ProcTwin aims to develop a demonstration platform to predict and visualize best use of multiple processing steps in a steel manufacturing chain. The methodology includes intelligent coupling of interconnected processing steps by numerical simulation, soft sensors, process data and distributed machine learning.  

Integrated numerical modelling that captures the interactions, relations, and feedback loops between various processing stations enables prediction for smart optimization of energy efficiency and product quality in the steel manufacturing. Continuous casting, reheating, hot metal working, quenching and leveling processes are examples that are controlled separately but strongly interconnected in terms of parameters. These processes serve as objective functions in two parallel use cases at Celsa (ES) and SSAB (SW).

It is well known that process optimization can have a significant effect on reducing carbon footprint in steel production, and implementing new digital tools will enable a faster transition towards sustainable industry. 

ProcTwin is divided in clear work packages to reach the objectives: one is adaption of existing physically based numerical models of each process step to generate critical data that is impossible to measure or observe. 

Another is development of novel sensors and data integration for a secure and effective sharing industrial data. The innovative concept of ProcTwin is development of distributed machine learning to predict the process chains with large amounts of parameters.

Lastly, these technologies will be combined through a demonstrator platform to model the manufacturing processes and enable control for increased product quality, energy efficiency and operator support.

The ProcTwin project is built upon a set of strategic and technical objectives that will enable a transformative shift in steel manufacturing:

1. Integrated Numerical Modelling
   Develop physics-based simulation models tailored to each process step in the steel production chain. Capture the interdependencies, interactions, and feedback loops to generate essential data that are otherwise inaccessible.
   
   
2. Advanced Soft Sensors and Data Integration
   Design and implement innovative soft sensors that allow for real-time process monitoring. Build secure and interoperable systems to integrate industrial data across multiple platforms and facilities.
   
   
3. Distributed Machine Learning
   Employ cutting-edge AI techniques to manage complex, distributed data environments. Train machine learning models that predict outcomes and quality metrics, continuously improving through real-time data feedback.
   
   
4. Demonstration in Industrial Use Cases
   Showcase the digital twin platform at two major steel production sites:
   • Celsa Group (Spain): Focusing on surface quality and rolling optimization.
   • SSAB EMEA (Sweden): Enhancing control in quenching and leveling processes
     
     
5. Facilitate Sector-Wide Adoption
   Create a demonstrator platform that can be scaled to other industrial contexts. Empower plant operators with intuitive interfaces and support policy and standardization efforts in digital manufacturing.

This project has received funding from the European Union under grant agreement number 101178721 - PROCTWIN.

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