AN APPROACH TO FAULT-DIAGNOSIS OF CHEMICAL PROCESSES VIA NEURAL NETWORKS

This article presents an approach to fault diagnosis of chemical processes at steady-state operation by using artificial neural networks. The conventional back-propagation network is enhanced by adding a number of functional units to the input layer. This technique considerably extends a network's capability for representing complex nonlinear relations and makes it possible to simultaneously diagnose multiple faults and their corresponding levels in a chemical process. A simulation study of a heptane-to-toluene process at steady-state operation shows successful results for the proposed approach.