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IMPLEMENTATION OF A SAG GRINDING EXPERT SYSTEM AT BARRICK NORTH MARA 19

IntroductionThe Barrick North Mara Mine and the Advanced SystemsGroup at SGS collaborated on the implementation of anexpert system for the control and optimization of a semiautogenous (SAG) mill. The primary objective was tomaintain an on spec and stable grinding product to leachingwhile maximizing throughput. This was achieved byoptimization of the mill power draw and load through themanipulation of appropriate variables. The SGS expertcontrol technology was chosen by Barrick North Mara dueto its proven success at achieving these objectives in avariety of different processes that were prone to a highdegree of variability1,2,4.

In many industries, control and optimization strategiesrely on the ability of mathematical models to reliablydescribe the dynamic response of a process. In mineralprocessing, using mathematical modelling for a SAG milldoes not provide a robust solution due to the large numberof unmeasured states that are required to properly model amill’s operation. Furthermore, the physical and chemicalprocesses involved in mineral processes are also notcompletely understood in all cases, adding to the difficultyin acquiring models suitable for use in advanced controlstrategies3.

When a process is complex and subject to unmeasuredstates, a rules based, fuzzy logic approach is a validalternative. Fuzzy logic controllers offer goodgeneralization, leading to robust solutions and are capableof the non linear response necessary to maintain SAG mill

stability. The fuzzy logic model consists of expert rules oractions associated to a mill’s (or a milling circuit)operational state. The model must contain all relevantprocess states to work properly. This approach does notexclude the use of mathematical models. In fact,mathematical models can be effectively deployed in a MEChybrid controller for further optimization.

For North Mara, the process of defining the model startswith a generic template of a fuzzy controller into which theexperience of over 40 previous SAG mill expert solutionshas been distilled. The next step is to work in cooperationwith site to customize the template to their needs.

The expert system for the North Mara implementationwas built using the MET expert console (MEC) toolkitwhich is based on Gensym’s G2 inference software. TheMEC toolkit provides a powerful platform forimplementing a fuzzy rules based system for the advancedcontrol of mineral processing operations and is adaptable tosuit the specific need of different processes. Its graphicalnature also eases the development and implementation ofcontrol logic, and helps in fault finding and analysis ofcontrol actions implemented by the expert system.

The expert system implementation at North Mara wascompleted using SGS’s standard methodologies, whichhave been refined over a number of years and have proventhemselves, over numerous site implementations, to beeffective at achieving the control objectives. Themethodology does not only strive to capture the mostappropriate control strategy for the site, but also attempts to

METZNER, G., CORNEJO, F., STEYN, J., WESTCOTT M., FESTA, A., BARNARD, E., and BRITS, C. Implementation of a SAG grinding expert systemat Barrick North Mara, Tanzania. World Gold Conference 2009, The Southern African Institute of Mining and Metallurgy, 2009.

Implementation of a SAG grinding expert system at BarrickNorth Mara, Tanzania

G. METZNER*, F. CORNEJO†, J. STEYN*, M. WESTCOTT*, A. FESTA†,E. BARNARD‡, and C. BRITS‡

*Advanced Systems Group, SGS Mineral Services, South Africa †Advanced Systems Group, SGS Mineral Services, Canada

‡Barrick, North Mara

The Barrick North Mara Mine and the Advanced Systems Group at SGS Minerals collaborated onthe implementation of a grinding expert system for the control and optimisation of a single semiautogenous (SAG) mill. Over the last 5 years, grinding expert control has gained wide acceptanceby mineral processors. In this paper, two key factors for success are highlighted: the adaptabilityof the software tools and the interaction with site personnel.

Created with SGS’s MET expert console (MEC) toolkit, and running on Gensym’s G2 real timeinference software, the North Mara expert logic has been set up to keep a steady and on specleaching feed slurry while striving to maximize throughput. Leach feed stability was achieved bystandardizing operating practices across shifts; a result of successful collaboration with sitepersonnel in developing a consistent control strategy. The strategy maintains the SAG load andpower draw in an optimised range by manipulating SAG feed rates and inlet water flow.

