Model Developments to Study Some Aspects of Improving Efficiencies in EAF Plants
Time: Thu 2020-06-11 10.00
Location: https://kth-se.zoom.us/j/67826970577 , Vid fysisk närvaro eller Du som saknar dator/datorvana kan kontakta firstname.lastname@example.org, (English)
Doctoral student: Niloofar Arzpeyma , Materialvetenskap, Unit of Process
Opponent: Professor Emeritus Lauri Holappa,
Supervisor: Docent Mikael Ersson, Materialens processteknologi
The aim of this thesis is to investigate some aspects of improvements with respect to the energy consumption and raw material selection as well as the understanding of the influence of uncertainties on the performance in electric arc furnace (EAF) plants. The effect of electromagnetic stirring on the scrap melting and post combustion capacity are investigated in two EAFs by using computation fluid dynamic (CFD) models. The results showed that electromagnetic stirring can contribute to a better heat transfer rate at the melt – scrap interface. The Grashof and Nusselt numbers for both electromagnetic stirring and natural convection were estimated, as well as compared to the data from previous studies. Also, the results of the post-combustion in the duct system were used to predict the concentration of uncombusted CO at the possible position to install an off – gas analysis equipment. Also, modeling of the post-combustion in the whole furnace showed that the post-combustion can be improved by increasing the flow rate of the secondary oxygen in a virtual lance burner (VLB) under the meltdown and refining periods of the process. In order to investigate the influence of additions of raw materials on energy, melt composition and slag properties, a static mass and energy balance model is developed. The distribution ratios for metallic elements and dust parameters are calibrated by using process data from an EAF. The model is then applied to investigate the effect of hot briquetted iron (HBI) additions in that particular EAF. The results showed that these additions resulted in an increased electricity consumption and slag amount. The model is then applied to predict how it is possible to adjust the amount of slag formers to reach a desired MgO saturation level. In addition, a statistical model is developed which simulate the melt composition by applying uncertainties in scrap composition, scrap weighing and element distribution factors. The model can estimate the mean and standard deviations in the element concentration of scraps. The results of the model application in an EAF showed that the simulated melt chemical composition is in good agreement with the measured one, when the estimated values for scraps are applied as data in the model.