1. Presentation to The Mill Optimisation Summit 2011 Presented by: Paul Wilson Technology Manager Calibre Automation, Communications & Technology Group Optimising SAG mill throughput: A case study in tuning

2. Two SAG mills 1

3. Porgera Mine, PNG 4.5 Megawatt, variable speed drive About 500 tonnes per hour per mill Highly variable lithology with grinding factors from 6 to 18 kilowatt hours per tonne The mills 2

4. 3

5. The mills were often unstable, seen as oscillations in the feedrate trend graphs Unstable behaviour 4

6. Loss of production 7 As much as 15% on bad days Up to 380 ounces of gold per day on bad days At $425 US per ounce = $160,000 per day You could hire a very good plant operator for that kind of money

7. Natural instability 8 Poor tuning causes natural instability

8. Operator-caused instability 8 Poor operator skills also forces instability

9. The control system 9 Highest level Minnovex expert system control Optional top level control Constraint control Mid-level Closed loop control Bottom level Delta-V distributed control system

10. The Minnovex expert system 9 A fuzzy rules based artificial intelligence system Running on a G2 expert system shell Running on a Windows NT PC platform Takes data from, & feeds setpoints to, the loop controllers on the Delta-V

11. Performance comparison 9 Expert system control is far better than poor operator control

12. Why mills go unstable (#1) 9 Mills stall (bog or centrifuge). The behaviour at maximum throughput is highly non-linear

13. Why mills go unstable (#2) 9 Dynamic behaviour of a mill is type 1 Control engineers recognise that type 1 systems are more likely to be unstable than type 0 systems Caused by the inherent integration in the mill transfer function

14. Mill load (level) is the integral of the nett feed rate level = ∫ (Q in – Q out ).dt This induces a -90 o phase shift in the transfer function which leads to reduced stability Why mills go unstable (#2) 10

15. Why mills go unstable (#2) 9 The integration causes a phase shift

16. Simulated integral response 11

17. 1.When a mill stalls it stops working. 2.The mill fills with unground material. 3.It takes time to grind out the rock and get the outflow going again So: the control system / plant operator must be: PATIENT Why mills go unstable (#3) 10

18. The result of operator impatience 13

19. Mill under tuned expert system control 13

20. Section A to B is the maximum speed of recovery to prevent stalling the mill again Expert system recovers from a motor overheat event 13

21. The result 30 In 2004 / 2005 mill production rose from 850,000 ounces to 1,000,000 ounces At $425 US per ounce that was $63.75 million US increase per year Not possible without increasing SAG mill throughput

22. Extra energy used 7 The difference in energy usage between the unstable zone and the stable zone is: the unstable zone averages 25% more motor energy per tonne of product than the stable zone and produces 15% less product Which adds 10% to the energy costs for the remainder of the processing plant

23. How was it done? 30 Develop a mathematical model Use trend plots and tests to characterise the mill (find the characteristics of mill behaviour) Estimate fastest possible recovery times on the worst-case ores Retune the expert system rules for robust, always-stable behaviour

24. How was it done? 30 Block diagram model of mill behaviour

25. How was it done? 30 Transfer function of mill load to ore feedrate

26. Additional development 30 The decision to secondary crush the harder ores. A secondary crusher was installed. With a bit of clever mathematics we were able to estimate SAG mill grinding factor at the primary crusher. We used this to feed some of the hard ore (GF > 10 kWhr per tonne) through the secondary crusher thus increasing SAG mill throughput on the harder ore.

27. Questions 31 Your questions are welcome Plant characterisation & transfer function development is a complex process. I am happy to discuss some of the methods afterwards with anyone interested.