Slag foaming directly affects the productivity and quality of steel during the electric arc furnace (EAF) process. Therefore, the slag foaming height needs to be monitored in real-time. However, direct measurement of the slag foaming height is difficult to achieve because the inside of the EAF consists of harsh environments, i.e., high temperature and the presence of gas and dust. A stepwise regression model of the slag foaming height was created using sensor data from the EAF. A total of 272 operational data sets from the EAF process were used to develop and validate the regression model. This data came from 140ton DC-EAF of Dongkuk Steel in Pohang, Korea. We randomly selected 80% of the data for developing the regression model; the remaining 20% of data were used for model validation. The model was validated using the validation benchmark coefficient of determination (R2) and correlation coefficients. As a result, the important variables of slag foaming were statistically selected a priori. Using the regression model, the slag foaming height can be predicted without additional sensors. Based on the developed model, the effects of oxygen injection and carbon injection on the slag foaming height of EAF were predicted and are discussed herein.
All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Mechanics of Materials
- Metals and Alloys
- Materials Chemistry