Prediction of Sulfur Content during Steel Refining Process Based on Machine Learning Methods

Abstract

The neural network technology combining genetic algorithm is utilized to predict the sulfur content and optimize the desulfurization operation at the end of the refining process. Three types of prediction models are developed to achieve the optimal model. The prediction accuracy can be improved by the application of the deep neural network while the root means square error (RMSE) value of the optimal prediction model and the mean absolute error (MAE) value are less than 5 ppm. Moreover, the proportion of heats with prediction errors less than 5 ppm reaches 82%. Effects of dissolved oxygen contents, initial sulfur contents, carbon contents, and the amount of desulfurizer addition on the desulfurization process are considered. The optimal amount of slag addition with various initial sulfur contents is calculated. With the increase of initial sulfur content in the molten steel, the optimal amount of slag‐modified agent addition increases from about 500–750 kg.