OPTIMISATION OF A SAG MILL THROUGH TRAJECTORY AND POWER MODELLING. Paul Toor

Transcription

1 OPTIMISATION OF A SAG MILL THROUGH TRAJECTORY AND POWER MODELLING Paul Toor

2 Presentation Summary - Problem Statement - Case Study - Trajectory Modelling - Power Modelling - Identifying Optimum Region of Operation - Results & Benefits - Summary & Conclusion 2

3 Problem Statement Mill performance is very sensitive to total filling. Powell, M.S., van der Westhuizen, A.P. & Mainza, A.N. (2009). Applying grindcurves to mill operation and optimisation. Minerals. Engineering,

4 Problem Statement However no mill filling percentage is reported to the control room, instead a calculated mill load is provided. 4

5 Problem Statement The reported mill load is a arbitrary measure and is comprised of liner weight, the charge weight plus an offset. Load Reading= Charge mass + Liner mass + Offset The inclusion of liner weight is particularly problematic as it is dynamic. 5

6 Problem Statement Toor, P., Franke, J., Powell, M.S., Perkins, T., (2011), Quantifying the Influence of Liner Shape on Mill Performance. Proceedings International autogenous and semiautogenous grinding technology 2011, Sep , Ed. Flintoff et al, Published CIM.. 6

7 Problem Statement Toor, P., Franke, J., Powell, M.S., Perkins, T., (2011), Quantifying the Influence of Liner Shape on Mill Performance. Proceedings International autogenous and semiautogenous grinding technology 2011, Sep , Ed. Flintoff et al, Published CIM.. 7

8 Problem Statement Toor, P., Franke, J., Powell, M.S., Perkins, T., (2011), Quantifying the Influence of Liner Shape on Mill Performance. Proceedings International autogenous and semiautogenous grinding technology 2011, Sep , Ed. Flintoff et al, Published CIM.. 8

9 Problem Statement Ball trajectory is another critical parameter. Overthrow causes liner damage and reduced efficiency. Underthrow causes damping of grinding action also leading to reduced efficiency. 9

10 Problem Statement The assessment of ball trajectory cannot be done in isolation as the toe of the charge needs to be determined to make any meaningful assessment 10

11 Proposed Methodology 12

12 Background Case Study SABC Fixed Speed F mm (Dependant on ore source). Ball Top Size 125 mm Very little autogenous grinding. (BAG Mill) 13

13 Trajectory Modelling 15

14 Trajectory Modelling 16

15 Trajectory Modelling Trajectories calculated at two week intervals. Outermost trajectory when liners new. Innermost trajectory when liners fully worn. 17

16 Trajectory Modelling 25 % Mill Filling required when liners are new Decrease in trajectory constant over time. Virtually impossible to overthrow media after 4 weeks of liner wear. Significant depletion in trajectory. Liner redesign recommend. 18

17 JKMRC Power Model A model which relates the two most critical parameters in SAG milling (Ball charge and Total Charge (%)) to a readily available output, Power (kw). 19

18 Power Curves 20

19 Power Curves 21

20 Power Curves 22

21 Power Curves 23

22 Power Curves 24

23 Power Curves Ball Charge Is Reasonably Estimated from Inspections and Mass Balancing 25

24 Power Curves Example: Power = 1400 kw Ball Charge = 15% Total Charge=21.5% 26

25 Power Curves: Determining Optimum Regions 27

26 Power Curves: Determining Optimum Regions Target Power <97% Or 1450 kw 28

27 Power Curves: Determining Optimum Regions Target Power >1300 kw=86.67% 29

28 Power Curves: Determining Optimum Regions Target Mill Filling < 25% 30

29 Power Curves: Determining Optimum Regions Target Mill Filling > 18% 31

30 Power Curves: Determining Optimum Regions Target Ball Filling < 17.5% 32

31 Power Curves: Determining Optimum Regions Target Ball Filling > 12.5% 33

32 Power Curves: Determining Optimum Regions 34

33 Real World Example Case 1 Tph:~220 Power:~ 1300 kw Ball Charge:-13.3% Mill Load:~ 60t Mill Filling=21% Rock Filling=7.7% Rock to Ball Ratio~1:2 Case 2 Tph:- 205 Power: kw Ball Charge:- 11 % Mill Load:~65 t Mill Filling=28 % Rock Filling=17% Rock to Ball Ratio~3:2 39

34 Real World Example 40

35 Real World Example 41

36 Real World Example Case 2 Tph:- 210 Power: Ball Charge:- 11 % Mill Load:~65 t Mill Filling=28 % Rock Filling=17% Rock to Ball Ratio~3:2 Actions Required Reduction in mill load set point 15t Addition of 2 tonnes of media 42

37 Power Curves: Major Benefits Allows the calculation and tracking of critical parameters - Mill Filling - Ball Filling - Rock Filling Data can be coupled with Trajectory models for improved insight Not affected by liner mass Use of actual mill parameters rather than inference from mill load and or power. 43

38 Power Curves: Major Benefits Power curves/modelling utilised on a daily basis on site to determine ball charging regime and load set point. Improved mill performance - Mill operating more consistently due to tight regulation of rock and ball filling. - Faster ramp ups post relines Methodology can be utilised for both fix and variable speed mills. 44

39 Conclusion Load cells are adequate in informing the DCS, metallurgist and operators the rate of change of mill filling. However they do not provide information on the total mill filling or the composition of rock and ball. Mill filling is a critical parameter, with its optimisation the mill may be optimised. Power Modelling allows for an accurate method to determine mill filling in real time with no or few mill stoppages. 45

40 Conclusion Having developed power models it is reasonably straight forward to determine optimum operating regions. Furthermore ensuring the mill remains in the optimum region becomes significantly easier with knowledge of Total, Ball and Rock Filling. The end result being increased mill performance and improved liner management. 46

41 company/metso metsoworld metsogroup metsoworld metsogroup