Hybrid and cognitive digital twins for the process industry

Author:

Johansen Stein Tore1,Unal Perin2,Albayrak Özlem2,Ikonen Enso3,Linnestad Kasper J.4,Jawahery Sudi5,Srivastava Akhilesh K.6,Løvfall Bjørn Tore7

Affiliation:

1. Department of Process Technology, SINTEF Industry , S. P. Andersens Veg 15, N-7494 , Trondheim , Norway

2. Teknopar , Ankara , Turkey

3. University of Oulu , Oulu , Finland

4. Cybernetica , Oslo , Norway

5. Cybernetica , Trondheim , Norway

6. Department of Process Technology, SINTEF Industry , Porsgrunn , Norway

7. Department of Process Technology, SINTEF Industry , Trondheim , Norway

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Aerospace Engineering,General Materials Science,Civil and Structural Engineering,Environmental Engineering

Reference23 articles.

1. EU Commission, The European Green Deal, Communication From The Commission To The European Parliament, The Council, The European Economic And Social Committee And The Committee Of The Regions, COM(2019) 640 Final; 2019.

2. Birol F. Key world energy statistics 2021 [Internet]. International Energy Agency; 2021. Available from: https://www.iea.org/reports/key-world-energy-statistics-2021.

3. A.SPIRE, Processes4Planet Roadmap: Sustainable Process Industry through Resource and Energy Efficiency; 2021.

4. Abburu S, Berre AJ, Jacoby M, Roman D, Stojanovic L, Stojanovic N. COGNITWIN – Hybrid and Cognitive Digital Twins for the Process Industry. In Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). Cardiff, United Kingdom: IEEE; June 2020. p. 1–8.

5. Lucia DJ, Beran PS, Silva WA. Reduced-order modeling: New approaches for computational physics. Prog Aerosp Sci. 2004;40:51–117. 10.1016/j.paerosci.2003.12.001.

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