DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, CK | - |
dc.contributor.author | Villez, K | - |
dc.contributor.author | Lee, IB | - |
dc.contributor.author | Rosen, C | - |
dc.contributor.author | Vanrolleghem, PA | - |
dc.date.accessioned | 2016-04-01T01:43:18Z | - |
dc.date.available | 2016-04-01T01:43:18Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2007-03-01 | - |
dc.identifier.issn | 0006-3592 | - |
dc.identifier.other | 2007-OAK-0000006597 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23557 | - |
dc.description.abstract | Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and soon. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearty; (2) multiple models with a;posterior probability for modeling different operating regions; (3) local batch monitoring by the T-2- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then; these local regions can be supervised separately; leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model. (c) 2006 Wiley Periodicals, Inc. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | JOHN WILEY & SONS INC | - |
dc.relation.isPartOf | BIOTECHNOLOGY AND BIOENGINEERING | - |
dc.subject | batch monitoring and supervision | - |
dc.subject | biological system | - |
dc.subject | multiple operational modes | - |
dc.subject | probabilistic modeling | - |
dc.subject | sequencing batch reactor (SBR) | - |
dc.subject | wastewater treatment | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | MULTIVARIATE | - |
dc.subject | REMOVAL | - |
dc.title | Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1002/BIT.21220 | - |
dc.author.google | Yoo, CK | - |
dc.author.google | Villez, K | - |
dc.author.google | Lee, IB | - |
dc.author.google | Rosen, C | - |
dc.author.google | Vanrolleghem, PA | - |
dc.relation.volume | 96 | - |
dc.relation.issue | 4 | - |
dc.relation.startpage | 687 | - |
dc.relation.lastpage | 701 | - |
dc.contributor.id | 10104673 | - |
dc.relation.journal | BIOTECHNOLOGY AND BIOENGINEERING | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | BIOTECHNOLOGY AND BIOENGINEERING, v.96, no.4, pp.687 - 701 | - |
dc.identifier.wosid | 000244287200007 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 701 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 687 | - |
dc.citation.title | BIOTECHNOLOGY AND BIOENGINEERING | - |
dc.citation.volume | 96 | - |
dc.contributor.affiliatedAuthor | Lee, IB | - |
dc.identifier.scopusid | 2-s2.0-33947154689 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 55 | - |
dc.description.scptc | 57 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | batch monitoring and supervision | - |
dc.subject.keywordAuthor | biological system | - |
dc.subject.keywordAuthor | multiple operational modes | - |
dc.subject.keywordAuthor | probabilistic modeling | - |
dc.subject.keywordAuthor | sequencing batch reactor (SBR) | - |
dc.subject.keywordAuthor | wastewater treatment | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
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