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Batch tracking via nonlinear principal component analysis
Author(s) -
Dong Dong,
McAvoy Thomas J.
Publication year - 1996
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.690420810
Subject(s) - principal component analysis , nonlinear system , batch processing , computer science , process (computing) , tracking (education) , component (thermodynamics) , extension (predicate logic) , process engineering , data mining , artificial intelligence , engineering , psychology , pedagogy , physics , quantum mechanics , thermodynamics , programming language , operating system
Abstract Batch processes are very important to the chemical and manufacturing industries. Techniques for monitoring these batch processes to ensure their safe operation and to produce consistently high‐quality products are needed. Nomikos and MacGregor (1994) presented a multiway principal component analysis (MPCA) approach for monitoring batch processes, and test results show that the method is simple, powerful, and effective. MPCA, however, is a linear method, and most batch processes are nonlinear. Although data treatment techniques can remove some nonlinearity from the data, nonlinearity is still a problem when using MPCA for monitoring. In this article a nonlinear principal component analysis (NLPCA) method (Dong and McAvoy, 1993) is used for batch process monitoring. Results show that this method is excellent for this problem. Another interesting extension of this approach involves multistage batch process monitoring, which is illustrated through a detailed simulation study.