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Modeling and performance monitoring of multivariate multimodal processes
Author(s) -
Feital Thiago,
Kruger Uwe,
Dutra Julio,
Pinto José Carlos,
Lima Enrique Luis
Publication year - 2013
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13953
Subject(s) - principal component analysis , continuous stirred tank reactor , mixture model , multivariate statistics , component (thermodynamics) , process (computing) , identification (biology) , maximization , computer science , set (abstract data type) , statistical process control , data mining , engineering , artificial intelligence , mathematics , machine learning , mathematical optimization , physics , botany , chemical engineering , biology , thermodynamics , programming language , operating system
A multimodal modeling and monitoring approach based on maximum likelihood principal component analysis and a component‐wise identification of operating modes are presented. Analyzing each principal component individually allows separating components describing the variation within the individual modes from those capturing variation which the modes commonly share. On the basis of the former set, a Gaussian mixture model produces a statistical fingerprint that describes the production modes. The advantage of the component‐wise analysis is a simple identification of the mixture model parameters, which does not rely on the computationally cumbersome expectation maximization. The proposed method diagnoses abnormal process conditions by defining statistics relating to the components describing (1) between‐cluster variation, (2) within cluster variation, and (3) model residuals. The article demonstrates the benefits of this approach over existing work by an application to a continuous stirred tank reactor (CSTR) simulator and the analysis of recorded data from a furnace and a chemical reaction process. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1557–1569, 2013