An Approach to Fault Diagnosis of Chemical Processes via Neural Networks

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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.


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.

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