Batch process monitoring based on just-in-time learning and multiple-subspace principal component analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 137, Issue , 2014, Pages 128-139

  Lv, Zhaomin ^a   Yan, Xuefeng ^a   Jiang, Qingchao ^a  

^a China   (China)

Author keywords

Batch process monitoring; Just in time learning; Multiple subspace; Principal component analysis

Indexed keywords

PENICILLIN DERIVATIVE;

ALGORITHM; ARTICLE; BAYES THEOREM; CONTROLLED STUDY; FEASIBILITY STUDY; FED BATCH FERMENTATION; INTERMETHOD COMPARISON; JUST IN TIME LEARNING; MACHINE LEARNING; MULTIPLE SUBSPAPCE PRINCIPAL COMPONENT ANALYSIS; ONLINE SYSTEM; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MONITORING; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 84904335114     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2014.06.010     Document Type: Article

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