Slag Foaming Estimation in the Electric Arc Furnace using Machine Learning Based Long Short-Term Memory Networks

Abstract Slag foaming is a key factor in terms of quality and productivity in the electric arc furnace (EAF) steelmaking process. Optimal control of slag foaming is required, but is difficult due to the absence of practical on-line measuring methods and the broad process variability. In this study, a soft sensor model, which correlates the influential process variables with the slag foaming height, was developed by using machine learning based long short-term memory (LSTM) networks for modeling sequential and nonlinear data. The developed model was validated using actual steelmaking dataset in terms of performance metrics such as the root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient corresponding to a value for a SS400 carbon steel grade to be 42.3, 0.905, and 0.963, respectively. In order to evaluate the general applicability of the developed model for other steel grades, data for A615 and S355 steel grades were also applied and found to satisfy the benchmark standards indicating that the developed model can be applied to the broad range of other steel grades. Sensitivity-based Pruning (SBP) on the model shows that electricity, carbon and oxygen are the most influential process variables to the slag foaming height and could potentially be used to promote enhanced optimization in terms of energy saving and cost-efficiency for the EAF steelmaking process.