Abstract
Bladed mixers are widely used in the industry for granular mixing. In the past decades, the mixing of particles in bladed mixers has been extensively investigated experimentally and numerically. Recently, GPU-based DEM has been employed to simulate various industrial-scale applications. This work aims to apply the GPU-based DEM to investigate the effect of rotation speed and mixer size on granular mixing in bladed mixers of different sizes. The simulation in a larger mixer revealed distinct particle flow patterns that well-describe the mixing mechanism which is difficult to observe in a smaller mixer. The Lacey’s mixing index curves revealed a delay in mixing as the mixer size increases. The mixing rate decreases as the mixer size increases and it can be improved with increasing rotation speed. The average particle velocity increase significantly with increasing rotation speed and mixer size.
Highlights
The bladed mixer is a typical mixer used in bulk chemicals processing, food, and pharmaceutical industries
Stewart et al [2] used the positron emission particle tracking (PEPT) experiment to study the motion of particles in a bladed mixer
We focus on the effect of rotation speed and mixer size on the flow and mixing of particles in bladed mixers
Summary
The bladed mixer is a typical mixer used in bulk chemicals processing, food, and pharmaceutical industries. The bladed mixer is used to homogenize granulate powders, and to enhance a chemical reaction [1]. Inadequate mixing in product manufacturing could cause product rejection due to poor product quality. The mixing process in a bladed mixer has been studied extensively for the past decades. Stewart et al [2] used the positron emission particle tracking (PEPT) experiment to study the motion of particles in a bladed mixer. They described that the particles moved upwards, forming a heap and eventually, moved over the blade. The PEPT experiment lacks dynamic information at the particle scale and can suffer from experimental errors due to low resolution and/or uncertainty in data interpretation [3]
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