5
Once the optimal mill filling has been determined, it
is important to maintain this parameter as the mill liners
wear. A SAG mill load model can be developed to deter-
mine the optimal mill load setpoint for the operators based
on the cumulative tonnes processed on the installed mill
liners.
How to maintain optimal mill filling:
• Step 1: Mill load vs mill filling
Since mill filling is not a typical parameter available
to mill operators, a relationship with mill load needs
to be developed. Crash stop and grind-out measure-
ments with liner wear data can be used to determine
mill filling at an average liner thickness. A relation-
ship is then developed between mill load and mill
filling as a function of average liner thickness.
• Step 2: Develop a mill load model
To achieve the required mill filling, the mill load
model can then be plotted against the cumulative
tonnes processed on the current liner set. This guides
the operator (or control system) to achieve target
mill filling.
An example of a SAG mill load model is shown
in Figure 6.
• Step 3: Continuous validation
Routine crash stops, and grind outs should be con-
ducted in conjunction with liner wear measurements
to continuously validate and update the SAG mill
load model.
Since it is not always practical to crash-stop the mill
due to lost production or safety concerns, a practical solu-
tion is to install a mill-filling measurement device (Kaltech
Sentinel [14]). If the model is well calibrated, a deviation
from the calibration curve can indicate that the media level
is off target.
Process Control
Challenge—Recommending the Correct Control
Solution
The primary control objective is maintaining a stable mill
load and maximizing throughput for variable ore compe-
tency, feed size distribution, and operating conditions [1].
Causes of poor controller setup include:
• Over-reactive control response, either too large, too
frequent of changes, exacerbating mill load fluctua-
tions because of poorly tuned control system or an
overly zealous control room operator
• Poor understanding of response times and the lag
between the control system change and the response
in mill performance resulting in cyclic control.
• Sensors and instrumentation that is not functioning
or poorly calibrated.
• Lack of operator confidence in the control system or
inadequate operator training in the appropriate con-
trol response.
An example of over-reactive control response to chang-
ing mill load and a properly tuned response is shown in
Figure 7. Figure 7a shows a highly fluctuating mill load
because of large changes in the SAG mill feed rate setpoint.
Figure 5. SAG mill performance curves [4] Figure 6. SAG mill load model
Figure 7. Over-reactive feed rate control (a) tuned feed rate
control (b) [11]
Once the optimal mill filling has been determined, it
is important to maintain this parameter as the mill liners
wear. A SAG mill load model can be developed to deter-
mine the optimal mill load setpoint for the operators based
on the cumulative tonnes processed on the installed mill
liners.
How to maintain optimal mill filling:
• Step 1: Mill load vs mill filling
Since mill filling is not a typical parameter available
to mill operators, a relationship with mill load needs
to be developed. Crash stop and grind-out measure-
ments with liner wear data can be used to determine
mill filling at an average liner thickness. A relation-
ship is then developed between mill load and mill
filling as a function of average liner thickness.
• Step 2: Develop a mill load model
To achieve the required mill filling, the mill load
model can then be plotted against the cumulative
tonnes processed on the current liner set. This guides
the operator (or control system) to achieve target
mill filling.
An example of a SAG mill load model is shown
in Figure 6.
• Step 3: Continuous validation
Routine crash stops, and grind outs should be con-
ducted in conjunction with liner wear measurements
to continuously validate and update the SAG mill
load model.
Since it is not always practical to crash-stop the mill
due to lost production or safety concerns, a practical solu-
tion is to install a mill-filling measurement device (Kaltech
Sentinel [14]). If the model is well calibrated, a deviation
from the calibration curve can indicate that the media level
is off target.
Process Control
Challenge—Recommending the Correct Control
Solution
The primary control objective is maintaining a stable mill
load and maximizing throughput for variable ore compe-
tency, feed size distribution, and operating conditions [1].
Causes of poor controller setup include:
• Over-reactive control response, either too large, too
frequent of changes, exacerbating mill load fluctua-
tions because of poorly tuned control system or an
overly zealous control room operator
• Poor understanding of response times and the lag
between the control system change and the response
in mill performance resulting in cyclic control.
• Sensors and instrumentation that is not functioning
or poorly calibrated.
• Lack of operator confidence in the control system or
inadequate operator training in the appropriate con-
trol response.
An example of over-reactive control response to chang-
ing mill load and a properly tuned response is shown in
Figure 7. Figure 7a shows a highly fluctuating mill load
because of large changes in the SAG mill feed rate setpoint.
Figure 5. SAG mill performance curves [4] Figure 6. SAG mill load model
Figure 7. Over-reactive feed rate control (a) tuned feed rate
control (b) [11]