Application of CFD in vacuum dezincing process

Most lead bullion is refined by pyrometallurgical methods - this involves a serics of processes that remove the antimony (softening) silver (Parkes process), zinc (vacuum dezincing) and if need be, bismuth (Betterton-Kroll process). The first step, softening, removes the antimony, arsenic and tin by air oxidation in a furnace or by the Harris process. Next, in the Parkes process, zinc is added to the melt to remove the silver and gold. Insoluble zinc, silver and gold compounds are skimmed off from the melt surface. Excess zinc added during desilvering is removed from lead bullion using one of ghree methods: * Vacuum dezincing; * Chlorine dezincing; or * Harris dezincing. The present study concentrates on the Vacuum dezincing process for lead refining. The main aims of the research are to develop mathematical model(s), using Computational Fluid Dyanmics (CFD) a Surface Averaged Model (SAM), to predict the process behaviour under various operating conditions, thus providing detailed information of the process - insight into its reaction to changes of key operating parameters. Finally, the model will be used to optimise the process in terms of initial feed concentration, temperature, vacuum height cooling rate, etc.

Numerical simulation of vacuum dezincing of lead alloy

Removing zinc by distillation can leave the lead bullion virtually free of zinc and also produces pure zinc crystals. Batch distillation is considered in a hemispherical kettle with water-cooled lid, under high vacuum (50 Pa or less). Sufficient zinc concentration at the evaporating surface is achieved by means of a mechanical stirrer. The numerical model is based on the multiphysics simulation package PHYSICA. The fluid flow module of the code is used to simulate the action of the stirring impeller and to determine the temperature and concentration fields throughout the liquid volume including the evaporating surface. The rate of zinc evaporation and condensation is then modelled using Langmuir’s equations. Diffusion of the zinc vapour through the residual air in the vacuum gap is also taken into account. Computed results show that the mixing is sufficient and the rate-limiting step of the process is the surface evaporation driven by the difference of the equilibrium vapour pressure and the actual partial pressure of zinc vapour. However, at higher zinc concentrations, the heat transfer through the growing zinc crystal crust towards the cold steel lid may become the limiting factor because the crystallization front may reach the melting point. The computational model can be very useful in optimising the process within its safe limits.

Multiscale modelling of dendrite growth during vacuum arc remelting

Vacuum Arc Remelting (VAR) is the accepted method for producing homogeneous, fine microstructures that are free of inclusions required for rotating grade applications. However, as ingot sizes are increasing INCONEL 718 becomes increasingly susceptible to defects such as freckles, tree rings, and white spots increases for large diameter billets. Therefore, predictive models of these defects are required to allow optimization of process parameters. In this paper, a multiscale and multi-physics model is presented to predict the development of microstructures in the VAR ingot during solidification. At the microscale, a combined stochastic nucleation approach and finite difference solution of the solute diffusion is applied in the semi-solid zone of the VAR ingot. The micromodel is coupled with a solution of the macroscale heat transfer, fluid flow and electromagnetism in the VAR process through the temperature, pressure and fluid flow fields. The main objective of this study is to achieve a better understanding of the formation of the defects in VAR by quantifying the influence of VAR processing parameters on grain nucleation and dendrite growth. In particular, the effect of different ingot growth velocities on the microstructure formation was investigated. It was found that reducing the velocity produces significantly more coarse grains.

Vacuum arc remelting time dependent modelling

Vacuum arc remelting (VAR) aims at production of high quality, segregation-free alloys. The quality of the produced ingots depends on the operating conditions which could be monitored and analyzed using numerical modelling. The remelting process uniformity is controlled by critical medium scale time variations of the order 1-100 s, which are physically initiated by the droplet detachment and the large scale arc motion at the top of liquid pool [1,2]. The newly developed numerical modelling tools are addressing the 3-dimensional magnetohydrodynamic and thermal behaviour in the liquid zone and the adjacent ingot, electrode and crucible.

Multiscale modeling of the onset of freckle formation during vacuum arc remelting

A multiscale model for the Vacuum Arc Remelting process (VAR) was developed to simulate dendritic microstructures during solidification and investigate the onset of freckle formation. On the macroscale, a 3D multi-physics model of VAR was used to study complex physical phenomena, including liquid metal flow with turbulence, heat transfer, and magnetohydrodynamics. The results showed that unsteady fluid flow in the liquid pool caused significant thermal perturbation at the solidification front. These results were coupled into a micromodel to simulate dendritic growth controlled by solute diffusion, including local remelting. The changes in Rayleigh number as the microstructure remelts was quantified to provide an indicator of when fluid flow channels (i.e. freckles) will initiate in the mushy zone. By examining the simulated microstructures, it was found that the Rayleigh number increased more than 300 times during remelting, which suggests that thermal perturbation could be responsible for the onset of freckle formation.

Vacuum arc remelting time dependent modelling

Newly developed numerical modelling tools are described, which address the 3-dimensional (3D) time-dependent magnetohydrodynamic and thermal behaviour in the liquid pool zone in the adjacent ingot, electrode and crucible. The melting electrode film flow and the droplet detachment initiation are simulated separately by an axisymmetric transient model.