Recursive data-based prediction and control of batch product quality

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In typical batch and semibatch processes, process/feedstock disturbances occur frequently and on-line measurements of product quality variables are not available. As a result, most batch processes have not been able to achieve tight quality control. Empirical, data-driven approaches are very attractive for dealing with this problem because of the difficulties associated with developing accurate process models from first principles. An approach for recursive on-line quality prediction was developed around data-based model structures. Techniques designed to incorporate the predictive models into on-line monitoring and control of batch product quality were also examined. The proposed control approach can be viewed as shrinking-horizon model-predictive control based on empirical models. The effectiveness of the proposed prediction and control methods are illustrated by using an industrially relevant simulated polymerization example.
Publisher
AMER INST CHEMICAL ENGINEERS
Issue Date
1998-11
Language
English
Article Type
Article
Keywords

PARTIAL LEAST-SQUARES; PRINCIPAL COMPONENT ANALYSIS; NEURAL-NETWORK MODELS; INFERENTIAL CONTROL; PLS APPROACH; PERFORMANCE; REGRESSION; DIAGNOSIS; REACTOR; DESIGN

Citation

AICHE JOURNAL, v.44, no.11, pp.2442 - 2458

ISSN
0001-1541
DOI
10.1002/aic.690441112
URI
http://hdl.handle.net/10203/73329
Appears in Collection
CBE-Journal Papers(저널논문)
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