Adaptive sampling of dynamic systems for generation of fast and accurate surrogate models

dc.contributor.authorTalis, Torben
dc.contributor.authorWeigert, Joris
dc.contributor.authorEsche, Erik
dc.contributor.authorRepke, Jens-Uwe
dc.date.accessioned2022-01-07T10:13:07Z
dc.date.available2022-01-07T10:13:07Z
dc.date.issued2021-11-05
dc.description.abstractFor economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis.en
dc.description.sponsorshipTU Berlin, Open-Access-Mittel – 2021en
dc.identifier.eissn1522-2640
dc.identifier.issn0009-286X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16097
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-14871
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc660 Chemische Verfahrenstechnikde
dc.subject.otheradaptive samplingen
dc.subject.otherdynamic data-driven modelingen
dc.subject.otherrecurrent neural networksen
dc.subject.othersurrogate modelingen
dc.titleAdaptive sampling of dynamic systems for generation of fast and accurate surrogate modelsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1002/cite.202100109en
dcterms.bibliographicCitation.issue12en
dcterms.bibliographicCitation.journaltitleChemie - Ingenieur - Techniken
dcterms.bibliographicCitation.originalpublishernameWileyen
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.pageend2104en
dcterms.bibliographicCitation.pagestart2097en
dcterms.bibliographicCitation.volume93en
tub.accessrights.dnbfreeen
tub.affiliationFak. 3 Prozesswissenschaften>Inst. Prozess- und Verfahrenstechnik>FG Dynamik und Betrieb technischer Anlagende
tub.affiliation.facultyFak. 3 Prozesswissenschaftende
tub.affiliation.groupFG Dynamik und Betrieb technischer Anlagende
tub.affiliation.instituteInst. Prozess- und Verfahrenstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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