A prediction model for electric arc furnace steelmaking based on multiple energy inputs

This study proposes a conceptual model for predicting electric power consumption in electric arc furnace steelmaking. From the material and energy balance perspective, an optimization model was established for the 130 t pre-heated horizontal charging electric arc furnace of scrap steel built by a Chinese iron and steel group. The model dynamically correlates power supply time, hot metal charging ratio and scrap steel preheating temperature to predict power consumption while meeting metallurgical requirements. The Fluent module in ANSYS was used to conduct numerical simulation research on the preheating process of scrap steel, and the influences of four main factors, namely, initial gas velocity, initial gas temperature, preheating time and scrap porosity, on the preheating temperature of scrap steel were analyzed. This research provides theoretical guidance for the actual smelting production. The results show that when the scrap preheating type horizontal electric arc furnace was preheated from room temperature to 800 ℃ under the production mode of “all scrap steel”, 19.63 kWh/t power consumption could be saved for every 100 ℃ increase in temperature. The “hot metal combined scrap steel” production mode exhibited a more energy-saving potential.