Hybrid Digital Twin for process industry using Apros simulation environment

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorAzangoo, Mohammaden_US
dc.contributor.authorSalmi, Joonasen_US
dc.contributor.authorYrjölä, Iivoen_US
dc.contributor.authorBensky, Jonathanen_US
dc.contributor.authorSantillan, Gerardoen_US
dc.contributor.authorPapakonstantinou, Nikolaosen_US
dc.contributor.authorSierla, Seppoen_US
dc.contributor.authorVyatkin, Valeriyen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen_US
dc.contributor.departmentAalto Universityen_US
dc.contributor.departmentSemantum Oyen_US
dc.contributor.departmentVTT Technical Research Centre of Finlanden_US
dc.date.accessioned2021-12-15T07:24:15Z
dc.date.available2021-12-15T07:24:15Z
dc.date.issued2021-11-30en_US
dc.description.abstractMaking an updated and as-built model plays an important role in the life-cycle of a process plant. In particular, Digital Twin models must be precise to guarantee the efficiency and reliability of the systems. Data-driven models can simulate the latest behavior of the sub-systems by considering uncertainties and life-cycle related changes. This paper presents a step-by-step concept for hybrid Digital Twin models of process plants using an early implemented prototype as an example. It will detail the steps for updating the first-principles model and Digital Twin of a brownfield process system using data-driven models of the process equipment. The challenges for generation of an as-built hybrid Digital Twin will also be discussed. With the help of process history data to teach Machine Learning models, the implemented Digital Twin can be continually improved over time and this work in progress can be further optimized.en
dc.description.versionPeer revieweden
dc.format.extent4
dc.format.extent1-4
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAzangoo , M , Salmi , J , Yrjölä , I , Bensky , J , Santillan , G , Papakonstantinou , N , Sierla , S & Vyatkin , V 2021 , Hybrid Digital Twin for process industry using Apros simulation environment . in Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 . , 9613416 , IEEE , pp. 1-4 , IEEE International Conference on Emerging Technologies and Factory Automation , Västerås , Sweden , 07/09/2021 . https://doi.org/10.1109/ETFA45728.2021.9613416en
dc.identifier.doi10.1109/ETFA45728.2021.9613416en_US
dc.identifier.isbn9781728129907
dc.identifier.isbn9781728129891
dc.identifier.otherPURE UUID: a9ba2d69-0c61-4b95-be04-5efeae335dd3en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/a9ba2d69-0c61-4b95-be04-5efeae335dd3en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85122961551&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/76702515/ELEC_Azangoo_etal_Hybrid_digital_twin_for_process_industry_IEEE_ETFA_2021.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/111622
dc.identifier.urnURN:NBN:fi:aalto-2021121510763
dc.language.isoenen
dc.relation.ispartofIEEE International Conference on Emerging Technologies and Factory Automationen
dc.relation.ispartofseriesProceedings of 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021en
dc.rightsopenAccessen
dc.subject.keywordIndustriesen_US
dc.subject.keywordUncertaintyen_US
dc.subject.keywordDigital twinen_US
dc.subject.keywordPrototypesen_US
dc.subject.keywordMachine learningen_US
dc.subject.keywordHybrid power systemsen_US
dc.subject.keywordData modelsen_US
dc.titleHybrid Digital Twin for process industry using Apros simulation environmenten
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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