Geometallurgical Data Mining of Ore Processing Behavior and the Integrated Value Chain

Geometallurgical characteristics of an orebody strongly influence processing outcomes. Explicitly tracking and quantifying that impact is difficult. However, it is becoming increasingly feasible as modern information systems can facilitate in-depth analytical models. Machine learning algorithms can then examine relationships between the mineralogical qualities of the ore and the ensuing process performance. As a test case, we investigate connections between in situ ore and downstream processing outcomes for a large copper porphyry, using a combination of custom Python, discrete event simulation (DES), and data mining. The focus is on performance variations with pyrite content and swelling clay content.