Inferential measurement of SAG mill parameters

Semi-autogenous (SAG) mill total load and ball load have a marked influence on mill performance. Direct measurement of these inventories is difficult, hence inferential measurement is an attractive option to open the way to their control. Advances have been made in this area based on combined state and parameter estimation formulations, though there is still scope for further soft sensor development. This work investigates the use of mill powerdraw and weight simulation models combined with the corresponding measured variable (to form residual equations) to obtain estimates of total volumetric load fraction, Jt, and ball charge volumetric fraction, Jb, by the application of a constrained nonlinear optimisation technique. The sensitivity of the estimates to model parameters and the uncertainty in the estimates are investigated. Results show that, although all estimates illustrated good agreement with nominal conditions, the estimates from the weight-based models contained the least uncertainty. The inclusion of a mill liner wear model in combined state and parameter estimation formulations is recommended due to the notable contribution of liner weight to the mill weight measured variable.