Title: Using neural networks for fault detection in a distillation column

Authors: I. Manssouri, Y. Chetouani, B. El Kihel

Addresses: Departement Genie Chimique, Universite de Rouen France, Rue Lavoisier, 76130 Mont Saint Aignan Cedex, France. ' Departement Genie Chimique, Universite de Rouen France, Rue Lavoisier, 76130 Mont Saint Aignan Cedex, France. ' Laboratoire de Genie Industriel et Production Mecanique, ENSA, BP 669, 60000 Oujda, Maroc

Abstract: Several methods of fault detection have been put to testing with the purpose of securing the installations and reducing the risks of accidents. This paper presents a new approach of fault detection based on the realisation of a Bayesian neural separate at radial basis functions. In this paper, our contribution consists of demonstrating the way this kind of network can be used as faults separate, applied to a continuous distillation column containing a binary mixture of toluene/methylcyclohexane. The latter is carried out through the use of test base containing two operating modes: normal and abnormal.

Keywords: classification; distillation column; fault detection; process safety; radial basis function; RBF; reliability; neural networks.

DOI: 10.1504/IJCAT.2008.020953

International Journal of Computer Applications in Technology, 2008 Vol.32 No.3, pp.181 - 186

Published online: 26 Oct 2008 *

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