dc.contributor.author | Blakseth, Sindre Stenen | |
dc.contributor.author | Andersson, Leif Erik | |
dc.contributor.author | Mocholí Montañés, Rubén | |
dc.contributor.author | Mazzetti, Marit Jagtoyen | |
dc.date.accessioned | 2023-09-08T08:36:11Z | |
dc.date.available | 2023-09-08T08:36:11Z | |
dc.date.created | 2023-08-15T08:30:12Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Computer-aided chemical engineering. 2023, 52 831-836. | en_US |
dc.identifier.issn | 1570-7946 | |
dc.identifier.uri | https://hdl.handle.net/11250/3088148 | |
dc.description.abstract | Four 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.abstract | Hybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator | |
dc.language.iso | eng | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Hybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator | en_US |
dc.title.alternative | Hybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | |
dc.source.pagenumber | 831-836 | en_US |
dc.source.volume | 52 | en_US |
dc.source.journal | Computer-aided chemical engineering | en_US |
dc.identifier.doi | 10.1016/B978-0-443-15274-0.50133-5 | |
dc.identifier.cristin | 2166930 | |
dc.relation.project | Norges forskningsråd: 318899 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |