Autogenous (AG) milling is utilised around the world for rst stage particle size reduction. The system exhibits highly non-linear behaviour in addition to being subject to unmeasured variability associated with most ore bodies. Anglo American Platinum aimed at improving online optimisation of the circuit by implementing industrial model predictive control to reduce system variability and continuously drive towards the optimal operating point within system constraints. A dimensional analysis of the circuit was conducted to explain the relationships between the various milling parameters discussed in the literature survey. The measured variables used in the analysis satis ed Buckingham's theorem, indicating that a complete subset of dimensionless groups were present and suitably able to describe process movement. These relationships were used as a reference point in determining the dynamic step response models between these variables necessary for model based control. The industrial dynamic matrix controller commissioned on the AG mill resulted in a 66 % reduction in power and a 40 % reduction in load. These are the main controlled variables of the mill. The controller also managed to reduce its objective function, e ective power utilisation, by 11 %. This stability improvement enabled a test campaign where the mill was controlled at various operating regions in order to establish the conditions conducive to the nest product size at a given mill feed rate. Moving the mill's operating region from the benchmarked plant to this optimal grind environment (at benchmarked variability) provided an estimated potential recovery increase of 0.27 % (absolute) due to better precious metal liberation. Stabilising the mill at this point with the model predictive controller resulted in a further 0.04 % potential recovery increase (absolute). The 0.31 % potential recovery increase is estimated at a monetary value of $93.1 million per annum. Copyright
Dissertation (MEng)--University of Pretoria, 2013.