Dynamic Optimisation of Real-time Ore Flow from Uncertain Resource to Run-of-mine Stockpiles and Processing Plant
Traditional production scheduling models primarily focus on generating static in-situ production strategies, often overlooking the dynamic and uncertain nature of mining environments and the integration of post-extraction ore flow into mill feed via Run-of-Mine (RoM) stockpiles. Developing a framework that effectively integrates real-time ore-tracking data with ore flow scheduling across the mining value chain would substantially improve the adaptability and profitability of production strategies under dynamic operational conditions.
This project aims to integrate real-time ore tagging technologies with mathematical programming approaches to dynamically optimise ore flow, from the uncertain, in-situ resource to downstream RoM stockpiles and ultimately to the mill feed. Sensor outputs from real-time, on-line ore tracking systems are utilised as inputs for mathematical models designed to optimise ore flow strategies across the mining value chain in real time.
Additional focus areas include the development of operational constraints specifically tailored to the characteristics of the Sublevel Caving (SLC) method, the implementation of real-time grade updating strategies within RoM stockpiles to align with integrated ore-tracking systems, and the optimisation of ore flow under contamination control requirements.
The ultimate research goal is to establish an innovative framework capable of dynamically optimising ore flow throughout the entire mining value chain, including in-situ production scheduling, stockpiling, blending, and mill feeding. The framework will be adaptable and applicable to ore flow optimisation in a broad range of underground mining operations.
