A particle scale mixing measurement method using a generalized nearest neighbor mixing index

The dependence of mixing measurement on the sampling method and scale is a major concern in the characterization of the state of homogeneity of solid particle mixtures. A novel "particle neighborhood" based mixing index, called the generalized nearest neighbor (GNN) mixing index, is developed to mea-sure solids mixing at the particle scale. GNN is a statistically robust, grid-independent index and provides reliable particle scale mixture homogeneity values. The GNN index has a further advantage that it can be readily adapted to mixtures of unequal proportions or mixtures containing more than two species. To test the GNN index, X-ray computed tomography (CT) images are obtained to noninvasively extract detailed particle distribution of binary mixtures consisting of different-density particles in the mixing vessel. CT images are acquired at different mixing stages to accurately describe mixture homogeneity evolution during the mixing process. Mixture homogeneity values are quantified using the novel GNN mixing index, and these values are compared with measurements obtained using different mixing indices, including a standard deviation-based mixing index and a grid-independent location-based mixing index. The GNN mixing index is found to be well-suited for reliable mixture homogeneity reporting at the par-ticle scale.(c) 2022 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.