In Silico CFD Investigation of the Granulation Hydrodynamics in Rotating Drum: Process Sensitivity to the Operating Parameters and Drag Models

Abstract

Computational fluid dynamics (CFD) have been extensively used to simulate the hydrodynamics of multiphase flows (MPFs) in rotating machinery. In the presence of a granular dense phase, the Kinetic Theory of Granular Flow (KTGF) is usually coupled to Eulerian multi-fluid models to obtain tractable computational fluid models. In the present work, the hydrodynamic behavior of a three dimensional, industrial scale, and rotating drum granulator with gas–solid flows is assessed using the Eulerian–Eulerian approach coupled with the k-ε standard turbulence model. A Eulerian–Eulerian Two-Fluid Model (TFM) is used with the KTGF model for the granular phase. The sensitivities to different operating parameters, including the rotational speed (8, 16, and 24 rpm), inclination degree (3.57∘, 5.57∘, and 7.57∘), and degree of filling (20%, 30%, and 40%) are studied. Moreover, the impact of the drag model on the simulation accuracy is investigated. The flow behavior, regime transitions, and particle distribution are numerically evaluated, while varying the operating conditions and the drag models. The rotational speed and filling degree appear to have greater influences on the granulation effectiveness than on the inclination degree. Three drag models are retained in our analysis. Both the Gidaspow and Wen and Yu models successfully predict the two-phase flow in comparison to the Syamlal and O’Brien model, which seems to underestimate the hydrodynamics of the flow in both its axial and radial distributions (a fill level less than 35%). The methodology followed in the current work lays the first stone for the optimization of the phosphates fertilizer wet-granulation process within an industrial installation.