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Cited 21 time in webofscience Cited 26 time in scopus
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An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations

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dc.contributor.authorHwangbo, Soonho-
dc.contributor.authorAl, Resul-
dc.contributor.authorSin, Gurkan-
dc.date.accessioned2022-12-26T12:04:29Z-
dc.date.available2022-12-26T12:04:29Z-
dc.date.issued2020-12-05-
dc.identifier.issn0098-1354-
dc.identifier.issn1873-4375-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/5797-
dc.description.abstractThis study aims to develop a deep-learning-based and plant data-driven framework for process modeling to help understanding plant-wide processes. The systematic framework consists of the following steps: data processing based on domain-knowledge, deep-learning model development, model selection using information criteria, and global sensitivity analysis with Monte-Carlo simulations. The assessment of the quality of the optimal deep-learning model to support plant-wide process understanding is the key emphasis of this framework. The proposed framework was applied for analyzing long-term data from wastewater treatment plants to predict nitrous oxide emission characteristics. The results showed a promising potential of the framework to systematically and efficiently develop fit-for-purpose deep learning models with highly favorable cross-validation statistics (R-2). The framework is expected to facilitate the development of versatile deep-learning models based on plant data encompassing nonlinear and complex process phenomena, where especially mechanistic models are not available. (C) 2020 Elsevier Ltd. All rights reserved.-
dc.language영어-
dc.language.isoENG-
dc.publisherPergamon Press Ltd.-
dc.titleAn integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.compchemeng.2020.107071-
dc.identifier.scopusid2-s2.0-85090049664-
dc.identifier.wosid000598171400005-
dc.identifier.bibliographicCitationComputers & Chemical Engineering, v.143-
dc.citation.titleComputers & Chemical Engineering-
dc.citation.volume143-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryEngineering, Chemical-
dc.subject.keywordPlusARTIFICIAL-INTELLIGENCE-
dc.subject.keywordPlusSENSITIVITY-ANALYSIS-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusOPTIMIZATION-
dc.subject.keywordPlusSELECTION-
dc.subject.keywordPlusNETWORKS-
dc.subject.keywordPlusREMOVAL-
dc.subject.keywordAuthorDeep-learning-
dc.subject.keywordAuthorFull-scale plant data-
dc.subject.keywordAuthorDomain-knowledge-
dc.subject.keywordAuthorProcess modeling-
dc.subject.keywordAuthorWastewater treatment plant-
dc.subject.keywordAuthorNitrous-oxide emissions-
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