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dc.contributor.authorYoo, CK-
dc.contributor.authorLee, IB-
dc.contributor.authorVanrolleghem, PA-
dc.date.accessioned2016-04-01T01:49:25Z-
dc.date.available2016-04-01T01:49:25Z-
dc.date.created2009-02-28-
dc.date.issued2006-08-
dc.identifier.issn0167-6369-
dc.identifier.other2006-OAK-0000006285-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/23782-
dc.description.abstractThis article describes the application of on-line nonlinear monitoring of a sequencing batch reactor (SBR). Three-way batch data of SBR are unfolded batch-wisely, and then a adaptive and nonlinear multivariate monitoring method is used to capture the nonlinear characteristics of normal batches. The approach is successfully applied to an 80 L SBR for biological wastewater treatment, where the SBR poses an interesting challenge in view of process monitoring since it is characterized by nonstationary, batchwise, multistage, and nonlinear dynamics. In on-line batch monitoring, the developed adaptive and nonlinear process monitoring method can effectively capture the nonlinear relationship among process variables of a biological process in a SBR. The results of this pilot-scale SBR monitoring system using simple on-line measurements clearly demonstrated that the adaptive and nonlinear monitoring technique showed lower false alarm rate and physically meaningful, that is, robust monitoring results.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherSPRINGER-
dc.relation.isPartOfENVIRONMENTAL MONITORING AND ASSESSMENT-
dc.subjectadaptive batch monitoring-
dc.subjectmultiway kernel principal component analysis (MKPCA)-
dc.subjectnonlinear biological process-
dc.subjectsequencing batch reactor (SBR)-
dc.subjectPRINCIPAL COMPONENT ANALYSIS-
dc.subjectMULTIVARIATE-
dc.titleOn-line adaptive and nonlinear process monitoring of a pilot scale sequencing batch reactor-
dc.typeArticle-
dc.contributor.college화학공학과-
dc.identifier.doi10.1007/S10661-005-9-
dc.author.googleYoo, CK-
dc.author.googleLee, IB-
dc.author.googleVanrolleghem, PA-
dc.relation.volume119-
dc.relation.issue1-3-
dc.relation.startpage349-
dc.relation.lastpage366-
dc.contributor.id10104673-
dc.relation.journalENVIRONMENTAL MONITORING AND ASSESSMENT-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCIE-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationENVIRONMENTAL MONITORING AND ASSESSMENT, v.119, no.1-3, pp.349 - 366-
dc.identifier.wosid000241170900020-
dc.date.tcdate2019-01-01-
dc.citation.endPage366-
dc.citation.number1-3-
dc.citation.startPage349-
dc.citation.titleENVIRONMENTAL MONITORING AND ASSESSMENT-
dc.citation.volume119-
dc.contributor.affiliatedAuthorLee, IB-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc9-
dc.type.docTypeArticle-
dc.subject.keywordAuthoradaptive batch monitoring-
dc.subject.keywordAuthormultiway kernel principal component analysis (MKPCA)-
dc.subject.keywordAuthornonlinear biological process-
dc.subject.keywordAuthorsequencing batch reactor (SBR)-
dc.relation.journalWebOfScienceCategoryEnvironmental Sciences-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEnvironmental Sciences & Ecology-

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이인범LEE, IN BEUM
Dept. of Chemical Enginrg
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