  This work describes the development and validation of a digital twin for a
semi-autogenous grinding (SAG) mill controlled by an expert system. The digital
twin consists of three modules emulating a closed-loop system: fuzzy logic for
the expert control, a state-space model for regulatory control, and a recurrent
neural network for the SAG mill process. The model was trained with 68 hours of
data and validated with 8 hours of test data. It predicts the mill's behavior
within a 2.5-minute horizon with a 30-second sampling time. The disturbance
detection evaluates the need for retraining, and the digital twin shows promise
for supervising the SAG mill with the expert control system. Future work will
focus on integrating this digital twin into real-time optimization strategies
with industrial validation.
