Abstract
A statistical model is developed in order to simulate the melt composition in electric arc furnaces (EAFs) with respect to uncertainties in (1) scrap composition, (2) scrap weighing and (3) element distribution factors. The tramp element Cu and alloying element Cr are taken into account. The model enables simulations of a charge program as well as backwards estimations of the element concentrations and their variance in scrap. In the backwards calculation, the maximum likelihood method is solved by considering three cases corresponding to the involved uncertainties. It is shown that the model can estimate standard deviations for elements so that the real values lie within the estimated 95% confidence interval. Moreover, the results of the model application in each target product show that the estimated scrap composition results in a melt composition, which is in good agreement with the measured one. The model can be applied to increase our understanding of scrap chemical composition and lower the charged material cost and carbon footprint of the products.
Highlights
Mixing raw materials is critical in scrap-based plants, such as electric arc furnaces (EAFs), where meeting chemical composition targets and a cost-efficient mix of raw materials should be simultaneously taken into account
The results showed that the method is applicable when there is a correlation between a scrap and other scrap types and the target product chemical composition
Afterwards, the model is used to investigate the accuracy of Cu and Cr concentrations in the reported scrap compositions in an EAF plant
Summary
Mixing raw materials is critical in scrap-based plants, such as electric arc furnaces (EAFs), where meeting chemical composition targets and a cost-efficient mix of raw materials should be simultaneously taken into account. The aim is to develop a statistical model as a calculation tool that can mainly be used in an EAF plant to deal with the uncertainties in steel scrap analysis It should deal with lack of scrap analysis and facilitate production planning of a heat to meet the target composition. It is important to evaluate the copper concentration and its dispersion in different types of charged scrap This importance is described by Gyllenram et al [12] by obtaining a relationship between the quality cost, confidence interval and percent of low-quality scrap with high Cu content (with an average of 0.3 and standard deviation of 0.03), which can be used to determine the risk percent lying outside the target product limits. Metals 2021, 11, 892 ally, the uncertainties involved in weighing of scrap and in element distribution factors are included
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