Abstract

In the area of automation and process control, a pressing issue is the uncertainty in the dynamics of the SAG mill ore feeding process and the complexity in identifying the optimal feeding configuration to maintain a stable charge consistently over time. In this paper, we develop a Probabilistic Graphical Model using a Bayesian network that may enhance the decisionmaking process for operating a semi-autogenous grinding (SAG) mill. The Bayesian network is initially constructed based on prior knowledge. Conditional probabilities are calibrated using a subset for training and validated using a test subset. The obtained results demonstrate that the model achieves high accuracy in determining the fresh mineral feed rate and the power consumed by the mill. Thisshowcases the models effectiveness in capturing the primary operational standards.