Continuous [C] content prediction model in molten steel of the all-scrap electric arc furnace

The continuous [C] content prediction model in molten steel of the all-scrap electric arc furnace was established through the explicit finite difference method combined with numerical simulation based on the thermodynamics and kinetics of the decarburization reaction. Initially, a method to figure out the average [C] content of mixed input scrap was proposed, which provided a basis for subsequent calculations. Furthermore, scrap melting rate, mixing side-blowing/bottom stirring, and oxygen blowing intensity adjustment of oxygen lance had been considered as the three key elements. Decreasing scrap melting time was beneficial to improving the EAF efficiency because it accounts for 80% of the total operation time. The research found that the scrap melting rate was mainly affected by the solid-liquid phase heat transfer and [C] content transfer, and the heat transfer played a dominant role. Applying mixing side blowing/bottom stirring could lead to an improvement in the mass transfer coefficient of the decarburization reaction, and the value was increased from the range of 0.0059 s −1 -0.021 s −1 by using single side-blowing to the range of 0.0079 s −1 -0.023 s −1 . The mass transfer coefficient could be enhanced by the oxygen intensity adjustment of oxygen lances when mixing side blowing/bottom stirring was used. This was a benefit of deeper jet impact zones on the molten steel surface caused by high oxygen flow. After the model was applied to an industrial site, the prediction accuracy of the endpoint [C] content within the error range of ±0.02% and ±0.01% reached 91.2% and 85.7%, respectively.