FFG K1-MET Project 07/2023-06/2026
This project aims at employing advanced data analyses and methodology in order to investigate process data from different processes in the steelmaking chain, generating process understanding and knowledge on correlations and causations in operation, as well as develop recommendation or warning systems for the operator in order to adjust and improve operation. Topics range from questions on operation and stability of the blast furnace (BF), to the production of ultra-clean steels with Ruhrstahl-Heraeus (RH) treatment and the optimization of the continuous casting (CC) process.
Work Packages
- WP1-4: Temperature irregularities in BF bottom/ hearth, mass balance of zinc and alkali elements, investigations of BF charging models/ charging profile, raceway monitoring analyses
- WP5: Image analysis and state classification at the RH plant
- WP6: Hybrid Mold – Data evaluations around the CC process
Expected Results
- (WP1-4) Blast furnace: Prediction of temperature irregularities, mass balances in BF operation, charging models and development of optimized charging strategies, analyses of raceway blockages and possible correlations with process parameters and image material, predictive maintenance for tuyeres
- (WP5) RH plant: Comprehensive benchmark case for machine learning algorithms, setup of an advisory algorithm for the operator to be warned of irregular states of the RH plant
- (WP6) Continuous Casting: Modelling of heat transfers in the mold based on a hybrid approach, combining data from sensors in the CC mold with physical/ metallurgical-based process models
Project Consortium
Joanneum Research GmbH – Institute DIGITAL
Johannes Kepler University Linz – Department of Particulate Flow
Linz Center of Mechatronics – Area SENS
Montanuniversität Leoben
Chair of Cyberphysical Systems
Chair of Ferrous Metallurgy
Primetals Technologies Austria GmbH
voestalpine Stahl GmbH
voestalpine Stahl Donawitz GmbH
Links
Details on the research project can be found on the project webpage.
Funding Agency
- Österreichische Forschungsförderungsgesellschaft mbH (FFG)