Reference : Plant monitoring and fault detection - Synergy between data reconciliation and princi...
Scientific journals : Article
Engineering, computing & technology : Chemical engineering
Engineering, computing & technology : Computer science
Plant monitoring and fault detection - Synergy between data reconciliation and principal component analysis
Amand, Thierry [Université de Liège - ULiège > Chimie appliquée > LASSC > >]
Heyen, Georges [Université de Liège - ULiège > Département de chimie appliquée > LASSC (Labo d'analyse et synthèse des systèmes chimiques) >]
Kalitventzeff, Boris [Université de Liège - ULiège > Services généraux (Faculté des sciences appliquées) > Relations académiques et scientifiques (Sciences appliquées) >]
Computers & Chemical Engineering
Pergamon Press - An Imprint of Elsevier Science
Yes (verified by ORBi)
United Kingdom
[en] Data reconciliation and principal component analysis are tno recognised statistical methods used for plant monitoring and fault detection. We propose to combine them for increased efficiency. Data reconciliation is used in the first step of the determination of the projection matrix for principal component analysis (eigenvectors). principal component analysis can then be applied to raw process data for monitoring purpose. The combined use of these techniques aims at a better efficiency in fault detection. It relies mainly in a lower number of components to monitor. The method is applied to a modelled ammonia synthesis loop. (C) 2001 Elsevier Science Ltd. All rights reserved.

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