No abstract

3D computational fluid dynamics models using Fluent were developed to investigate the steel melt flow during waiting and arcing time. Both models were transient that analyzed 60 seconds to investigate the flow characteristics considering variation in steel melt thermo-physical parameters and operating conditions. The velocity of melt movement was high enough to make a turbulent flow (solved with realize k-ε turbulence model). It was found that the steel melt flow velocity increases by a combined effect of the steel melt temperature and composition, and slag pressure. The slag pressure increases by a double effect of slag density and height, and the steel melt fluid flow velocity changes with the slag pressure. The effect of the slag thickness is more significant than the effect of thermo-physical properties of steel melt. Although, the maximum steel melt velocity "during arcing time" may be as large as 0.67 m/s located at steel met outlet, the melt exhibits completely dead zones with minimum flow velocity distribution especially at the bottom and circumference areas. This indicates the importance of combined stirring and large reaction rates to achieve a complete homogeneous melt especially at bottom and circumference areas.

The present study aims at building a road map‐model for the steel melt compositional variations during the electric arc furnace (EAF) refining stage. The model is based on real measurements and compared to thermodynamic predictions. The studied parameters are steel melt carbon content and temperature. Herein, high‐temperature investigations (1550–1700 °C) with different chemistry ranges (mainly carbon content, 0.02–0.20% C) are carried out during the refining stage operation. The work develops a detailed empirical model to simulate the industrial EAF refining stage, which can be used to implement different optimization and control strategies for the EAF refining process. Furthermore, the study investigates the refining stage metallurgical phenomenon and the different operating conditions used to produce different steel grades over a wide range of carbon content; from ultra‐low carbon steel “0.02% C” till medium carbon steel grades “0.20% C.” Moreover, the work controls the steel melt tapping parameters for best cost achievement of steel melt yield and electrical energy consumption using simple measurements. These measurements are fitted into simple regression equations and contour plots for best refining stage control of the desired steel grade to be produced.