Using neural networks for fault detection in a distillation column

Authors:
I. Manssouri;Y. Chetouani;B. El Kihel
Affiliations:
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
Venue:
International Journal of Computer Applications in Technology
Year:
2008

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Abstract

Several methods of fault detection have been put to testing withthe purpose of securing the installations and reducing the risks ofaccidents. This paper presents a new approach of fault detectionbased on the realisation of a Bayesian neural separate at radialbasis functions. In this paper, our contribution consists ofdemonstrating the way this kind of network can be used as faultsseparate, applied to a continuous distillation column containing abinary mixture of toluene/methylcyclohexane. The latter is carriedout through the use of test base containing two operating modes:normal and abnormal.