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

Journal of Chemical Engineering & Process Technology
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

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.
Related article at
DownloadPubmed DownloadScholar Google
 

Abstract

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.

Keywords

Share This Page

Additional Info

Loading
Loading Please wait..
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords