Deoxidation of Molten Steel with Deoxidizer Containing Aluminum

Mathematical models have been developed to predict the removal of alumina inclusions from molten steel in a continuous casting tundish, including the effects of turbulent collisions, reoxidation, flotation, and removal on the inclusion size distribution. The trajectories of inclusion particles are tracked through the three-dimensional (3-D) flow distribution, which was calculated with the K-ε turbulence model and includes thermal buoyancy forces based on the coupled temperature distribution. The predicted distributions are most consistent with measurements if reoxidation is assumed to increase the number of small inclusions, collision agglomeration is accounted for, and inclusion removal rates are based on particle trajectories tracked through a non-isothermal 3-D flow pattern including Stokes flotation based on a cluster density of 5,000 kg/m 3 , and random motion due to turbulence. Steel samples should be taken from as deep as possible in the tundish near the outlet and several residence times after the ladle is opened, in order to best measure the Al 2 O 3 concentration entering the submerged entry nozzle to the mold. Inclusion removal rates vary greatly with size and with the presence of a protective slag cover to prevent reoxidation. The random motion of inclusions due to turbulence improves the relatively slow flotation of small inclusions to the top surface flux layer. However, it also promotes collisions, which slows down the relatively fast net removal rates of large inclusions. For the conditions modeled, the flow pattern reaches steady state soon after a new ladle opens, but the temperature and inclusion distributions continue to evolve even after 1.3 residence times. The removal of inclusions does not appear to depend on the tundish aspect ratio for the conditions and assumptions modeled. It is hoped that this work will inspire future measurements and the development of more comprehensive models of inclusion removal.These validated models should serve as powerful quantitative tools to predict and optimize inclusion removal during molten steel processing, leading to higher quality steel.

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“…Asano et al [12] measured the volume fraction of Al 2 O 3 in agglomerated inclusion clusters to be about 0.03. When β is set to 0.03 in Eq.…”

Section: B Inclusion Removal Model With Trajectory Calculationmentioning

confidence: 99%

Modeling of inclusion removal in a tundish

Miki¹,

Thomas

1999

Metall Mater Trans B

166

116

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“…These phenomena have been observed in Yin's experiment [34]. The collision among the particles usually results in aggregated loose clusters, so the apparent density is evidently low [35]. It should be noted that the predicted morphology of alumina cluster is different from that in reference [34].…”

Section: Discussionmentioning

confidence: 80%

A dynamic model of alumina inclusion collision growth in the continuous caster

Lei¹,

He²

2006

Journal of Non-Crystalline Solids

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“…However, the morphology of the clusters greatly affects their floatation behavior. [1][2][3] Although the morphology of those clusters have been observed by many researchers, [4][5][6][7][8][9][10][11] its effect on inclusion flotation is still not clear and has to be study in detail in order to improve the inclusion removal efficiency during the steel production.…”

Section: Introductionmentioning

confidence: 99%

Direct Numerical Simulation of Inclusion Cluster Floating Behavior in Molten Steel Using Lattice Boltzmann Method

Wang

Li³

et al. 2014

steel research int.

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A three‐dimensional fractal structure model was introduced to quantify the morphology of inclusion clusters, and the floating behavior of different inclusion clusters was simulated directly using lattice Boltzmann method to investigate the effect of morphology on the floating velocity in molten steel. It is found that the morphology, which can be described quantitatively by fractal dimension has a significant effect on the floating behavior of inclusions. The terminal floating velocity of inclusion clusters is smaller than that of spherical inclusions, which consist of same number of primary particles. The ratio between the floating velocity of inclusion clusters and that of spherical inclusions decreases with the increasing primary particle number. By introducing the fractal dimension, an equation for the floatation velocity of cluster‐shaped inclusions is derived from the simulation results.

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Deoxidation of Molten Steel with Deoxidizer Containing Aluminum

Abstract: The mechanism on the deoxidation of molten steel with deoxidizer containing aluminum was inve-(

Cited by 11 publications

References 1 publication

Modeling of inclusion removal in a tundish

Modeling of inclusion removal in a tundish

A dynamic model of alumina inclusion collision growth in the continuous caster

Direct Numerical Simulation of Inclusion Cluster Floating Behavior in Molten Steel Using Lattice Boltzmann Method

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