Johansen, S., Unal, P., Albayrak, Ö., Ikonen, E., Linnestad, K., Jawahery, S., Srivastava, A. & Løvfall, B. (2023). Hybrid and cognitive digital twins for the process industry. Open Engineering, 13(1), 20220418. https://doi.org/10.1515/eng-2022-0418
Hybrid and cognitive digital twins for the process industry
|Author:||Johansen, Stein Tore1; Unal, Perin2; Albayrak, Özlem2;|
1Department of Process Technology, SINTEF Industry, S. P. Andersens Veg 15, N-7494, Trondheim, Norway
2Teknopar, Ankara, Turkey
3University of Oulu, Oulu, Finland
4Cybernetica, Oslo, Norway
5Department of Process Technology, SINTEF Industry, Porsgrunn, Norway
|Online Access:||PDF Full Text (PDF, 2.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20230912122813
|Publish Date:|| 2023-09-12
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
215 Chemical engineering
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This research was funded by the H2020 COGNITWIN project, which have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 870130.
|EU Grant Number:||
(870130) COGNITWIN - COGNITIVE PLANTS THROUGH PROACTIVE SELF-LEARNING HYBRID DIGITAL TWINS
© 2023 the author(s), published by De Gruyter This work is licensed under the Creative Commons Attribution 4.0 International License.