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Cited 29 time in webofscience Cited 41 time in scopus
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dc.contributor.authorCho, HW-
dc.contributor.authorKim, KJ-
dc.date.accessioned2016-03-31T12:56:28Z-
dc.date.available2016-03-31T12:56:28Z-
dc.date.created2009-02-28-
dc.date.issued2003-01-
dc.identifier.issn0022-4065-
dc.identifier.other2003-OAK-0000003116-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/18747-
dc.description.abstractBatch processes play an important role in the production of low-volume, high-value products such as polymers, pharmaceuticals, and biochemicals. Multiway Principal Components Analysis (MPCA), one of the multivariate projection methods, has been widely used for monitoring batch processes. One major problem in the on-line application of MPCA is that the input data matrix for MPCA is not complete until the end of the batch operation, and thus the unmeasured portion of the matrix (called the "future observations") has to be predicted. In this paper we propose a new method for predicting the future observations of the batch that is currently being operated (called the "new batch"). The proposed method, unlike the existing prediction methods, makes extensive use of the past batch trajectories. The past batch trajectory which is deemed the most similar to the new batch is selected from the batch library and used as the basis for predicting the unknown part of the new batch. A case study on an industrial PVC batch process has been conducted. The results show that the proposed method results in more accurate prediction and has the capability of detecting process abnormalities earlier than the existing methods.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherAMER SOC QUALITY CONTROL-ASQC-
dc.relation.isPartOfJOURNAL OF QUALITY TECHNOLOGY-
dc.subjectbatch process-
dc.subjectmonitoring-
dc.subjectprincipal component analysis-
dc.subjectPRINCIPAL COMPONENT ANALYSIS-
dc.titleA method for predicting future observations in the monitoring of a batch process-
dc.typeArticle-
dc.contributor.college산업경영공학과-
dc.identifier.doi10.1080/00224065.2003.11980191-
dc.author.googleCho, HW-
dc.author.googleKim, KJ-
dc.relation.volume35-
dc.relation.issue1-
dc.relation.startpage59-
dc.relation.lastpage69-
dc.contributor.id10084322-
dc.relation.journalJOURNAL OF QUALITY TECHNOLOGY-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationJOURNAL OF QUALITY TECHNOLOGY, v.35, no.1, pp.59 - 69-
dc.identifier.wosid000180274600005-
dc.date.tcdate2019-01-01-
dc.citation.endPage69-
dc.citation.number1-
dc.citation.startPage59-
dc.citation.titleJOURNAL OF QUALITY TECHNOLOGY-
dc.citation.volume35-
dc.contributor.affiliatedAuthorKim, KJ-
dc.identifier.scopusid2-s2.0-0043256723-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc24-
dc.type.docTypeArticle-
dc.subject.keywordAuthorbatch process-
dc.subject.keywordAuthormonitoring-
dc.subject.keywordAuthorprincipal component analysis-
dc.relation.journalWebOfScienceCategoryEngineering, Industrial-
dc.relation.journalWebOfScienceCategoryOperations Research & Management Science-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaOperations Research & Management Science-
dc.relation.journalResearchAreaMathematics-

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김광재KIM, KWANG JAE
Dept. of Industrial & Management Eng.
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