Towards CO2-lean electric arc furnace steelmaking through fundamental and data-driven mathematical modeling


Steel is a material with unique properties, the limits of which are still to be found. Steel is produced mainly from iron ore by a combination of blast furnace (BF) ironmaking and basic oxygen furnace (BOF) steelmaking, although the share of scrap-based steelmaking using the electric arc furnace (EAF) process has increased steadily.

Project information

Project duration


Funded by

Research Council of Finland - Academy Research Fellow

Funding amount

271 627 EUR

Project coordinator

University of Oulu

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Project description

Climate change puts the steel industry under tremendous pressure of change. The European Union has issued a target of CO2-free steel production by 2040.

The CO2 emissions per ton of steel produced are much lower using the EAF process, but the energy intensity of the EAF process creates a significant incentive to reduce the energy and material efficiency of the process. The fundamental limits of the contemporary EAF control practice of using static energy input set-points and history data the limits of efficiency will soon be reached. In an ideal case, the operating practice would be adapted automatically for a desired tap-to-tap time depending on the requirements of production planning. The prediction of the electric arc furnace process is conducted with mathematical models. Contemporary models typically do not adapt to changes in process practice and are not applicable for real-time prediction of the process. The research proposed in this project builds on the current state of the art and aims to advance the boundaries of science further in three ways:

  1. by employing a constrained Gibbs energy minimization to obtain detailed information on the process kinetics,
  2. by combining the established fundamental basis of the EAF models with machine learning,
  3. by synchronizing the model for the time scale of online applications, and
  4. by making use of new optical emissions spectroscopy-based online measurements to provide insight into the predicted process

The improvements in process control are expected to make the CO2-lean scrap-based EAF steelmaking an economically feasible alternative to the blast furnace route. The developed modeling and control approaches reduce the specific electricity consumption of existing electric arc furnaces and consequently help reduce the emissions of energy production. Furthermore, better control of the process enables a higher degree of internal circulation as well as the use of biomaterials to replace fossil materials in the EAF. Improvements in the modeling and control of the EAF process also promote a shift to CO2-free hydrogen-based ironmaking.