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COGNITWIN - Cognitive plants through proactive self-learning hybrid digital twins

COGNITWIN will set a new standard for the design, development and operation of the European process industry by introducing a platform for virtual component-based architecture that integrates IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities.

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While the concepts of digitalisation and Industry 4.0 are making rapid inroads into the European manufacturing sector, there are several aspects that can be still incorporated into the system to strengthen the goal of optimal process operations. One such aspect to the digitalisation vision is the "cognitive element", where the process plants can learn from pattern recognition in historical data and adapt to changes in the process, simultaneously being able to predict unwanted events in the operation before they happen. Through this project, COGNITWIN (Cognitive Digital Twin), we aim to add the cognitive elements to the existing process control systems and thus enabling their capability to self-organise and offer solutions to unpredicted behaviours. 

To achieve the objectives of the project, we have built a consortium of following six process industries, four research groups and four technology providers from seven countries:


  • Hydro Aluminium Deutschland GmbH 
  • SHI FW Energia Oy
  • Sidenor Aceros Especiales Europa S.L. 
  • Elkem ASA 
  • Saarstahl AG
  • Noksel Steel Pipe Company 

University/R&D Institutes

  • The German Research Center for Artificial Intelligence (DFKI) 
  • Fraunhofer-Gesellschaft 
  • University of Oulu 

Technology Providers

  • Cybernetica AS
  • Nissatech Innovation Centre
  • TEKNOPAR Industrial Automation 
  • Scortex 

SINTEF is coordinating the project and leading two work packages; 'Project Management' and 'COGNITWIN Hybrid AI and Cognitive Twin Toolbox. 

For more information about the project, see the COGNITWIN web site.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 870130

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Project duration

2019 - 2023

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