A static mass and energy balance model combined with a MgO saturation slag model is developed for electric arc furnaces. The model parameters including distribution ratios and dust factors are calibrated for a specific furnace using experimental data. Afterward, the model is applied to study the effect of charging different amounts of hot briquetted iron (HBI) on energy consumption, charged slag former amount, and slag composition. The following results were obtained per each 1% increase of HBI additions: (i) a 0.16 Nm³/t decrease in the amount of injected oxygen for metal oxidation, (ii) a 1.29 kWh/t increase in the electricity consumption, and (iii) a 34 kg increase in the amount of the slag.

Numerical modeling was used to study the capability of postcombustion in an electric arc furnace (EAF) equipped with virtual lance burners. The CO flow rate at the molten bath surface was estimated using the off-gas data obtained close to the outlet of an EAF. Then, the effect of the secondary oxygen flow rate on postcombustion was studied. The results show a CO flow rate of 0.6 kg·s⁻¹ and 0.8 kg·s⁻¹ for operation modes of burner and burner + lancing. Increase of the secondary oxygen flow rates of 60% and 70% result in 17% and 7% increase in the postcombustion ratio (PCR) for the burner and burner lancing modes, respectively.

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.