An IoT-enabled dispatch framework for dynamic ore grade lending in open-pit mines: a simulation-based performance analysis

Abstract In open-pit mines, traditional truck dispatching systems typically make it hard to balance the demand for regular ore grade blending with the need for operating efficiency. Some people have suggested that intelligent, IoT-enabled systems could be a solution, but there is not always a thorough, controlled evaluation of how well they work compared to traditional baselines. This study fills in the gaps by creating a discrete-event simulation framework that lets you compare a traditional, proximity-based dispatching strategy with a new IoT-enabled method in a quantitative way. The suggested algorithm uses a closed-loop feedback system that works in real time to make judgments about how to blend ore dynamically, always bringing the stockpile back to the goal grade. We tested the two systems in the same way and looked at important performance indicators like grade consistency, manufacturing throughput, and equipment use. The simulation results show that the IoT-enabled system is much better since it can keep the target ore grade with great consistency and stability. These metallurgical improvements were accomplished with almost no effect on total production throughput (< 0.3% difference), and they made the workload of the excavator much more even, which means that operations are more sustainable. This study gives strong, quantitative proof that a smart, grade-aware dispatching system can improve both metallurgical quality and operational efficiency at the same time. The simulation framework shown here is a useful model for reducing risk and measuring the effects of IoT and AI technologies. This will help Mining 4.0 operations become more productive and sustainable.