Development of a computational thermodynamics EERZ model for the improvement of hot rolled light steel profiles steel refining

Light steel profiles are steels with medium C, Mn and Si contents. In these steels internal cleanness is important for mechanical properties and surface quality, both during hot rolling and during the coating of the final product. Production costs of these steels are defined by the total heat time, from charging the Electric Arc Furnace (EAF) to starting continuous casting. Ladle furnace (LF) processing has a large influence on heat time and costs related to deoxidation, meeting composition requirements with a high yield of oxidizing alloying additions and proper rinsing to promote homogenization and cleanness. Thermodynamics and kinetics control how slag and steel oxidation levels evolve, as well as the evolution of the non-metallic inclusion population since at this stage primary oxides are the main inclusions. A model based on solid thermodynamic and kinetics fundamentals would be extremely useful for the producer of these steels, so that the evolution of the heat in the ladle furnace could be properly understood and, afterward, improve. In this work, we present the development of an “Effective Equilibrium Reaction Zone” (EERZ) for the LF processing of these steels, using a well-established thermodynamic database for metal and slags and a module of commercial computational thermodynamic software. We aim at demonstrating that these easily accessible and useable tools can be very effective to help in industrial process development. Thus, based on three selected industrial heats we adjust the kinetic parameters to describe the processes occurring for tapping from the EAF to releasing the heat for continuous casting. Volumetric mass transfer coefficients, heat transfer coefficients and other less critical variables are adjusted based on these heats and the model is then tested in seven further heats of the same steel grade. Results indicate that it is possible to achieve a good fit of the model to the industrial process and this helps identifying critical process variables to optimize the process schedule of these steels. Furthermore, we identify possible current limitations of the modeling strategy as suggested in the preliminary steps for the development of a EERZ model that may be coupled for on-line use, the support LF operators.