Prediction of granule packing and flow behavior based on particle size and shape analysis

Packing behavior and flowability of particulate material have long been acknowledged as important parameters for the processing of pharmaceutical materials. When properly understood, these properties can provide insight into weight uniformity, tableting performance and process design. The aim of this study was to measure particle size and shape distributions of different granular intermediates with a dynamic particle size image analyzer, and then use these distributions to predict packing efficiency and different metrics of flowability by employing partial least squares (PLS) modeling. From measurements of size and shape, a model was constructed that allowed for the prediction of flowability indices, bulk and tap densities with a high degree of accuracy for the granular materials used. In this study the use of size and shape distributions in the construction of a model providing both accurate flowability indices and bulk and tap density estimates has been demonstrated for example granular materials in which cohesive forces did not dominate powder behavior. It is believed that the continued application of the outlined experimental design would eventually lead to the fundamental understanding of how size and shape characteristics of materials influence particle behavior including downstream processibility.