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Monitoring batch processes using multiway principal component analysis
Author(s) -
Nomikos Paul,
MacGregor John F.
Publication year - 1994
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.690400809
Subject(s) - principal component analysis , statistical process control , computer science , multivariate statistics , process (computing) , exploit , component (thermodynamics) , batch processing , data mining , tracking (education) , trajectory , artificial intelligence , machine learning , psychology , pedagogy , physics , computer security , astronomy , thermodynamics , programming language , operating system
Multivariate statistical procedures for monitoring the progress of batch processes are developed. The only information needed to exploit the procedures is a historical database of past successful batches. Multiway principal component analysis is used to extract the information in the multivariate trajectory data by projecting them onto low‐dimensional spaces defined by the latent variables or principal components. This leads to simple monitoring charts, consistent with the philosophy of statistical process control, which are capable of tracking the progress of new batch runs and detecting the occurrence of observable upsets. The approach is contrasted with other approaches which use theoretical or knowledge‐based models, and its potential is illustrated using a detailed simulation study of a semibatch reactor for the production of styrene‐butadiene latex.