural networks are considered as one that can handle this problem effectively. As such, in this work, delayed neural networks Nonlinear AutoRegressive, Nonlinear AutoRegressive with eXogenous inputs and Nonlinear Input-Output models are developed and simulated with the aid of MATLAB R2010b to predict the top and bottom sections temperatures of the column. The predicted temperatures of the Input-Output models were found not to be satisfactory. However, the good agreements observed from the plots and the good values of the correlation coefficients and the mean squared errors between the predicted temperatures of NAR and NARX models and the experimental ones showed that these two models can be used to represent the process.