Iterative process design with surrogate-assisted global flowsheet optimization

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dc.contributor.authorJanus, Tim
dc.contributor.authorEngell, Sebastian
dc.date.accessioned2022-03-15T12:18:25Z
dc.date.available2022-03-15T12:18:25Z
dc.date.issued2021-11-03
dc.description.abstractFlowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for rejection and generation of candidates to a framework for global flowsheet optimization. These extensions halve the amount of time needed for optimization such that the integration of the framework in a workflow for iterative process design becomes applicable.en
dc.identifier.urihttp://hdl.handle.net/2003/40793
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22650
dc.language.isoende
dc.relation.ispartofseriesChemie - Ingenieur - Technik;93(12)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learningen
dc.subjectMachine learningen
dc.subjectProcess designen
dc.subjectProcess optimizationen
dc.subjectProcess synthesisen
dc.subject.ddc660
dc.titleIterative process design with surrogate-assisted global flowsheet optimizationen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationJanus, T. and Engell, S. (2021), Iterative Process Design with Surrogate-Assisted Global Flowsheet Optimization. Chemie Ingenieur Technik, 93: 2019-2028. https://doi.org/10.1002/cite.202100095de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202100095de

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