Dynamic Modeling & Simulation of a Sag Mill for Characterization of Mill Charge

"Modeling and Simulation of SAG Mill has been widely used in design and optimization of mill performance in terms of its power draw, processing capacity and product size distribution. However, these models are solved under steady approximation and do not provide any information on mill charge distribution in real time. This paper attempts to characterize mill contents by solving Whiten’s first order contents based model in Matlab/Simulink environment. The parameters in the model such as breakage rate (S), discharge rate (D) and appearance function (A) were estimated utilizing process and design data collected from a gold mine operating in Alaska coupled with nonlinear parameter estimation scheme. This model is then utilized to predict dynamic response of other key operational variables such as mill power, bearing pressure, charge level and product size distribution. The transient response of mill behavior with respect to changes in feed size distribution, tonnage and mill feed water is also presented. This dynamic simulation approach can be used for practicing different control strategy and training purposes. The observed response of mentioned variables was validated utilizing dynamic response data from the plant to encompass operational characteristics of SAG Mill behavior. Keywords: SAG Mill, Dynamic Modeling, Simulation, Nonlinear Parameter Estimation. INTRODUCTION Grinding in mineral industry is one the most important steps in the process of size reduction and plays a crucial role in determining the overall economics of the milling plant. Much of the efforts focused on improving the process efficiency using modeling and simulation have been practiced by researcher and engineers. However, up till now these models were simulated under steady state approximation because of their complex nature and difficulty in programming them in a dynamic framework. Steady state simulations have been successfully utilized in circuit designing, capacity planning and pre-flowsheet optimization. The maturity in steady state models is because of its applicability and regular use in steady state simulation packages such as JKSimMet, Limn, ModSim, etc. With the advent of equipment such as SAG and AG mill has been a recent shift in the simulating these models using dynamic approach. Because of the economic benefits of these devices and ability to act both as a primary grinding device and as secondary or tertiary crushing device they have gained a large popularity and are now universal to any milling plant. However, these device poses a real challenge to engineers and researchers in terms of operation and control. The major reason attributed to problems faced is because the ore constitutes the major portion of the charge in these devices when compared to ball and rod mill. This rock charge is subjected to disturbance such as changes in feed size distribution and ore hardness which results in unstable operation and frequent mill stoppages. One way to prevent this is to measure and control these external variables, however, lack of available instrumentation and unavailability of sensors to measure these variables has foiled the understanding of complete process occurring inside the mill thereby, inhibiting development of proper control system."