Prediction of the porosity of multi-component mixtures of cohesive and non-cohesive particles

This paper presents an experimental and theoretical study of the packing of mixtures of cohesive (fine) and non-cohesive (coarse) particles. The experimental results, produced by means of a standard funnel packing method with glass beads as experimental materials, are first used to depict the similarity between the packings of fine and coarse particles. On this basis, the so-called linear packing model is extended to estimate the porosity of mixtures of fine and coarse particles with a wide size range. Its interaction functions and equivalent packing size are determined empirically. The applicability of the resultant model is demonstrated by the good agreement between the predicted and measured results for typical packing systems, including particles with the Gaudin–Schuhmann distribution or with a mixture size distribution. Finally, the packing of particles with a lognormal distribution, involving both cohesive and non-cohesive particles, is investigated in detail. This example also demonstrates how the proposed model can be used to solve a packing problem.

Graphical abstractThis paper presents an experimental and theoretical study of the packing of mixtures of cohesive and non-cohesive particles. A mathematical model is formulated to estimate the porosity of such particle mixtures. Its applicability is then demonstrated by the good agreement between the predicted and measured results for typical packing systems. Finally, an example is provided to demonstrate how the model can be used to solve a packing problem.Figure optionsDownload full-size imageDownload high-quality image (63 K)Download as PowerPoint slideHighlights► Comprehensive study is carried out on the packing of cohesive and non-cohesive particles. ► Mathematical model is formulated to estimate the porosity of such particle mixtures. ► Applicability of the model has been tested for various representative packing systems. ► Example is provided to demonstrate how the model can solve a packing problem.