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SAG Mill Control at Northparkes

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SAG Mill Control at Northparkes Mines (Not So Hard After All)

A. J. Thornton, Principal Process Control Engineer1

Tom Pethybridge, Production Superintendent – OPD2

Tom Rivett, Process Control Engineer2 Richard Dunn, Metallurgical Superintendent2

1. MIPAC Pty Ltd, PO Box 308, Albion, QLD. 4010 Australia 2. Northparkes Mines Limited, PO Box 995, Parkes, NSW. 2870 Australia

ABSTRACT

The difficulty of stabilizing SAG mill load limits the effectiveness of feedback control approaches. Control is particularly difficult during periods when the feed ore is either unusually hard or fine. There is also a strong tendency to lose control during transient periods when feed ore changes. The control problem is also compounded by long process deadtimes, recycle streams, noisy measurements, non-linear dynamics and poor feed control.

To overcome these problems rule-based expert systems have been applied, with notable success (in some cases). But is SAG mill control the exclusive domain of rule-based approaches or can more conventional control techniques be successfully applied? This paper explores this question and describes a successful application (95 % operator utilisation) of conventional control techniques to two SAG milling circuits at Northparkes Copper Mines. The strategy employs both feedback and feedforward controllers to account for the major plant dynamics. The control strategy has been implemented in the plant PLC (programmable logic controller) using standard blocks and algorithms. This has contributed significantly to acceptance by instrument maintenance personnel and ensured that ‘local’ support for the system is a practical reality. Good quality field instrumentation and base level control was found to be pivotal to the success of this project. A number of other lessons have been learnt and documented, some of which can be applied to any control project in mineral processing.



INTRODUCTION This paper describes the successful implementation of SAG mill control at Northparkes Mines. Control of SAG and AG mills has been considered by many to be the exclusive domain of rule-based expert systems; however this project has proven that more conventional control approaches can be successful. It should also be noted that this control was implemented in the existing plant control system (PLC). This has resulted in a robust system which has been well accepted by plant operational and maintenance staff. The control strategies described in this paper have been applied successfully to ML03 (SAG Mill 2) for around 2 years. During March 2005 an identical control strategy was implemented on ML01 (SAG Mill 1). Control strategy utilisation has been reported at 95%.

PROCESS DESCRIPTION AND CONTROL OBJECTIVES Northparkes Mines produces copper concentrate predominantly from a porphyry deposit mined using a block cave. Comminution begins with an underground crusher feeding two SAG mills: ML01 (plan feedrate 240tph) and ML03 (plan feedrate 423 tph ). Ore is stacked, using fixed chutes, on two stock piles and fed to each SAG mill using 2-3 (of four) vibrating feeders. Each SAG mill is run in closed circuit with a gyratory pebble crusher. Secondary and tertiary grinding is provided by ball mills on each module. The objectives of SAG mill automation are to reduce the number of manual control actions required, extend the life of mill internals, and provide a steady feed to the down stream processes. This equates to the stabilisation of SAG mill load and density.

WHAT ARE ‘CONVENTIONAL’ CONTROL TECHNIQUES? Producing a definition of conventional control is as difficult as defining advanced control. The following definition may be useful in understanding the central theme of this paper: Conventional control includes the use of PID, feedforward control, cascade control and closely related techniques. Included in this list should be techniques like deadtime compensation, loop decoupling, gain scheduling and signal linearisation, provided that these can be applied using the standard block set available on a modern PLC or DCS system. Therefore a Smith Predictor, 3-element boiler drum level controller, or a heat exchanger temperature controller (heat balance model) would be included in the definition of conventional control. Items not included in the definition of conventional would be any techniques arising from artificial intelligence research such as neural networks, fuzzy logic or expert systems, not withstanding the wide use of some of these approaches. Model-based control or adaptive/self-tuning control which requires additional computing hardware and/or special software ‘modules’ over and above the standard block set are also not, for the purposes of this paper, considered conventional control elements. Examples of controllers requiring additional hardware or software include self-tuning PID and, Model Predictive Control (eg Dynamic Matrix Control).



WHY IS SAG MILL CONTROL DIFFICULT? Controlling charge load and density in SAG mills is complicated by a number of well known difficulties. Unfortunately what is less well known is that conventional control techniques can be used to overcome most of these difficulties. Feed Variations Changes in both the size distribution (f80) and hardness (work index) of the feed are the most important disturbances affecting SAG mill behaviour. Size distribution can be measured using image analysis techniques and used as a feedforward signal for improving load control. Unfortunately, there is no direct method of measuring ore hardness online. Ore hardness, however, can be inferred by continuously fitting online dynamic circuit models to the actual plant (eg Mill Auto-Pilot).

