The semi-autogenous grinding (SAG) mill is a primary grinder used in the mineral processing industry and utilizes the grinding media (steel balls) and rock ore to grind the particles in rocks. An event of a “trip on overload” happens if there is a situation where the total weight/motor power of the mill goes above the safe limits set by the electro-mechanical system of that machine; this leads to automatic shut-down of the milling process. Trips are disruptive to production processes and reduce mineral recovery; therefore, a systematic troubleshooting approach will help restore the process to normal state.
When the mill trips due to the overload repeatedly, then the first step in diagnosing this problem is to check if this problem exists physically, or it is just an erroneous trip due to inaccurate instrument measurements. This is done by checking load cells, bearing pressure sensor calibration, and whether the drives give any erroneous readings. After proving that the problem exists, then this becomes a problem of process control.
Troubleshooting sag mills must therefore focus on evaluating the composition of the fed ore and feed rate. The sag mill process is very sensitive to fast changing ore hardness or particle sizes. Introduction of competent ore into a mill significantly decreases the breakage rate, and unless the feed tonnages are reduced accordingly, an accumulation of material will build up rapidly leading to overload volumetrically and of power (Hermosilla et al., 2024). This should prompt operators to audit the preceding ore feed, reduce feed rate as well as water feeding for proper slurry rheology.
In case the feed is kept more or less consistent, then focus should lie on analyzing mill internal filling and grinding media charges. The mill filling with charge defines the energy requirement, and if not maintained at its optimal setting point, causes heavy losses in efficiency (le Roux & Craig, 2019). Extended grind curves help identify the balance of internal ball charge with rock charge without creating any bottleneck to breaking. Moreover, a physical examination of the discharge grates as well as pulp lifters is recommended since pegged grates may prevent discharging slurry causing weight overload.
As a solution for avoiding trip due to overload, the troubleshooting technique changes from its reactive mode to a proactively managed one of the whole system. In modern comminution systems, model predictive control is used for managing the complicated nonlinear autogenous grinding that happens in SAG mills. For example, using physics-driven neural network techniques, we can make prediction about overload before reaching critical value that starts trips (Hermosilla et al., 2024). By ensuring steady speed of mill and controlling feed rate using prediction of operating parameters, optimal working regime of mill is achieved.
In conclusion, troubleshooting in SAG mills prone to overload conditions should adopt a very systematic process in order to solve the problem effectively. The right steps to be followed involve starting with checking the mechanical and instrument problems, then conducting an assessment of the feed ore competence, analyzing the mill filling process and applying the sophisticated predictive control. Following such a step-by-step process helps prevent overload issues and ensure the optimum efficiency of the mineral processing circuits.
References
Hermosilla, R., Valle, C., Allende, H., Aguilar, C., & Lucic, E. (2024). SAG’s overload forecasting using a CNN physical informed approach. Applied Sciences, 14(24), 11686. https://doi.org/10.3390/app142411686
le Roux, J. D., & Craig, I. K. (2019). Plant-wide control framework for a grinding mill circuit. Industrial & Engineering Chemistry Research, 58(26), 11585–11600. https://doi.org/10.1021/acs.iecr.8b06031
Powell, M., & Mainza, A. (2006). Extended grinding curves are essential to the comparison of milling performance. Minerals Engineering, 19(15), 1487–1494. https://doi.org/10.1016/j.mineng.2006.08.004
