DC Field | Value | Language |
---|---|---|
dc.contributor.author | Russell, SA | ko |
dc.contributor.author | Kesavan, P | ko |
dc.contributor.author | Lee, JayHyung | ko |
dc.contributor.author | Ogunnaike, BA | ko |
dc.date.accessioned | 2013-02-28T06:48:02Z | - |
dc.date.available | 2013-02-28T06:48:02Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1998-11 | - |
dc.identifier.citation | AICHE JOURNAL, v.44, no.11, pp.2442 - 2458 | - |
dc.identifier.issn | 0001-1541 | - |
dc.identifier.uri | http://hdl.handle.net/10203/73329 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | AMER INST CHEMICAL ENGINEERS | - |
dc.title | Recursive data-based prediction and control of batch product quality | - |
dc.type | Article | - |
dc.identifier.wosid | 000076927200011 | - |
dc.identifier.scopusid | 2-s2.0-0032451290 | - |
dc.type.rims | ART | - |
dc.citation.volume | 44 | - |
dc.citation.issue | 11 | - |
dc.citation.beginningpage | 2442 | - |
dc.citation.endingpage | 2458 | - |
dc.citation.publicationname | AICHE JOURNAL | - |
dc.identifier.doi | 10.1002/aic.690441112 | - |
dc.contributor.localauthor | Lee, JayHyung | - |
dc.contributor.nonIdAuthor | Russell, SA | - |
dc.contributor.nonIdAuthor | Kesavan, P | - |
dc.contributor.nonIdAuthor | Ogunnaike, BA | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | PARTIAL LEAST-SQUARES | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | NEURAL-NETWORK MODELS | - |
dc.subject.keywordPlus | INFERENTIAL CONTROL | - |
dc.subject.keywordPlus | PLS APPROACH | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | REACTOR | - |
dc.subject.keywordPlus | DESIGN | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.