Abstarct: Semiautogenous (SAG) mills are the main part of mineral processing plants. Any variation in these mills affect on the processing circuit directly. Reducing these variations and therefore better controlling the processing system is the aim of SAG mill modeling. SAG mill modeling has been doing on various approaches. One of the most important features of these mills is the huge power draw which is about 5-10 MW/h. This includes the highest cost in the whole mineral processing unit, therefore predicting the mill power draw in grinding technology is very profitable. Numerous models have been introduced in batch or pilot scale yet, but there has not introduced any model for the industry scale. One of the main parameters for the industry scale model is the operational time that has not been included in previous model. Thus introducing a novel model for predicting SAG mill power draw baxsed on the operational time was the aim of this thesis. Liner wear is one of the most important parameters which effect the mill power draw variations. In this research a new kinetics model was introduced for liner wear prediction and then including it in power draw model. Accordingly a dynamic model was introduced for mill power draw prediction. The proposed model was validated with various methods such as Monte Carlo Simulation, Variance Propagation Law and Neural Network Modeling with Sarcheshmeh copper complex data. All analysis results confirmed the model validation. The new modified model predicted the mill power draw at 6000 kW that was equal to real operational values with only 1% variance but previous models predicted the mill power at 9400 kW that is much far from the real values. This research showed that the SAG mill liner wear is follow by zero order kinetics model with high precision (R2= 0.997). Using this model, the liner thickness prediction at any given time was possible. On the other hand the liner change out time was estimated. Liner weight wear rate for the Sarcheshmeh copper complex SAG mill was estimated 25 kg/h and for the liner thickness was about 0.017 mm/h. The wear rate constant in the kinetics model was calculated 10-4 h-1. In this thesis a new approach was also presented for the liner wear by using Grey Theory. The prediction error for liner wear was about 1.7% that showed the grey theory was also a proper method for predicting the liner change out time. Applying the grey theory, the useful life of Sarcheshmeh SAG liner was estimated 5400 hours which was almost equal with real conditions. Monte Carlo simulation results showed that mill power draw could be controlled by controlling just two operational parameters such as fixed and free end trunnion pressures.