Modeling of geometallurgical variables is becoming increasingly important for improved management of mineral resources. Mineral processing circuits are complex and depend on the interaction of a large number of properties of the ore feed. Plant performance variables of interest can include metallurgical recovery of the metals or elements being mined; prediction of concentrate grades; acid consumption if applicable; comminution variables such as drop weight index, sag power index, and bond mill work index, which help determine the economically optimal grind size and forecast throughput.

Normally there are an insufficient number of pilot plant trials to consider direct three-dimensional spatial modeling for the entire deposit, which leads to the need for using other related variables to predict plant performance, such as the more extensively head grades, mineral associations, grain sizes, and mineralogy variables. Geostatistical techniques lend themselves to this task, providing not only estimates but also range of probable values, i.e., assessing the variability of geometallurgical variables across the deposit. The spatial models produced are suitable for mine and plant optimization, improving decision making processes and thus decreasing risk and increasing recovery by representing in the mine schedule the ore variability. This should be done at all project stages to evaluate processing options and optimize operational performance.