The use of artificial neural networks for the prediction of a chemical composition of hot metal produced in blast furnace

摘要

Purpose: The paper presents the possibilities of using neural networks for the prediction of chemical composition of hot metal produced in blast furnace. Design/methodology/approach: Three blast furnaces in ArcelorMittal, Unit in D.browa Górnicza, provided the data for the model construction. The data reflect a number of variables, which describe the blast furnace process. Findings: The results obtained, based on input parameters, show that the construction of such neural networks is viable. There is a good correlation between expected and actual results. Practical implications: The model can be used as an auxiliary tool for blast furnace operators. Originality/value: Prediction of a chemical composition of hot metal at the stage of adjusting hot metal process parameters.