Real-Time Estimation of SAG Mill Charge Characteristics for Process Optimization

Real-Time Estimation of SAG Mill Charge Characteristics for Process Optimization

*P. Toor1, W. Valery1, S. Morrell2, K. Duffy1

  1. Hatch 61 Petrie Terrace. Brisbane, QLD, 4000, Australia
  2. SMC Testing, Kenmore Hills, QLD, 4069, Australia

(*Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.)
*This paper was presented at SAG Conference held 24-28 September 2023 in Vancouver, Canada. To view the full paper select download file below. 

Control of the total charge and the ball charge volume is vital to the optimum performance of semi-autogenous grinding (SAG) millsto maximise throughput and energy efficiency. However, neither of these parameters can be directly measured online. Where they are installed, load cells measure the mill and charge total mass. Where load cells are not installed, the total mass can be inferred from bearing pressure. The charge mass is then estimated by subtracting the mass of the mill shell plus lifters and liners; but, the liner mass changes with wear and mill relines. However, when combined with an accurate SAG mill power-draw model, total charge volume and ball load can be estimated with reasonable accuracy.

The Morrell C-model is generally regarded as one of the most accurate tumbling mill power-draw models and is ideally suited for this application of estimating fill levels of the SAG mill in real time. This paper presents three methods using the Morrell C-model to estimate total and ball charge volumes for SAG mills, depending on the data and measurements available. The methodologies outlined all allow analysis of real-time data and large data sets spanning months and years of operation, thus facilitating identification of optimum operating conditions and aiding in early detection, trouble-shooting, and rectifying poor mill performance. Several case studies are provided to demonstrate the application and accuracy of the results derived from the proposed methodologies.

Keywords: SAG mill, power modelling, comminution, optimization