Abstract
Autogenous grinding is characterized by non-linearities, time-varying dynamics and a high level of uncertainties, conditions which usually originate from the variability of the ore feed characteristics (hardness and grade). These characteristics make this grinding operation particularly appealing for some type of knowledge-based control. This paper discusses the application of the dynamic matrix control algorithm to an autogenous grinding operation. This control algorithm is a long-range predictive control algorithm which has been successfully applied to other processes. The study was carried out using an empirical simulator calibrated with industrial data. The simulation results were compared to those obtained using PID and learning controllers. The ability of the dynamic matrix control to improve the efficiency of this complex and highly sensitive process is clearly demonstrated.
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