Statistical Inference Modeling Using Pearson Correlation Filters and Best Subset Selection Applied to Steel Desulfurization Preliminary to Ladle Furnace Treatment

Research on low-sulfur and ultra-low-sulfur steel is an important field, since sulfur is known to increase hydrogen-induced cracking, lower hot ductility, and increase solidification cracking susceptibility. Several approaches can be used to assess the sulfur removal performance in steel-making plants, and to improve the desulfurization reaction. Statistical data-driven approaches improve the capacity to build mathematical models that merge all thermodynamics and kinetics features, and measure each one’s importance in the specific studied process. The present study aims to model the desulfurization process from the steel tapping until the ladle arrival at the ladle furnace station in a billet steel-making plant. It was performed using Pearson correlation filters and an implementation of a best subset selection algorithm with multiple linear regression. It was observed that, for the analyzed heats, the silicon carbide added during the tapping, the ladle transfer time, the EAF slag viscosity, and the activity of CaO·Cr 2 O 3 are the most important features that influence the desulfurization efficiency between the steel tapping and the arrival at the ladle furnace station. It was observed that the metallurgical theoretical background is an essential requirement to build appropriate process mathematical models and avoid wrong decisions.