Predicting the flowability of powder mixtures from their single components properties through the multi-component population-dependent granular bond number; extension to ground powder mixtures

Abstract

The granular Bond number, defined as the ratio between interparticle attractive forces and particle's weight, can be computed to predict the flow behavior of powders. Previous studies used this dimensionless number to predict the flowability of various pharmaceutical or ceramic powders, exhibiting polydispersed particle size distributions. In this paper, we employ a multi-component population-dependent granular Bond number in order to apply this model to powder mixtures. Some binary and ternary mixtures are prepared using two different techniques: a Turbula® mixer and a ball mill. The flowability predictions appear to be in very good agreement with the empirical measurements, carried out with a powder shear tester. However, the model parameters seem to be slightly different between milled and raw powders. The model discussed in this paper allows a prediction of the flowability of powder mixtures according to their composition and serve as a guide for product formulation and equipment design.

Investigating grinding mechanisms and scaling criteria in a ball mill by dimensional analysis

“…2 show that the particles shape does not change drastically before and after grinding. This was already evidenced in a previous paper where the authors shown that the particle size is actually the most important parameter governing the flowability of this specific powder along the grinding process [24].…”

A study of particle adhesion for cohesive powders using a novel mechanical surface energy tester

“…Two trends for the Bond numbers are shown in the figure, which indicates that the surface energies of the particles cause the difference rather than the domain for the geometry of the powders. Therefore, a Bond number can represent particle adhesion at a specific size, which will be helpful for assessing powder cohesiveness at a bulk level and is also applied by other researchers [22,23]. The results in Fig.…”