Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 1-DEM Based Methods

Computational methods using mechanistic modeling with a specific application in the area of continuous blending are presented. These methods complement experimental designs and aim to reduce the amount of time, effort, and material required to characterize a device or a process. The discrete element method is applied to the specific case of continuous mixing using two approaches. The first approach models an entire blender and studies the impact of changing impeller speed on residence time distribution (RTD), number of blade passes experienced by the powder, and mean centered variance of the particle residence time. The mean centered variance and the number of blade passes exhibit a maximum with increasing impeller speed, indicating optimal mixing behavior at an intermediate speed range. The second DEM approach utilizes a periodic slice of the full blender in order to explore in detail the effect of speed, fill level, and cohesion on mixing performance. The results show that the transverse mixing rates were generally higher than the corresponding axial mixing rates. Transverse diffusion was highest for large impeller speeds at larger fills, and axial diffusion was highest for large impeller speeds at smaller fills. Variations in particle-particle cohesion within the investigated range do not affect diffusion values significantly. The axial diffusion coefficient can be reasonably predicted from the residence time distribution using the 1D advection-diffusion model.