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Supplementary funding for COGNITWIN project - Impact from Hybrid Twins for the Norwegian Process Industry

Awarded: NOK 0.99 mill.

The COGNITWIN project is funded by the European Union?s Horizon 2020 research and innovation programme under grant agreement No 870130. The main aim of the project is to develop a fully digitalized concept of self?learning and a proactive next generation of Digital Twins such as Hybrid Twins and Cognitive Twins for process industries, which can: * Recognize, forecast, and communicate less optimal process behaviour well before these occur and * Self-adjust to keep the process continuously close to or at optimum. The COGNITWIN Forsterk project focuses on disseminating results from the EU project to Norwegian stakeholders. This is done through activities such as webinars, popular scientific articles and participance in conferences.

While the concept of digitalisation and Industry 4.0 is making rapid inroads into the European manufacturing sector, there are several aspects that can still be incorporated and strengthen competitiveness and sustainability of Industrial process operations. One such aspect to the digitalisation vision is the "cognitive element", where the process plants can learn from historical data and adapt to changes in the process while also 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 element to the existing process control systems and thus enabling their capability to self-organise and offer solutions to unpredicted behaviours. Sgnificant tools for such cognitive behaviour are : 1) quality data from apppropriate sensors to provide correct information of process state (rawmaterial, Intermediates and Product qualities) 2) Digital twin, i.e. physical and data-driven models connected to smart graphical user interfaces 3) ML and AI algoritms that interact with the digital twins and act as decision support for the operators These three tools will, step by step, be developed against the ultimate competitive and sustainable autonomous and predictive operation of industrial processes.

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