Overloading or Underloading The consequences of either overloading or underloading a SAG mill are very significant. An overloaded mill will often require reducing or stopping the feed, while underloading can increase liner wear and damage. In more extreme cases both conditions can result in tripping or stopping the SAG mill. Feed System Control Crushed ore is fed onto the feed conveyor using one or more variable speed feeders. The feeders are located under the ore stockpile or bins. By running different feeder combinations the size distribution of the SAG mill feed can be regulated (to some extent). The feedrate isusually automatically controlled by adjusting the speed of one or more of the feeders. It is quite common to have one feeder being adjusted automatically to control feedrate to the mill and another feeder being adjusted manually to regulate the size distribution. Reported problems from feed system control include:

• Ore blockages above the feeders. • Management and control of multiple feeders. • Uneven loading of ore on the feed conveyor, resulting in a variable weightometer

signal. • Noisy weightometer signal. • Variable feed size distribution and/or hardness.

Load Measurement In this paper load is defined as the total weight of ore, water and grinding balls within the mill. Load can be measured directly by mounting the mill on load cells, or indirectly by inferring load from bearing oil pressure. Both approaches have advantages but also some short comings. Bearing oil pressure suffers from inaccuracy. Load cells are expensive to retrofit to existing mills and, if they stop working, are difficult to repair. At Northparkes load is inferred by measuring the sound level (in the correct frequency range) of the tumbling mill charge. It has been found that sound level around 82 to 83 dB’s produces optimal results. Experienced mill operators have long known that a ‘“quiet” mill is a mill heading for an overload condition.



Recycle Streams The most common recycle stream in SAG milling are rocks (often called scats or oversize) which have reported to the oversize of the discharge screen. It is common practice is to crush this material before returning it to the feed conveyor. Scats recycle rate (tonnes per hour) must be measured and accounted for in both the mill load and density control loops. If the scats recycle is not included in these loops stability problems will be experienced, particularly during transient conditions. Process Deadtime In most SAG circuits there are deadtimes ranging from seconds to minutes between the ore feeders and the feed weightometer, and between the weightometer and the mill. There are also similar deadtimes caused by the recycle conveyor. All these deadtimes can be compensated using reasonably standard control techniques available on most PLC and DCS systems. Measurement Noise The response of the load loop would be improved by the use of derivative control. Derivative control would minimise overshoot and oscillatory behaviour, both of which can occur in SAG mills. Unfortunately derivative control and signal noise don’t mix. The signal can be filtered to reduce its noise content, however if too much filtering is introduced any benefit from derivative control will be negated.

NORTHPARKES SAG MILL CONTROL The control strategy developed for the SAG milling circuits at Northparkes Mines was implemented on a Schneider Quantum PLC, which is programmed using Concept Software. The SAG mill control was developed using the Function Block Language which is available within the Concept software package. The control strategies have been constructed using standard function blocks (eg PID, Lag, Lead/Lag, Deadtime etc.). Although advanced logic control functions are available within Concept (eg fuzzy logic), these were not used. The control strategy at Northparkes comprises three sub systems: multiple feeder control, feedrate control (includes density control) and load / power control. Multiple Feeder Control (MFC) The multiple feeder control (MFC) is shown in Figure 1. This is a single input multiple output control structure which is largely unknown in mineral processing. MFC is often used to control multiple compressors or boilers feeding into a common header where the objective is to control header pressure1.



Figure 1. Multiple feeder control used at Northparkes Mines.

The MFC system received a cascade setpoint signal from the feedrate controller. Note that the total feedrate to the mill is controlled (new feed plus scats recycle). A ratio and/or bias parameter can be applied to each feeder signal to account for individual feeder behaviour or to manually manipulate size distribution to the SAG mill. The MFC is tuned for a very fast response, typically with an integral time from 1 to 10 seconds. The operator has the flexibility to adjust feeders manually with the proviso that at least one feeder is enabled for automatic operation. If a feed chute becomes blocked, then the automatic feeder(s) will respond in a few seconds to maintain the feedrate to the SAG mill at setpoint. A similar situation occurs if a feeder trips or is stopped for some reason. The MFC provides the following benefits:

• Feeder trips, stops and blockages are automatically compensated for. • Any combination of feeders can be enabled for automatic control, without having to

retune the feedrate control loop. • Feeders can be individually ratioed or biased. • If the ratio or bias of an automatic or manual feeder is changed the automatic feeders

compensate. • If the speed of a manual feeder is adjusted the automatic feeders compensate.

