Adaptive sampling of dynamic systems for generation of fast and accurate surrogate models
| dc.contributor.author | Talis, Torben | |
| dc.contributor.author | Weigert, Joris | |
| dc.contributor.author | Esche, Erik | |
| dc.contributor.author | Repke, Jens-Uwe | |
| dc.date.accessioned | 2022-01-07T10:13:07Z | |
| dc.date.available | 2022-01-07T10:13:07Z | |
| dc.date.issued | 2021-11-05 | |
| dc.description.abstract | For 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.sponsorship | TU Berlin, Open-Access-Mittel – 2021 | en |
| dc.identifier.eissn | 1522-2640 | |
| dc.identifier.issn | 0009-286X | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/16097 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-14871 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 660 Chemische Verfahrenstechnik | de |
| dc.subject.other | adaptive sampling | en |
| dc.subject.other | dynamic data-driven modeling | en |
| dc.subject.other | recurrent neural networks | en |
| dc.subject.other | surrogate modeling | en |
| dc.title | Adaptive sampling of dynamic systems for generation of fast and accurate surrogate models | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1002/cite.202100109 | en |
| dcterms.bibliographicCitation.issue | 12 | en |
| dcterms.bibliographicCitation.journaltitle | Chemie - Ingenieur - Technik | en |
| dcterms.bibliographicCitation.originalpublishername | Wiley | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.pageend | 2104 | en |
| dcterms.bibliographicCitation.pagestart | 2097 | en |
| dcterms.bibliographicCitation.volume | 93 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 3 Prozesswissenschaften::Inst. Prozess- und Verfahrenstechnik::FG Dynamik und Betrieb technischer Anlagen | de |
| tub.affiliation.faculty | Fak. 3 Prozesswissenschaften | de |
| tub.affiliation.group | FG Dynamik und Betrieb technischer Anlagen | de |
| tub.affiliation.institute | Inst. Prozess- und Verfahrenstechnik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |