An Approach to Fault Diagnosis of Chemical Processes via Neural Networks
Document Type
Article
Abstract
This article presents an approach to fault diagnosis of chemical processes at steadystate 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.
Publication Info
AIChE Journal, Volume 39, Issue 1, 1993, pages 82-88.
Copyright 1993 John Wiley & Sons.
Fan, J. Y., Nikolaou, M., & White, R. E. (January 1992). An Approach to Fault Diagnosis of Chemical Processes Via Neural Networks. AIChE Journal, 39 (10, 82 – 88. http://dx.doi.org/10.1002/aic.690390109
Rights
Copyright 1993 John Wiley & Sons.
Fan, J. Y., Nikolaou, M., & White, R. E. (January 1992). An Approach to Fault Diagnosis of Chemical Processes Via Neural Networks. AIChE Journal, 39 (10, 82 – 88. http://dx.doi.org/10.1002/aic.690390109