DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, DS | - |
dc.contributor.author | Park, JM | - |
dc.date.accessioned | 2016-03-31T13:36:35Z | - |
dc.date.available | 2016-03-31T13:36:35Z | - |
dc.date.created | 2009-08-25 | - |
dc.date.issued | 1999-10-08 | - |
dc.identifier.issn | 0168-1656 | - |
dc.identifier.other | 1999-OAK-0000001027 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20209 | - |
dc.description.abstract | In monitoring and controlling wastewater treatment processes, on-line information of nutrient dynamics is very important. However, these variables are determined with a significant time delay. Although the final effluent quality can be analyzed after this delay, it is often too late to make proper adjustments. In this paper, a neural network approach, a software sensor, was proposed to overcome this problem. Software sensor refers to a modeling approach inferring hard-to-measure process variables from other on-line measurable process variables. A bench-scale sequentially-operated batch reactor (SBR) used for advanced wastewater treatment (BOD plus nutrient removal) was employed to develop the neural network model. In order to improve the network performance, the structure of neural network was arranged in such a way of reflecting the change of operational conditions within a cycle. Real-time estimation of PO43-, NO3-, and NH4+ concentrations was successfully carried out with the on-line information of the SBR system only. (C) 1999 Elsevier Science B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | JOURNAL OF BIOTECHNOLOGY | - |
dc.subject | neural network | - |
dc.subject | software sensor | - |
dc.subject | sequentially operated batch reactor | - |
dc.subject | wastewater | - |
dc.subject | ACTIVATED-SLUDGE PROCESS | - |
dc.subject | SOFTWARE SENSORS | - |
dc.subject | SYSTEM | - |
dc.subject | IDENTIFICATION | - |
dc.subject | PREDICTION | - |
dc.subject | ORP | - |
dc.title | Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor | - |
dc.type | Article | - |
dc.contributor.college | 화학공학과 | - |
dc.identifier.doi | 10.1016/S0168-1656(99)00171-6 | - |
dc.author.google | Lee, DS | - |
dc.author.google | Park, JM | - |
dc.relation.volume | 75 | - |
dc.relation.issue | 2-3 | - |
dc.relation.startpage | 229 | - |
dc.relation.lastpage | 239 | - |
dc.contributor.id | 10054404 | - |
dc.relation.journal | JOURNAL OF BIOTECHNOLOGY | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF BIOTECHNOLOGY, v.75, no.2-3, pp.229 - 239 | - |
dc.identifier.wosid | 000083683900014 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 239 | - |
dc.citation.number | 2-3 | - |
dc.citation.startPage | 229 | - |
dc.citation.title | JOURNAL OF BIOTECHNOLOGY | - |
dc.citation.volume | 75 | - |
dc.contributor.affiliatedAuthor | Park, JM | - |
dc.identifier.scopusid | 2-s2.0-0037509217 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 64 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | ACTIVATED-SLUDGE PROCESS | - |
dc.subject.keywordPlus | SOFTWARE SENSORS | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | ORP | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | software sensor | - |
dc.subject.keywordAuthor | sequentially operated batch reactor | - |
dc.subject.keywordAuthor | wastewater | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
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