DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, MW | - |
dc.contributor.author | Hong, SH | - |
dc.contributor.author | Choi, H | - |
dc.contributor.author | Kim, JH | - |
dc.contributor.author | Lee, DS | - |
dc.contributor.author | Park, JM | - |
dc.date.accessioned | 2016-04-01T01:09:03Z | - |
dc.date.available | 2016-04-01T01:09:03Z | - |
dc.date.created | 2009-08-25 | - |
dc.date.issued | 2008-10 | - |
dc.identifier.issn | 1359-5113 | - |
dc.identifier.other | 2008-OAK-0000008193 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/22475 | - |
dc.description.abstract | A real-time remote monitoring system for wastewater treatment plants (WWTPs) has been developed to give local operators a guideline that would allow them to arrive at the optimum operational strategy in the early stage of a process disturbance. Especially, small-scaled WWTPs in Korea's rural areas show a large fluctuation in their influent loading and, therefore, they require an efficient operation for treatment of organic matter, nitrogen and phosphorus. However, under requirements to lower running costs, most of the small-scaled WWTPs are being forced to operate with a minimum number of operators. It is too costly for them to employ a local expert to maintain plant systems properly. In recent years, recognition of these problems has raised great interests in real-time remote monitoring systems. They serve the key information needed for efficient operation, and help to transfer knowledge from the experts at a remote control center to local operators in real-time. In this study, both operation data and measurement values from a novel mobile multi-sensor system were transmitted on-line by a telecommunication system. Then multivariate statistical process controls and software sensor techniques were applied to supervise local WWTPs. The developed remote monitoring system makes it possible to monitor the current plants' statuses and to support the operation of local wastewater systems. (C) 2008 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.relation.isPartOf | PROCESS BIOCHEMISTRY | - |
dc.subject | remote monitoring | - |
dc.subject | multivariate statistical process control | - |
dc.subject | principal component analysis | - |
dc.subject | neural network | - |
dc.subject | software sensor | - |
dc.subject | wastewater treatment plant | - |
dc.subject | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject | SEQUENCING BATCH REACTOR | - |
dc.subject | PERFORMANCE | - |
dc.title | Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/j.procbio.2008.06.002 | - |
dc.author.google | Lee, MW | - |
dc.author.google | Hong, SH | - |
dc.author.google | Choi, H | - |
dc.author.google | Kim, JH | - |
dc.author.google | Lee, DS | - |
dc.author.google | Park, JM | - |
dc.relation.volume | 43 | - |
dc.relation.issue | 10 | - |
dc.relation.startpage | 1107 | - |
dc.relation.lastpage | 1113 | - |
dc.contributor.id | 10054404 | - |
dc.relation.journal | PROCESS BIOCHEMISTRY | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PROCESS BIOCHEMISTRY, v.43, no.10, pp.1107 - 1113 | - |
dc.identifier.wosid | 000259773100013 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 1113 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 1107 | - |
dc.citation.title | PROCESS BIOCHEMISTRY | - |
dc.citation.volume | 43 | - |
dc.contributor.affiliatedAuthor | Park, JM | - |
dc.identifier.scopusid | 2-s2.0-50449095583 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 32 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | PRINCIPAL COMPONENT ANALYSIS | - |
dc.subject.keywordPlus | SEQUENCING BATCH REACTOR | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordAuthor | remote monitoring | - |
dc.subject.keywordAuthor | multivariate statistical process control | - |
dc.subject.keywordAuthor | principal component analysis | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | software sensor | - |
dc.subject.keywordAuthor | wastewater treatment plant | - |
dc.relation.journalWebOfScienceCategory | Biochemistry & Molecular Biology | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Engineering | - |
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