Abstract

Fine material migration occurs in cave mining during ore extraction and is often associated with ore dilution, which, in the worst case, can cause drawpoint closure. Additionally, the migration of fine material can imply safety issues, such as inrush of fines and, when mixed with water, mudrushes. Nowadays, there are empirical and numerical tools used to study fine material migration; however, in cave mining a large number of rock fragments have to be simulated during flow based on a production plan, and simulations are even more complex when fine rock fragment is involved. Here, a new fine material migration logic based on cellular automata is presented to improve the calculation of this phenomenon according to the literature. The key variable used is the size ratio between the rock fragments of the broken column. The mean size, d(50), is used to represent a characteristic rock size and calculate the coarse and fine fragment ratio. The logic presented is calibrated using experimental data from a physical model and is then compared at mine scale. The results show that use of d(50) to classify the size fragments during gravity flow allows experimental conditions to be replicated. Moreover, if the particle size distribution of coarse material is known, it can be used to adjust a calibration parameter of the migration logic in the flow simulator. We conclude that incorporating the d(50) to characterize the granular material in gravity flow logic using cellular automata provides an accurate and quick fine material migration estimation.