Statistical Characteristics of Geometry, Density and Porosity of Individual Ore Particles: A Case Study

Abstract

This study aims to develop a methodology to describe and predict the statistical characteristics of individual ore particles in terms of length, width, height, volume, mass, area, circularity, aspect ratio, density, and porosity. The mean value, standard deviation, and appropriate distribution function were calculated or identified for each data set of a given particle property in a given size fraction. It was found that the mean value and the standard deviation of the same particle property can either be predicted from particle size or be approximated by a constant. The best-fit distribution of each kind of particle property was identified by the Anderson–Darling test using Minitab software. Generally, the data sets with the same particle property but different size fractions and ore types follow the same distribution. A methodology was developed to predict the distribution of individual particle properties in a given size fraction by particle size, and the fitting quality is good in most cases. The statistical characteristics of individual ore particles can improve the precise processing of ore feed in concentrators, the preparation of feed samples for lab-scale testing, the calibration of image analysis of ore particle size distribution, etc.