Hybrid and cognitive digital twins for the process industry
In a Europe that is undergoing digital transformation, the COGNITWIN project is contributing to accelerate the transformation and introduce Industry 4.0 to the European process industries. The opportunities here can be illustrated by the SPIRE 2050 Vision document (https://www.spire2030.eu/sites/def...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-03-01
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| Series: | Open Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/eng-2022-0418 |
| _version_ | 1827969075408535552 |
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| author | Johansen Stein Tore Unal Perin Albayrak Özlem Ikonen Enso Linnestad Kasper J. Jawahery Sudi Srivastava Akhilesh K. Løvfall Bjørn Tore |
| author_facet | Johansen Stein Tore Unal Perin Albayrak Özlem Ikonen Enso Linnestad Kasper J. Jawahery Sudi Srivastava Akhilesh K. Løvfall Bjørn Tore |
| author_sort | Johansen Stein Tore |
| collection | DOAJ |
| description | In a Europe that is undergoing digital transformation, the COGNITWIN project is contributing to accelerate the transformation and introduce Industry 4.0 to the European process industries. The opportunities here can be illustrated by the SPIRE 2050 Vision document (https://www.spire2030.eu/sites/default/files/users/user85/Vision_Document_V6_Pages_Online_0.pdf), which states that “Digitalisation of process industries has a tremendous potential to dramatically accelerate change in resource management, process control and in the design and the deployment of disruptive new business models.” The process industries are characterized with harsh environments where sensors are either costly, not available, or may be subject to costly maintenance. The development of digital twins that can exploit the combinations of data-based and physics-based models is often found to be a preferred path to robust digital twins that can help cutting costs and reduce energy consumption. In this article, we present 5 out of 6 industrial pilots that are developed in the COGNITWIN project. We discuss the commonalities and differences between the selected approaches and give some ideas about how cognition can be incorporated into the digital twins. The aim of this article is to inspire similar projects in related industries. |
| first_indexed | 2024-04-09T18:32:23Z |
| format | Article |
| id | doaj.art-ddda392bac1a48a89546958f8f0f0d2e |
| institution | Directory Open Access Journal |
| issn | 2391-5439 |
| language | English |
| last_indexed | 2024-04-09T18:32:23Z |
| publishDate | 2023-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Engineering |
| spelling | doaj.art-ddda392bac1a48a89546958f8f0f0d2e2023-04-11T17:07:15ZengDe GruyterOpen Engineering2391-54392023-03-01131p. 1810.1515/eng-2022-0418Hybrid and cognitive digital twins for the process industryJohansen Stein Tore0Unal Perin1Albayrak Özlem2Ikonen Enso3Linnestad Kasper J.4Jawahery Sudi5Srivastava Akhilesh K.6Løvfall Bjørn Tore7Department of Process Technology, SINTEF Industry, S. P. Andersens Veg 15, N-7494, Trondheim, NorwayTeknopar, Ankara, TurkeyTeknopar, Ankara, TurkeyUniversity of Oulu, Oulu, FinlandCybernetica, Oslo, NorwayCybernetica, Trondheim, NorwayDepartment of Process Technology, SINTEF Industry, Porsgrunn, NorwayDepartment of Process Technology, SINTEF Industry, Trondheim, NorwayIn a Europe that is undergoing digital transformation, the COGNITWIN project is contributing to accelerate the transformation and introduce Industry 4.0 to the European process industries. The opportunities here can be illustrated by the SPIRE 2050 Vision document (https://www.spire2030.eu/sites/default/files/users/user85/Vision_Document_V6_Pages_Online_0.pdf), which states that “Digitalisation of process industries has a tremendous potential to dramatically accelerate change in resource management, process control and in the design and the deployment of disruptive new business models.” The process industries are characterized with harsh environments where sensors are either costly, not available, or may be subject to costly maintenance. The development of digital twins that can exploit the combinations of data-based and physics-based models is often found to be a preferred path to robust digital twins that can help cutting costs and reduce energy consumption. In this article, we present 5 out of 6 industrial pilots that are developed in the COGNITWIN project. We discuss the commonalities and differences between the selected approaches and give some ideas about how cognition can be incorporated into the digital twins. The aim of this article is to inspire similar projects in related industries.https://doi.org/10.1515/eng-2022-0418cognitive plantsdigital twinscognitionindustry 4.0process industry |
| spellingShingle | Johansen Stein Tore Unal Perin Albayrak Özlem Ikonen Enso Linnestad Kasper J. Jawahery Sudi Srivastava Akhilesh K. Løvfall Bjørn Tore Hybrid and cognitive digital twins for the process industry Open Engineering cognitive plants digital twins cognition industry 4.0 process industry |
| title | Hybrid and cognitive digital twins for the process industry |
| title_full | Hybrid and cognitive digital twins for the process industry |
| title_fullStr | Hybrid and cognitive digital twins for the process industry |
| title_full_unstemmed | Hybrid and cognitive digital twins for the process industry |
| title_short | Hybrid and cognitive digital twins for the process industry |
| title_sort | hybrid and cognitive digital twins for the process industry |
| topic | cognitive plants digital twins cognition industry 4.0 process industry |
| url | https://doi.org/10.1515/eng-2022-0418 |
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