alexa Change Detection in a Distillation Column Based on the
ISSN: 2157-7048

Journal of Chemical Engineering & Process Technology
Open Access

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Research Article

Change Detection in a Distillation Column Based on the Generalized Likelihood Ratio Approach

Yahya Chetouani*
Département Génie Chimique, Rue Lavoisier, 76821, Mont Saint Aignan Cedex, France
Corresponding Author : Yahya Chetouani
Université de Rouen
Département Génie Chimique
Rue Lavoisier, 76821
Mont Saint Aignan Cedex, France
Fax: (0033)235146130
E-mail: [email protected]
Received March 31, 2011; Accepted November 14, 2011; Published November 20, 2011
Citation: Chetouani Y (2011) Change Detection in a Distillation Column Based on the Generalized Likelihood Ratio Approach. J Chem Eng Process Technol 2:115. doi:10.4172/2157-7048.1000115
Copyright: © 2011 Chetouani Y. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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With increasing demands for efficiency, product quality, reliability and process safety, the field of fault detection (FD) plays an important role in chemical industries. This paper deals with a FD method based on the combination of Generalized Likelihood Ration Test (GLRT) and Artificial Neural Networks (ANNs). A reliable neural model in normal conditions, under all regimes (i.e. steady-state and dynamic conditions), is found by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model and by an experimental design. The efficiency of the combination of these two approaches used for detecting faults has been tested under real anomalous conditions on a real plant as a distillation column. From the experimental results, it is observed that the proposed FD is able to detect the process status effectively.


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