The use of artificial neural networks for the prediction of sulphur content in hot metal produced in blast furnace

Purpose: The paper presents the possibilities of using artificial intelligence for the prediction of sulphur content in hot metal produced in blast furnace. Design/methodology/approach: Three blast furnaces in ArcelorMittal, Unit in Dąbrowa Górnicza, provided the data for the model construction. The data reflect a number of variables, which describe the blast furnace process. Findings: : Materials research performed with the use of data mining and neural networks is consistent with the results obtained during the real research in a real laboratory. The obtained results show that the construction of such neural networks is practical. There is a strong correlation between predicted value and real value. Practical implications: The presented model can be used in the industrial practice as an additional tool for blast furnace and steel plant operators. Originality/value: Prediction of sulphur content in hot metal at the stage of adjusting hot metal process parameters.