Data-Driven Dynamic Simulations of Gold Extraction Which Incorporate Head Grade Distribution Statistics

Abstract

The Alhué mining district, Chile, is an example of a high-grade Au-Ag-Zn(-Pb) deposit with mineralized veins that contain variable amounts of copper sulfides, which are detrimental to the cyanidation process. Similar deposits can be found in the central zone of Chile, with polymetallic veins (Au, Ag, Cu, Pb, and Zn) that are related to subvolcanic intrusive events, the development of collapse calderas, and extensive hydrothermal alteration, such as Bronces de Petorca, the Chancón mining district and Cerro Cantillana; areas of the world with similar formations include the western United States and the Henan Province in central China, for example. Mineralogical variation can be managed within the metallurgical process by alternating its operational modes. The decision to switch between modes is governed by current and forecasted stockpile levels feeding into the process, according to a discrete rate simulation (DRS) framework that has now been developed to incorporate head grade data for gold. Customized simulations that incorporate probability distribution models using head grade have now been developed, following a statistical analysis based on data from the Alhué district. This study applies data-driven simulation modeling to represent standardized operational modes and their impact on the operational performance of gold extraction.