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dc.contributor.authorBlakseth, Sindre Stenen
dc.contributor.authorAndersson, Leif Erik
dc.contributor.authorMocholí Montañés, Rubén
dc.contributor.authorMazzetti, Marit Jagtoyen
dc.date.accessioned2023-09-08T08:36:11Z
dc.date.available2023-09-08T08:36:11Z
dc.date.created2023-08-15T08:30:12Z
dc.date.issued2023
dc.identifier.citationComputer-aided chemical engineering. 2023, 52 831-836.en_US
dc.identifier.issn1570-7946
dc.identifier.urihttps://hdl.handle.net/11250/3088148
dc.description.abstractFour surrogate modelling techniques are compared in the context of modelling once-through steam generators (OTSGs) for offshore combined cycle gas turbines (GTCCs): Linear and polynomial regression, Gaussian process regression and neural networks for regression. Both fully data-driven models and hybrid models based on residual modelling are explored. We find that speed-ups on the order of 10k are achievable while keeping root mean squared error at less than 1%. Our work demonstrates the feasibility of developing OTSG surrogate models suitable for real-time operational optimization in a digital twin context. This may accelerate the adoption of GTCCs in offshore industry and potentially contribute towards a 25% reduction in emissions from oil & gas platforms. © 2023 Elsevier B.V. Author keywords Digital Twin; Gaussian Process Regression; Neural Networks; Residual Modelling; Surrogate Modelling
dc.description.abstractHybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleHybrid Dynamic Surrogate Modelling for a Once-Through Steam Generatoren_US
dc.title.alternativeHybrid Dynamic Surrogate Modelling for a Once-Through Steam Generatoren_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersion
dc.source.pagenumber831-836en_US
dc.source.volume52en_US
dc.source.journalComputer-aided chemical engineeringen_US
dc.identifier.doi10.1016/B978-0-443-15274-0.50133-5
dc.identifier.cristin2166930
dc.relation.projectNorges forskningsråd: 318899
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal