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Process monitoring and diagnosis by multiblock PLS methods
Author(s) -
MacGregor John F.,
Jaeckle Christiane,
Kiparissides Costas,
Koutoudi M.
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.690400509
Subject(s) - process (computing) , projection (relational algebra) , principal component analysis , event (particle physics) , computer science , fault detection and isolation , multivariate statistics , data mining , artificial intelligence , pattern recognition (psychology) , process engineering , engineering , machine learning , algorithm , physics , quantum mechanics , actuator , operating system
Schemes for monitoring the operating performance of large continuous processes using multivariate statistical projection methods such as principal component analysis (PCA) and projection to latent structures (PLS) are extended to situations where the processes can be naturally blocked into subsections. The multiblock projection methods allow one to establish monitoring charts for the individual process subsections as well as for the entire process. When a special event or fault occurs in a subsection of the process, these multiblock methods can generally detect the event earlier and reveal the subsection within which the event has occurred. More detailed diagnostic methods based on interrogating the underlying PCA/PLS models are also developed. These methods show those process variables which are the main contributors to any deviations that have occurred, thereby allowing one to diagnose the cause of the event more easily. These ideas are demonstrated using detailed simulation studies on a multisection tubular reactor for the production of low‐density polyethylene.