Abstract
In the present study, the thickening process of tailings slurry in a gravity thickener is modelled using Computational Fluid Dynamics (CFD), with a focus on the effects of particle size and feed velocity. A Two-Fluid Model (TFM) is employed to describe the particle-fluid and particle-particle interactions while turbulence is described by the ω-based Reynolds Stress Model (RSM). The validity of the model is demonstrated by close agreement between experimental measurement and simulation predictions of total solid mass of underflow, and particle cumulative mass distribution as a function of particle size in the underflow. The feedwell performance is evaluated in terms of kinetic energy dissipation and mixing performance. Particle size has a significant influence on the distribution of solid concentration. The increase of particle size leads to a higher critical settling height. For a given underflow discharging rate, solid concentration in the underflow increases with feed velocity only in a certain range, whereas increases continuously in the overflow. Large particle size corresponds to a higher critical feed velocity. Modelling the granular flow with a wide particle size distribution in a thickener helps explore the particle dynamic behaviors, thereby improving the thickening process.
Published Version
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