The viscosities of solid‐liquid mixtures were experimentally determined for silicon oil‐paraffin system at room temperature and solid‐liquid oxide mixture at steelmaking temperature. The use of oil‐paraffin systems was to confirm the results of high temperature measurements, the experimental conditions being very difficult to control. The silicon oil‐ paraffin mixtures behaved Newtonian until the particle fraction reached 0.15. At this fraction, the mixture started deviate from Newtonian flow; though some average values could still be collected with very high uncertainty. Liquid‐2CaO.SiO2 mixtures and liquid‐MgO mixtures were studied at steelmaking temperature with carefully prepared particle fractions and well controlled conditions. Liquid‐2CaO.SiO2 mixture behaved Newtonian even when the particle fraction reached 0.1. The results of both room temperature measurements and steelmaking temperature measurements were used to examine the applicability of the existing models. Einstein‐Roscoe equation was found to be the only model applicably for the systems studied. No modification of the model parameter was found necessary, though the particles were not spherical.
In the present study, the effect of gas flow rate on homogenization and inclusion removal in a gas stirred ladle was investigated. Both industrial trials and cold model experiments were conducted. CFD calculation was also carried out as an auxiliary tool. The mixing times predicted by CFD simulation agreed well with both the model experiments and industrial data. 99% mixing could be achieved in about 2–3 minutes. The increase of flow rate of inert gas would not improve the mixing substantially, while the mixing time decreased somewhat with the increase of gas flow rate. The water model study showed also that the gas flow rate had a negligible effect on the rate of inclusion removal. Both the experiments and CFD calculation strongly suggested that a low gas flow rate should be applied in the ladle treatment.
A two-dimensional non-steady state computational fluid dynamic model was employed to gain a basic understanding of the flow in the ingot during casting. The surface velocity was found to be affected by the inlet angle and was found to vary with the casting level. The information of surface velocity was related to the probability of the inclusion removal. Water model experiments were also conducted to help the understanding. The model calculation revealed the same trends as the results of water model experiments. Both computational fluid dynamic data and the physical modelling showed that the inlet angle of 5u currently used in the industry was a good alternative regarding inclusion removal. The present work suggests strongly that the melting shop should try to obtain a liquid or semiliquid film at as early stage as possible in ingot casting.
A new approach was made to model the dephosphorisation process in a 300 tons basic oxygen furnace converter with three argon gas inlets. The main feature of the new approach was to utilise the velocity vectors obtained by computational fluid dynamics (CFD) simulation in a standalone model. The CFD simulation was carried out using commercial software COMSOL Multiphysics. In the standalone model, the steel melt domain was sliced into 1000 cells. The calculated velocity vector in each cell was assumed constant. Based on the imported velocity vectors from the CFD calculation, the mass transfer of carbon and phosphorus was calculated by taking into account the slag-metal reactions. The mass exchange between slag and metal was considered to be dominated by the metal droplet formation due to the oxygen jet. The convergence of the model calculation and the promising comparison between the model prediction and the industrial data strongly suggested that the proposed approach would be a powerful tool in dynamic process control. As a preliminary step, the model only simulated the process after the formation of slagmetal-gas emulsion. Note that the present work is intended to establish a structure of the model. More precise descriptions of other process aspects need to be included before the model can be practically employed in a dynamic controlling system.
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