A Mesoscale Endpoint Predictive Model of Ore Grinding Particle Size This study investigates the use of kinetic model to predict the particle size of ore grinding process. A new modeling framework is proposed where a discretized macroscale model and a mesoscale kinetic model are combined to predict the grinding product particle size. The key idea is to divide the mill into several connected virtual submills, and each submill is simulated using kinetic algorithm. This enables a distributed parameter model of the continuous grinding process where the dynamics of the axial distribution of material in the mill may be of interest. The proposed model is validated using experimental data showing satisfactory results.

Scheduling with fixed maintenance, shared resources and nonlinear feedrate constraints: A mine planning case study Given a short term mining plan, the task for an operational mine planner is to determine how the equipment in the mine should be used each day. That is, how crushers, loaders and trucks should be used to realise the short term plan. It is important to achieve both grade targets (by blending) and maximise the utilisation (i.e., throughput) of the mine. The resulting problem is a nonlinear scheduling problem with maintenance constraints, blending and shared resources. In this paper, we decompose this problem into two parts: the blending, and the utilisation problems. We then focus our attention on the utilisation problem. We examine how to model and solve it using alternative approaches: specifically, constraint programming, MIQP and MINLP. We provide a repair heuristic based on an outerapproximation, and empirically demonstrate its effectiveness for solving the realworld instances of operational mine planning obtained from our industry partner.