Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

dc.contributor.authorHäußling Löwgren, Bartolomeus
dc.contributor.authorWeigert, Joris
dc.contributor.authorEsche, Erik
dc.contributor.authorRepke, Jens-Uwe
dc.date.accessioned2020-05-04T13:48:42Z
dc.date.available2020-05-04T13:48:42Z
dc.date.issued2020-03-20
dc.date.updated2020-04-29T00:01:58Z
dc.description.abstractIn this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.en
dc.description.sponsorshipBMWi, 0350013A, ChemEFlex - Umsetzbarkeitsanalyse zur Lastflexibilisierung elektrochemischer Verfahren in der Industrie; Teilvorhaben: Modellierung der Chlor-Alkali-Elektrolyse sowie anderer Prozesse und deren Bewertung hinsichtlich Wirtschaftlichkeit und möglicher Hemmnisseen
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlinen
dc.identifier.eissn2071-1050
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11081
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9969
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc006 Spezielle Computerverfahrende
dc.subject.otheruncertainty analysisen
dc.subject.otheroptimization under uncertaintyen
dc.subject.otherchance-constrained optimizationen
dc.subject.otherskewed distributionen
dc.titleUncertainty Analysis for Data-Driven Chance-Constrained Optimizationen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber2450en
dcterms.bibliographicCitation.doi10.3390/su12062450en
dcterms.bibliographicCitation.issue6en
dcterms.bibliographicCitation.journaltitleSustainabilityen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume12en
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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