dc.contributor.author | Hotvedt, Mathilde | |
dc.contributor.author | Grimstad, Bjarne André | |
dc.contributor.author | Ljungquist, Dag | |
dc.contributor.author | Imsland, Lars Struen | |
dc.date.accessioned | 2023-03-02T15:31:25Z | |
dc.date.available | 2023-03-02T15:31:25Z | |
dc.date.created | 2023-01-20T00:04:22Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2405-8963 | |
dc.identifier.uri | https://hdl.handle.net/11250/3055527 | |
dc.description.abstract | Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predictions. However, the superiority of gray-box models is yet to be demonstrated in the literature. This article examines scenarios where a gray-box model is expected to outperform physics-based and data-driven models. The experiments are conducted with synthetic data where properties of the underlying data generating process are controlled. The results show that a gray-box model yields increased prediction accuracy over a physics-based model in the presence of process-model mismatch, and improvements over a data-driven model when the amount of available data is small. On the other hand, gray-box and data-driven models are similarly influenced by noisy measurements. Lastly, the results indicate that a gray-box approach may be advantageous in nonstationary process conditions. Unfortunately, model selection prior to training is challenging, and overhead on gray-box model development and testing is unavoidable. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | When is gray-box modeling advantageous for virtual flow metering? | en_US |
dc.title.alternative | When is gray-box modeling advantageous for virtual flow metering? | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | IFAC-PapersOnLine | en_US |
dc.identifier.doi | 10.1016/j.ifacol.2022.07.496 | |
dc.identifier.cristin | 2110993 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |