Abstract
Simulation platforms are applied to design and test alternative approaches to integrated management, including scheduling and planning over different timeframes. For mining and metallurgical systems, these platforms can be initially developed using mass balancing in conjunction with discrete event simulation (DES), and then detailed in later engineering phases by hierarchically embedding models and submodels to represent critical risks and opportunities. A promising application of DES is for implementing machine-learning enabled control strategies within mining and metallurgical systems, first by simulating the as-is system and then quantifying the effect of enhanced approaches. The kinetics of individual unit operations can be detailed within the mineral value chain, by embedding time-adaptive finite difference (TAFD) into DES; the resulting DES-TAFD hybrids are computationally efficient and can simulate hundreds or thousands of operational days in a matter of minutes. DES platforms support modern approaches to integrated management by simulating and refining alternate control strategies, and can enable system-wide improvements that balance production metrics with environmental considerations. In particular, the current presentation discussed experiences in embedding geological variation into DES of mining systems, and thermochemical models into DES of extractive metallurgical plants. Well-defined operational modes provide integrated responses to changing risk profiles, leading to better outcomes.