Presentation to The Mill Optimisation Summit 2011

Presented by:Paul WilsonTechnology Manager Calibre Automation, Communications & Technology Group

Optimising SAG mill throughput:A case study in tuning

Two SAG mills

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• 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

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The mills

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The mills were often unstable, seen as oscillations in the feedrate trend graphs

Unstable behaviour

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Loss of production

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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

Natural instability

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Poor tuning causes natural instability

Operator-caused instability

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Poor operator skills also forces instability

The control system

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Highest levelMinnovex expert system control

Optional top level controlConstraint control

Mid-levelClosed loop control

Bottom levelDelta-V distributed control system

The Minnovex expert system

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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

Performance comparison

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Expert system control is far better than poor operator control

Why mills go unstable (#1)

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Mills stall (bog or centrifuge). The behaviour at maximum throughput is highly non-linear

Why mills go unstable (#2)

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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

Mill load (level) is the integral of the nett feedrate

level = ∫ (Qin – Qout).dt

This induces a -90o phase shift in the transfer function which leads to reduced stability

Why mills go unstable (#2)

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Why mills go unstable (#2)

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The integration causes a phase shift

Simulated integral response

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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)

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The result of operator impatience

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Mill under tuned expert system control

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Section A to B is the maximum speed of recovery to prevent stalling the mill again

Expert system recovers from a motor overheat event

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The result

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• In 2004 / 2005 mill production rose from850,000 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

Extra energy used

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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

How was it done?

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• 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

How was it done?

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Block diagram model of mill behaviour

How was it done?

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Transfer function of mill load to ore feedrate

Additional development

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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.

Questions

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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.