Abstract
The paper presents a methodology of improving the efficiency of the steelmaking process based on computational intelligence solutions. To make fine adjustments to the steel composition alloy additions are added to the crude steel to adjust composition for the grade of steel being manufactured. The prediction of metal bath composition is a crucial factor in the economy of ladle furnace operation. Usually it is made by calculations based on the equilibrium of chemical reactions in molten steel. The paper presents the problem solution based on the prediction system build upon the committee of Artificial Neural Networks, the Support Vector Regression and Multivariate Linear Regression Model. A brief state of the art review of the application of computational intelligence (CI) in secondary steelmaking has been made. The prediction system used by authors has been introduced. Problems with data preparation have been presented. Experimental results and the final conclusions and recommendations have been presented. The solutions was implemented in one of the steelworks, where it allowed to improve the economy of the secondary steelmaking process.