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Batch process monitoring and its application to polymerization systems
Author(s) -
Ündey Cenk,
Ertunç Sinem,
Tatara Eric,
Teymour Fouad,
Çιnar Ali
Publication year - 2004
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.200450210
Subject(s) - process engineering , process (computing) , quality (philosophy) , polymerization , partial least squares regression , fault detection and isolation , reliability engineering , computer science , raw material , batch processing , fault (geology) , product (mathematics) , final product , materials science , chemistry , engineering , mathematics , artificial intelligence , machine learning , philosophy , actuator , composite material , operating system , programming language , organic chemistry , seismology , economics , geology , polymer , geometry , epistemology , market economy
Slight changes in raw material properties or operating conditions during critical periods of operation of batch and semi‐batch polymerization reactors may have a strong influence on reaction mechanism and impact final product quality. Online process monitoring, fault detection, fault diagnosis, and product quality prediction in real‐time ensure safe reactor operation and warn operators about excursions from normal operation that may lead to deterioration in product properties. Multivariate statistical process monitoring and quality prediction using multiway principal components analysis and multiway partial least squares have been successful in detecting abnormalities in process operation and product quality. When abnormal process operation is detected, fault diagnosis tools are used to determine the source cause of the deviation. Illustrative case studies are presented via simulated polyvinyl acetate polymerization.