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.

Our strategic, high-level objective is to establish the fully digitalized concept of self-learning and proactive next generation of Digital Twins, which operate in the hybrid world and can i) recognize, forecast and communicate less optimal process behaviour well before these occur and ii) adjust itself in order to keep the process continuously close to or at optimum. 


Specific Objectives:

  • COGNITWIN for Industry Process Excellence: Show improved performance in cognitive production plants by a technology demonstration of fully digitalized pilots.
  • Cognitive Digital Twins for Cognitive Retrofitting: Enabling an efficient and well-defined approach for “cognitive augmentation” of physical assets, processes and systems for Cognitive Digital Transformation in Process industry.
  • Hybrid Twins for Optimised Process Performance by hybrid models that combines first principle and data-driven models and use machine learning, AI and the connected data bases to pro-active forecast and communication, as well as self-learning by recognition of patterns in the data.
  • COGNITWIN Interoperability Toolbox as a Service: A reference architecture for the cognitive elements including of Big Data, Databases, IoT, Smart Sensors, Machine Learning, and AI technologies that realizes hybrid modelling, self-adaptivity and cognitive recognition, leveraging/extending the existing work into relevant communities.


  • COGNITWIN for increasing European Technology Dominance: Ensure the dominance of the Europe in technologies related to cognitive plants, thereby influencing the further development of Big Data, Databases, IoT, Smart Sensors, Hybrid Modelling, Machine Learning and AI technologies in relevant communities, focusing on the capabilities of the developed technologies for creating new generations of self-adaptive and cognitive algorithms and models. 
  • COGNITWIN for SPIRE: Ensure the knowledge transfer of results and experiences from the COGNITWIN project to the SPIRE Process Industry community, focusing on active participation in the new SPIRE DG7 Digitalisation group and in SPIRE organized events.
  • COGNITWIN for boosting European Industry: Provide competitive advantage to the European industry, esp. SMEs in the global market, through better exploitation of the synergies between Big Data, Databases, IoT, Smart Sensors, Hybrid Modelling, Machine Learning and AI technologies for an efficient resolution of complex process industrial challenges.
  • Effective dissemination and ensuring transfer of knowledge and experience generated in the pilots to the wide (European) audience in different industrial sectors by providing practical experiences from large-scale pilots to hundreds of companies through associated DIHs.