Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Mining trends in the gold sector indicate a growing imbalance global supply and demand chains, especially light of accelerated efforts towards industrial electrification automation. As such, it is important that research development continue to focus on processing options for more complex refractory ores. Unlike conventional (i.e., free-milling) ore feeds, not amenable standard cyanidation, requires additional pretreatment prior leaching recovery. With recent technological advancements, such as sensor-based sorting, there opportunity advance smaller untapped resources with marginal economics, particularly those proximity infrastructure within major districts. However, will be critical necessary tools are developed capture potential system-wide effects caused by varied feeds improve related decision-making processes earlier value chain. Discrete event simulation (DES) powerful computational technique can used monitor interactions between parameters response random natural variations; approach thus suitable modelling mining systems deal significant geological uncertainty. This work implements an integrated artificial neural network (ANN) DES framework regional coordination preconcentrated ores processed at centralized plant. Sample calculations presented based generated dataset reflective sediment-hosted systems.