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Multivariate Modeling of a Chemical Toner Manufacturing Process
Author(s) -
Khorami Hassan,
Fgaier Hedia,
Elkamel Ali,
Biglari Mazda,
Chen Baoling
Publication year - 2017
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/ceat.201400433
Subject(s) - principal component analysis , process (computing) , multivariate statistics , latent variable , process engineering , process control , computer science , process modeling , partial least squares regression , identification (biology) , product (mathematics) , matrix (chemical analysis) , batch processing , process analytical technology , unit operation , process optimization , work in process , engineering , artificial intelligence , machine learning , mathematics , chemistry , chromatography , chemical engineering , operating system , botany , geometry , operations management , environmental engineering , biology , programming language
Modeling, optimization, process monitoring, and product development in a toner process using multiway principal component analysis and multiway partial least square method is described. Process measurements and product quality values of past successful batches were collected in a data matrix and preprocessed through time alignment, centering, and scaling. Following the identification of latent variables, an empirical model was built through a fourfold cross validation that can represent the operation of a successful batch. The prepared model provided a realistic prediction of process behavior, realistically represented the operation of the industrial unit, and is mathematically simple enough to be used in online optimization and for automatic control strategies of selected abnormal batches.