IDENTIFICATION OF FLOW FAULTS IN CONTINUOUS REACTORS BY RELATING LINEAR-MODEL PARAMETERS AND PHYSICAL MAGNITUDES

A new procedure for predicting dead volume and bypassing in reactors w as explored. The method is specific for processes already implemented with a linear reference model. It is based on using a neural network ( NN) to obtain relationships between the parameters of the linear model and the dead volume and bypassing. Several experiments with bench sca le reactors were carried out and the dead volume and bypassing were fo und by using classical flow models. By computer simulation we studied the combination bf a NN and the linear model of a CSTR with dead volum e and bypassing. The NN is a three-layered perception, with sigmoid pr ocessing element and back-propagation learning. The input layer receiv es the parameters of the linear model and the output layer provides th e predicted dead volume and bypassing. The accuracy of the trained NN was verified by presenting unseen data to the NN. The prediction error s are less than 15%. (C) 1997 Elsevier Science Ltd.