BFI VDEh-Betriebsforschungsinstitut GmbH

Initial situation:

Steel manufacturing consists of multiple processing steps - such as continuous casting, reheating, hot metal working, quenching, and leveling - that are controlled separately but are strongly interconnected through shared parameters. These dependencies make it difficult to optimize the production chain, although process optimization is known to significantly reduce the carbon footprint. Many critical process data cannot be measured directly, and new digital tools are needed to accelerate the transition toward more sustainable steel production.

Project targets:

• Develop a demonstration platform to predict and visualize the optimal use of multiple processing steps in a steel manufacturing chain.
• Enable smart optimization of energy efficiency and product quality through integrated numerical modelling.
• Apply and validate the approach in two industrial use cases at Celsa (ES) and SSAB (SW).
• Provide improved process control and operator support across the whole production chain.

Innovative approaches:

• Intelligent coupling of interconnected processing steps using numerical simulation, soft sensors, process data, and distributed machine learning.
• Integrated numerical modelling capturing interactions, dependencies, and feedback loops across processing stations.
• Adaptation of physically based models for each process step to generate otherwise unmeasurable critical data.
• Development of novel sensors and data-integration methods for secure and efficient industrial data sharing.
• Distributed machine learning to predict complex process chains with large parameter sets.

Benefits for the industry:

• Improved energy efficiency and reduced production losses.
• Enhanced product quality through better prediction and coordination of process steps.
• Faster and more effective transition toward low-carbon and sustainable steel production.
• Stronger operator support through data-driven insights and process visualization.
• Better utilization of existing equipment via optimized process chains.

Further information:

https://www.estep.eu/clean-steel-partnership/proctwin

https://www.linkedin.com/company/proctwin-project/

This project is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.