Digital twin with automatic disturbance detection for real-time optimization of a semi-autogenous grinding (SAG) mill

TL;DR

This paper presents a digital twin for a SAG mill that integrates fuzzy logic, state-space modeling, and neural networks to enable real-time disturbance detection and supervision, aiming to optimize mill operation.

Contribution

The work introduces a novel digital twin architecture combining multiple control modules for SAG mills, validated with real operational data, and capable of real-time disturbance detection.

Findings

Digital twin predicts SAG mill behavior within 2.5 minutes.

The system effectively detects disturbances requiring retraining.

Validation shows promising supervision capabilities for SAG mill control.

Abstract

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.