Scrap management by statistical evaluation of EAF process data

The electric arc furnace (EAF) is a process for melting steel scrap with electricity. In this paper a method for estimating scrap properties based on the evaluation of historical process data is discussed and a series of scrap management strategies based on the estimated properties are suggested. Data from four Swedish EAFs have been analysed and on-line software applications have been developed and installed at one steel plant. The results from this study show that it is possible to use partial least squares to provide accurate estimates of the levels of impurity (Cu, Sn, As) and alloy content (Cr, Ni, Mo) in scrap grades. The degree of explained variation (R2) obtained in this study ranges between 40% and 70% for impurity elements and 70% and 100% for alloy elements. The mean prediction errors (RMSEE) are in some cases small enough to improve steel quality control in terms of chemical analysis. To ensure that the estimates remain consistent with scrap quality, it is suggested that the prediction models be updated on a regular basis.