Soft-sensor development using correlation-based just-in-time modeling

Soft-sensors have been widely used for estimating product quality or other key variables, but their estimation performance deteriorate when the process characteristics change. To cope with such changes, recursive PLS and Just-In-Time (JIT) modeling have been developed. However, recursive PLS does no...

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Bibliographic Details
Published in:AIChE journal 2009-07, Vol.55 (7), p.1754-1765
Main Authors: Fujiwara, Koichi, Kano, Manabu, Hasebe, Shinji, Takinami, Akitoshi
Format: Article
Language:English
Subjects:
Applied sciences
Catalysis
Catalysts
Catalytic reactions
Chemical engineering
Chemistry
Estimating techniques
estimation
Exact sciences and technology
General and physical chemistry
just-in-time modeling
principal component analysis
Product quality
Reactors
recursive partial least squares
Sensors
soft-sensor
Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
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Summary:Soft-sensors have been widely used for estimating product quality or other key variables, but their estimation performance deteriorate when the process characteristics change. To cope with such changes, recursive PLS and Just-In-Time (JIT) modeling have been developed. However, recursive PLS does not always function well when process characteristics change abruptly and JIT modeling does not always achieve the high-estimation performance. In the present work, a new method for constructing soft-sensors based on a JIT modeling technique is proposed. In the proposed method, referred to as correlation-based JIT modeling (CoJIT), the samples used for local modeling are selected on the basis of the correlation among measured variables and the model can adapt to changes in process characteristics. The usefulness of the proposed method is demonstrated through a case study of a CSTR process, in which catalyst deactivation and recovery are taken into account. In addition, its industrial application to a cracked gasoline fractionator is reported. © 2009 American Institute of Chemical Engineers AIChE J, 2009
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.11791