Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Abstract

In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate the system model using the maximum likelihood approach. Additionally, we propose using a new measurement that has not been considered in other modeling approaches. The benefits of our proposal are illustrated via numerical simulations. The results demonstrate that incorporating this new measurement within the framework of maximum likelihood estimation improves the accuracy of estimating the unknown parameters.

Keywords

• SAG mills
• system identification
• em algorithm
• maximum likelihood
• filtering
• modeling