Predicting Steel Grade Based on Electric Arc Furnace End Point Parameters

Steelmaking through Electric Arc Furnace (EAF) is known to be energy and cost intensive therefore, any improvement in processes will result in economic and environmental benefits. This study aims to improve the efficiency of the EAF process by predicting the most feasible steel grade which can be obtained with minimum purification based on endpoint parameters. Naïve Bayes classifier algorithm was employed to categorize EAF operational data. The operational data consists of 16 parameters with more than ten thousand data samples which classified into 6 possible steel grades, the carbon content of molten steel is defined as the decision variable to classify operational data. Finally, the results are also compared with MS Excel to examine how well the machine learning algorithm can be obtained. The results show the algorithm can classify data with more than 90% accuracy.