The Project

© DiGreeS
DiGreeS consortium

The aim of ”DiGreeS” is to develop a user-friendly digital platform for networked production based on novel and soft sensors and associated approaches and models. These are to be used in three individual use cases for different segments of the steel value chain.

 

  • Fraunhofer-Gesellschaft: Fraunhofer IZFP is coordinating the project under the leadership of Dr. Madalina Rabung
  • K1-MET GmbH (AT)
  • Szamitastechnikai es automatisalasi kutatointezet (HU)
  • VDEH-Betriebsforschungsinstitut GmbH (DE)
  • Fraunhofer Austria Research GmbH (AT)
  • Spectral Industries (NL)
  • Tata Steel (NL)
  • Saarstahl (DE)
  • voestalpine Steel & Service Center GmbH (AT)
  • voestalpine group-IT GmbH (AT)
  • ESTEP (B)

Ambition

The transition to low carbon and eco-friendly steel production in Europe requires a significant transformation of the steelmaking processes, particularly the introduction of new steelmaking routes. There is a strong need for enablers to plan and manage this revolution and ensure sustainable steel production. In this context, the steel production requires breakthrough technologies to reduce its environmental footprint as close to zero as possible. Additionally, a seamless digitalization of the production processes and skilled personnel are necessary to support and comprehend the transformation process.

DiGreeS will tackle these challenges by implementing an integrated digitalization approach throughout the steel value chain, to enable an enhanced use of the industrial data collected along the process chain and ensuring the uptake of human experiences for easier industrial integration.

The aim of DiGreeS is to develop a user-friendly digital platform for networked production based on novel and soft sensors as well as related approaches and models, which will be demonstrated in three individual use cases targeting different segments of the steel value chain. Within DiGreeS comprehensive Digital Twins will be developed to support the efficient feedstock verification and real-time control of the production of crude steel (CS) with the electric arc furnace (EAF) and to increase the process yield while improving the quality of intermediate and final steel products. In this context the potential of artificial intelligence (AI) and machine learning (ML) technologies will be fully exploited to support the optimal use of process data, and various scenarios specific to each use case (UC) will be modelled. Consequently, DiGreeS will apply digitalization solutions to improve the product quality of CS and final products, to enhance the raw material and energy efficiency of the steel production process, and thus to increase the circularity and re-duce the CO2 emissions of steel production. DiGreeS has the potential to save up to 800 million € in costs annually and reduce CO2 emissions from the steelmaking industry by up to 6 million tons per year.