To demonstrate the MFC system a simulation of a typical four feeder system is shown in Figure 2. Point A shows where a feeder not included in the MFC system is started and the speed manually adjusted to 20% and point B is where the feeder is stopped. Points C and D show the same sequence, however in this case the feeder is included in the MFC system.



Figure 2. Simulation of 4 feeder MFC system. Points A and B show feeder operation without

MFC and C and D with MFC.

The major benefits to Northparkes from the implementation of the MFC system have been:

• More stable feed to the SAG mill during periods of blockage prone ore. The flexibility to operate the four feeders in any combination of automatic or manual.

Feedrate and Density Control The next level of control implemented at Northparkes was feedrate and mill density control. These loops are depicted in Figure 4 below. For feedrate control a model-based PI controller (PIτd) was employed. This is one of the simplest forms of model-based control and is very effective in controlling noisy deadtime dominant processes. These characteristics make the PIτd controller very suitable for control of feedrate to SAG mills. Having a simple structure also allows the controller to be implemented in relatively simple PLC’s. The PIτd controller structure and its tuning have previously been described by Shinskey1 and its application to an industrial process has been described by Thornton2. The block diagram of the PIτd controller is shown in Figure 3. If the PID controller has an external integral feedback connection then it can be converted to the PIτd form by inserting a deadtime block. Unfortunately the PID block in the Northparkes PLC did not have this desirable feature. However, the PIτd was readily constructed from the basic blocks.

Figure 3. Block diagram of the PIτd controller. Deadtime compensation is achieved by inserting a deadtime block in the integral feedback loop.

Time (seconds)



The basic tuning procedure is to match the deadtime in the controller to the process deadtime. Noise filtering can be achieved at either the controlled or manipulated variable. In this application filtering the controlled variable was selected. Filtering reduces the sensitivity of the loop to errors in the estimation of dead time. The integral time is matched to the process time constant plus the noise filter time constant and the proportional gain is matched to the inverse of the process gain.Small adjustments to the proportional and integral constants following step changes to the setpoint were employed to fine-tune the controller. The time required for fine-tuning was found to be minimal (approximately 30 minutes) and in the authors opinion the PIτd controller was more straight forward to tune than the Smith Predictor algorithm. The usual approach for density control is to ratio the SAG mill feedwater to the new feedrate. This approach does not take into account variations in the amount of recycled scats material. Figure 4 shows the density control system used at Northparkes. In this system the total feed to the mill is controlled (ie new feed plus scats recycle), by adjusting the ore feeders (via the MFC) and the feedwater flow setpoint is ratioed to the total mill feed. Compensating for process deadtime (see Figure 4) was found to be important to achieve stable SAG mill density. Deadtime (1) accounts for the delay between the scats weightometer and the feed conveyor and deadtime (2) accounts for delay between the feed conveyor weightometer and the mill feed chute.

Figure 4. SAG mill feedrate and density controller.

Load / Power Control At Northparkes SAG load is inferred from measurement of the sound level of the tumbling charge. This is achieved by an industrial microphone located under the mill. The microphone system is equipped with a band pass filter allowing more important frequencies (as would be released by a ball hitting liner etc.) to be transmitted. The microphone system was installed and used successfully for manual control before implementation of automatic mill load control – probably increasing operator acceptance of the new controller.

The load / power control system is shown in Figure 5. This is a constraint control system where the lowest feedrate setpoint from either the load or power controller is selected.



Predominately mill load is controlled, and the power controller acts as an upper constraint on the mill motor power draw. The power controller setpoint is usually set to 2650 kW. The power controller acts as a soft constraint and avoids power excursions above its setpoint. Operating staff at Northparkes have identified a load setpoint of 82-83 dB as optimal in terms of maximising feedrate and minimising mill liner wear. The PIτd controller is also used for load and power control. The integral feedback on both controllers is connected to the process variable of the feedrate controller. This allows the inactive controller (mostly the power controller) to track the active controller whilst avoiding integral windup.

Figure 5. SAG Load and Power control (also shows feedrate and density control).

