ISSN:
0001-1541
Keywords:
Chemistry
;
Chemical Engineering
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
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.
Additional Material:
8 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/aic.690390109
Permalink