DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, CK | - |
dc.contributor.author | Lee, JM | - |
dc.contributor.author | Vanrolleghem, PA | - |
dc.contributor.author | Lee, IB | - |
dc.date.accessioned | 2016-03-31T12:26:34Z | - |
dc.date.available | 2016-03-31T12:26:34Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2004-05-28 | - |
dc.identifier.issn | 0169-7439 | - |
dc.identifier.other | 2004-OAK-0000004280 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/17916 | - |
dc.description.abstract | Batch processes play an important role in the production of low-volume, high-value products such as polymers, pharmaceuticals, and biochemical products. Multiway principal component analysis (MPCA), a multivariate projection method, has been widely used to monitor batch processes. But in-control data of non-stationary processes in fact contain inherent non-Gaussian distributed data due to ramp changes, step changes. and even weak levels of autocorrelation. Monitoring charts obtained by applying MPCA to such non-Gaussian data may contain nonrandom patterns corresponding to the data characteristics. To obtain better monitoring performance in a batch process with non-Gaussian data, on-line batch monitoring method with multiway independent component analysis (MICA) is developed in this paper. MICA is based on a recently developed feature extraction method, called independent component analysis (ICA), whereas PCA looks for Gaussian components. whereas ICA searches for non-Gaussian components. MICA projects the multivariate data into a low-dimensional space defined by independent components (ICs). When the measured variables have non-Guassian distributions, MICA provides more meaningful statistical analysis and on-line monitoring compared to MPCA because MICA assumes that the latent variables are not Gaussian distributed. The proposed method was applied to the on-line monitoring of a fed-batch penicillin production. The simulation results demonstrate the power and advantages of MICA. (C) 2004 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.subject | fault detection and diagnosis | - |
dc.subject | multiway independent component analysis (MICA) | - |
dc.subject | multiway principal component analysis (MPCA) | - |
dc.subject | on-line batch process monitoring | - |
dc.subject | FERMENTATION | - |
dc.subject | SUPERVISION | - |
dc.subject | CHARTS | - |
dc.title | On-line monitoring of batch processes using multiway independent component analysis | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/j.chemolab.2004.02.002 | - |
dc.author.google | Yoo, CK | - |
dc.author.google | Lee, JM | - |
dc.author.google | Vanrolleghem, PA | - |
dc.author.google | Lee, IB | - |
dc.relation.volume | 71 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 151 | - |
dc.relation.lastpage | 163 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, v.71, no.2, pp.151 - 163 | - |
dc.identifier.wosid | 000221584400006 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 163 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 151 | - |
dc.citation.title | CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | - |
dc.citation.volume | 71 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-2342615505 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 141 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | fault detection and diagnosis | - |
dc.subject.keywordAuthor | multiway independent component analysis (MICA) | - |
dc.subject.keywordAuthor | multiway principal component analysis (MPCA) | - |
dc.subject.keywordAuthor | on-line batch process monitoring | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Mathematics | - |
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