4.6 Article

The influence of cohesion on polyhedral shapes during mixing in a drum

Journal

CHEMICAL ENGINEERING SCIENCE
Volume 270, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2023.118499

Keywords

Mixing; Polyhedra; DEM; GPU

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The blending of granular materials with different morphologies in industrial processes is unpredictable. Discrete element method simulations using polyhedral particles were used to study the effect of cohesion and particle shape in a rotating drum. Results showed that symmetric polyhedral shapes had the best mixing, while irregular shapes had the worst. Mixing improved with increasing cohesive particle size. Equipment geometry and rotation speed also significantly affected mixing.
Numerous industrial processes require the blending of granular materials that differ in morphology for which the resulting mixing cannot be predicted from bulk tests. Discrete element method simulations are an attractive approach to understanding mixing at the particle level within processing equipment. However, due to computational cost, particle shape is typically approximated as spherical despite most materials being non-spherical. In this paper, the combined effect of cohesion and particle shape using polyhedral particles is studied in a rotating drum. It was found that repose angle calibrated spheres were unable to reproduce the same percolation behaviour as polyhedra, leading to significantly different results with a better match in the case of highly cohesive paste like materials that diminish the particle scale effects. Symmetric polyhedral shapes exhibited the best mixing, while irregular shapes had the worst mixing. Mixing improves with increasing cohesive particle size due to the ability to percolate the finer non-cohesive material. Finally, equipment geometry and rotation speed had a significant effect on mixing, such that making conclusions on the general mixing of materials without the actual mechan-ical actions it is subjected to should be done with caution. (c) 2023 Elsevier Ltd. All rights reserved.

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