Modeling and energy-based model predictive control of high pressure grinding roll

Abstract

Even though semiautogenous grinding mills and ball mills are normally used in grinding processes, the industry is driven to decrease cost by increasing efficiencies and decreasing energy consumption. High Pressure Grinding Rolls (HPGR) are seen as an energy-efficient alternative but their developments in modeling and control have received relatively little attention. In this work a model and a control scheme for HPGR is presented that considers the total energy consumed as one of the main controlled variable. First, the model was generated by using literature-reported information of a specific manufacturer and lithology. The dynamic representation of the treatment capacity, product granulometric distribution (reported as 80% percentile), compression energy, and rolling energy were considered as the most important model output variables. Second, model validation was performed with considerable positive results (based on assessment of estimation errors). Finally, the model was used to generate a multiple input multiple output control scheme. As result, it was observed that the model had a correct representation of the phenomena involved and that the peripheral velocity and pressure used in the HPGR are useful manipulated variables to control the energy consumed by the equipment in an MPC scheme.