RESULTS Figure 6 shows actual plant data from Northparkes (SAG Mill ML01) over a 12 hour period. The data shows a revealing sequence of events labelled A to E. Event A The SAG mill circuit is shutdown. Previously the control system was in automatic. Event B The SAG mill circuit is started; however the operator has not put the control system back into automatic. Between B and C the mill first starts to underload (mill gets noisy) and the operator increases the feedrate which caused the mill to enter an overloaded state (mill becoming quieter). A mill overload and trip is a likely possibility. The operator is taking no action to reduce the feedrate and bring the mill back into a safer operating regime. Event C At point C the control system was then switched to automatic and the feedrate is reduced avoiding an overload and returned the mill load back to setpoint (with minimal overshoot).



Figure 6. Plant data from Northparkes (12 hour sequence, SAG Mill ML01).

Event D The recycle crusher is by-passed and the control system maintains circuit stability with the mill load close to its setpoint. At this stage the power is operating below its setpoint and the power controller is no longer active. Event E



The recycle crusher is put back on-line. The stability of the circuit is maintained. The SAG power starts to exhibit at decreasing trend while the feedrate trends upwards. Operator Utilisation The recorded utilisation of the SAG mill control system is 95% which, for an advanced SAG mill control system is indicative of success. Summary The SAG mill control system is able to successfully handle a wide range of plant disturbances and keep the mill operating at the optimal load. Acceptance by operating personnel has been high (95% utilisation). An identical control system has recently (March 2005) been commissioned on the second SAG mill. A high degree of operator acceptance has been achieved. Increased stability in copper flotation has been attributed to improved SAG mill control at Northparkes. To quote one Northparkes metallurgist: “I have a nice data set showing a brief shut down that we had recently. The operators forgot to put the controller back into "mipac" and the power got quite high. The mill was put into "mipac" and immediately dropped feed -- avoiding an overload. The mill then stabilized and was hit by another disturbance when our oversize crusher tripped out. Again the controller dealt with the disturbance but slightly overshot when the crusher was turned back on 2 hrs later”

LESSONS LEARNT

1. Although the SAG mill control strategies were designed by external consultants, local site support for tuning, training and general support duties is imperative to success.

2. Support in terms of tuning, training, coaching and modification is on-going – it can never stop completely. Those companies which stop support of advanced control strategies never reap the full benefits from the initial effort and capital invested.

3. To successfully implement SAG mill control a sound working knowledge of both the process and feedback/feedforword control techniques are required. This knowledge does not usually reside in a single individual.

4. Once it has been established that the control strategies are robust and fit for duty, they must be completely integrated in to the operating procedures of the plant. In particular this will involve the modification of operator training manuals and procedures.

5. To paraphrase an oft’ used phrase – “the system must have one or preferably, more than one, site champion”.

6. Good quality plant instrumentation and base level control (eg feedrate control) is critical to the successful application of more advanced control strategies, be they expert systems, model-based approaches, or those based around more conventional techniques (such as those described in this paper).

7. Modern PLC and DCS systems can be used to successfully control SAG milling circuits.

8. Successful implementation of advanced SAG mill control, particularly in an existing plant with established operating procedures is not a short-term undertaking. At Northparkes approximately two years elapsed from the initial audit and design phases to a control strategy acceptance by operating personnel. Transferring the control system to the second SAG mill only took a few weeks.

CONCLUSIONS It has been established in a real plant environment that relatively conventional control strategies can be used to control SAG mill load (and power) and density and these strategies



can be implemented in a standard industrial PLC -- without the requirement for additional computer resources. Load and power control is now a well established operating tool at Northparkes Mines. Operator utilisation of 95% is being achieved. Several innovative modifications to the standard load and density control approaches employed on SAG mills have been made, including using the model-based PIτd controller for feedrate and power/load control, and employing a multiple output ore feeder controller (ie MFC) more commonly used on compressor and boiler applications. This control technology has been transferred to the second SAG mill at Northparkes. Several important lessons have been learnt during this project (see above). In the authors’ opinion the most important is:

Good quality plant instrumentation and base level control (eg feedrate control) is critical to the successful application of more advanced control strategies, be they expert systems, model-based approaches, or those based around more conventional techniques (such as those described in this paper). ACKNOWLEDGEMENTS The authors’ wish to thank their employers MIPAC Pty. Ltd. and Northparkes Mines Ltd. for providing the encouragement, resources and opportunity to publish this work.



REFERENCES

1. Shinskey, F. G., Process Control Systems, 4th ed., McGraw-Hill, 1996, New York, pp. 189-192.

2. Thornton, A. J., How Simple Control Can Save You Money, MIPAC Process Plant

Optimisation Seminar, March 2002, Brisbane, Australia.

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