Effect of particle size and shape on liquid-solid fluidization in a HydroFloat cell

The separation of coarse particles by flotation can be maximized by the support of fluidized bed hydrodynamics. Here, we investigated the effect of particle size and shape on the liquid-solid fluidization in a HydroFloat cell that was developed for the special separation process using CFD results and experimental data. Specifically, a 3D CFD model was developed to study the liquid and aspherical quartz particle flow behaviors in a fluidized bed in the cell. The Eulerian-Eulerian formulation with the RNG k-epsilon turbulence model was applied for the liquid phase, while the kinetic theory of granular flow was utilized for the particle phase. The Gidaspow with Haider and Levenspiel equations were used for the drag force coupling between the liquid and the aspherical particles. The CFD results agreed with the experimental data for the bed voidage and pressure drop. The CFD model was further applied to investigate the effect of particle size (d(S)) and particle shape factor (psi) on the bed expansion, pressure drop, and voidage of the fluidization for Re (particle Reynolds number) < 11.6. The Richardson-Zaki equation was modified to predict the voidage, within 12% of relative errors. The prediction was further improved by our semi-empirical correlation that reduced the errors to 8%. The particle size and shape were also found to significantly affect the time-averaged radial distribution of the axial velocity and volume fraction of the particles, granular temperature, dissipation rate, and viscosity of turbulence. The outcomes of this paper are useful for optimizing the operation of the HydroFloat cell. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study investigated the effect of particle size and shape on liquid-solid fluidization in a HydroFloat cell using CFD results and experimental data. The modeling approach successfully predicted and validated factors affecting bed expansion, pressure drop, and voidage, leading to improvements in prediction accuracy. These findings are valuable for optimizing the operation of the HydroFloat cell.