Hybrid and cognitive digital twins for the process industry

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.

[1]  Tchetin Kazak.  European Green Deal , 2022, Yearbook of the Law Department.

[2]  Ljiljana Stojanovic,et al.  An Approach for Realizing Hybrid Digital Twins Using Asset Administration Shells and Apache StreamPipes , 2021, Inf..

[3]  Heinz A Preisig,et al.  Ontology-Based Process Modelling-with Examples of Physical Topologies , 2021, Processes.

[4]  Tomas Benesl,et al.  Automated Design and Integration of Asset Administration Shells in Components of Industry 4.0 , 2021, Sensors.

[5]  Jian Zhang,et al.  How to model and implement connections between physical and virtual models for digital twin application , 2020 .

[6]  Dumitru Roman,et al.  COGNITWIN – Hybrid and Cognitive Digital Twins for the Process Industry , 2020, 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[7]  Louise Wright,et al.  How to tell the difference between a model and a digital twin , 2020, Advanced Modeling and Simulation in Engineering Sciences.

[8]  M. Zheludkevich,et al.  Interoperability architecture for bridging computational tools: application to steel corrosion in concrete , 2020, Modelling and Simulation in Materials Science and Engineering.

[9]  Tomas Benesl,et al.  Digital Twin and AAS in the Industry 4.0 Framework , 2019, IOP Conference Series: Materials Science and Engineering.

[10]  Stefan Pirker,et al.  Recurrence CFD – A novel approach to simulate multiphase flows with strongly separated time scales , 2016, 1606.00586.

[11]  Adrian Sandu,et al.  POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation , 2014, J. Comput. Phys..

[12]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[13]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[14]  P. Beran,et al.  Reduced-order modeling: new approaches for computational physics , 2004 .

[15]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[16]  Arne-Jørgen Berre,et al.  Data-Driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins , 2022, Technologies and Applications for Big Data Value.

[17]  P. Klein,et al.  A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability , 2021 .

[18]  Sebastian R. Bader,et al.  Smart Services in the Physical World: Digital Twins , 2020 .

[19]  S. T. Johansen,et al.  On pragmatism in industrial modeling , 2015 .