This article is a sequence of another one and displays results in the selective comminution of binary mineral mixtures. The selectivity of dolomite and quartz grinding rates under different proportions was studied. The called global grain contrast indicator (IGCG) was used to improve the analyses of the behavior of these minerals under comminution, using theoretical size distribution curves, fitted to the Hill's sigmoidal function. The results have suggested the optimum grinding times, within the tested range. The results presented here have validated the development of a neural network-based predictive model for grinding system simulation (a multilayer perceptron using backward propagation with moment), which will be approached in another article.
Selective comminution; binary mix; grinding; artificial neural network