The project life-cycle was slightly longer than average due to site specific constraints. One ofthese was the high turnover rate of site’s workforce during the project, coupled with theavailability of key staff on site due to the fly in fly out work roster. These constraints called fordifferent, and innovative ways of project commissioning and hand-over. This paper describesdetails of the control solution, its implementation as well as results and conclusions.

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WORLD GOLD CONFERENCE 200920

ensure the long-term sustainability of the expert system. Itdoes this through a focussed process of participation,involvement and training of all relevant role players.

This paper will discuss the implementation of the expertcontrol system at the Barrick North Mara operation utilizingthe SGS MEC toolkit, and will include a discussion of themethodology followed to successfully complete theimplementation, followed by a discussion of the resultsachieved during and after the conclusion of the project.

Process descriptionThe North Mara milling circuit is comprised of a SAGmilling section as well as a secondary ball milling section.A schematic, indicating the entire milling circuit, as well asthe installed instrumentation and control loops, is shown inFigure 1.

ROM ore is tipped onto a stockpile and then reloaded intoa ROM bin by means of a front end loader. The ore is thendrawn from the ROM bin and fed onto a vibrating grizzlyscreen by means of an apron feeder. The undersize materialpasses through the grizzly onto a conveyor belt while theoversize material passes through a jaw crusher whichdischarges onto the same conveyor belt. The ore is then fedonto a banana screen via a two tier conveyor system. Theundersize material from the banana screen passes onto aconveyor belt which feeds the SAG mill feed stockpilewhile the oversize material is fed into a secondary crusher.The crushed ore from the secondary stockpile is tipped ontothe SAG mill feed stockpile before being fed to the SAG,Ball, Crushing (SABC) circuit.

Ore is drawn from underneath the SAG mill feedstockpile onto the SAG mill feed conveyor belt by means ofthree vibrating feeders. The ore is fed into the SAG mill forprimary grinding. The SAG mill discharge passes through atrommel screen where the underflow reports to a sump

before being pumped to a cluster of thirteen cyclones forclassification. The trommel screen overflow (SAG millScats) discharges onto the pebble crusher feed belt. Oncethe Scats has been crushed, it is fed back into the SAG mill.

The cyclone overflow feeds onto the trash screen whilethe underflow is split between two ball mills for regrindingand onto a scalping screen. The discharge from the ballmills is pumped to the cyclone cluster for classification.The scalping screen underflow is fed into two KnelsonConcentrators while the overflow is fed back into the SAGMill. Concentrate from the Knelson Concentrators is fed tothe acacia reactor in the gold room while the tails is fedback into the ball mills.

The trash screen overflow falls into a bunker while theunderflow is fed into the leach adsorption circuitcomprizing of three leach tanks and seven adsorptions tanksin series. Loaded carbon is recovered from the first tank ofthe adsorption train and transferred to acid washing, AARLelution and electrowinning. The slurry from the finaladsorption tank is fed into two thickeners via two carbonsafety screens. The water recovered from the thickeneroverflow is recovered and used in the plant as process waterwhile the thickened underflow is pumped to the TailingsStorage Facility.

The cathodes in the electrowinning cells are washed withhigh pressure spray water and the gold slime is recovered ina plate and frame filter press. The gold sludge filter cake isdried in calcination ovens and smelted on site before beingdispatched.

Expert system implementation

Project implementation methodologyAs the number of advanced grinding control applicationscontinues to grow, a well instrumented grinding circuit

Figure 1. Milling circuit flowsheet

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without advanced control is becoming the exception. Theincreased acceptance of this technology has many reasons,ranging from dependable connectivity software toaffordable computing power. Arguably, the main factor hasbeen consistent utilization when sites have run advancedcontrol systems at greater than 90% online time for over 9years. Utilization is a key performance indicator thatindicates that the following objectives have been achieved:

• Continued performance gains• Site acceptance and ownership—tuning and

maintenance• Support.Site acceptance and ownership are established during the

implementation phase of the project. SGS has a standardproject methodology illustrated in Figure 2. Thismethodology has been described by Festa et al.4, however,the critical aspects of each stage are summarized as follows:

Knowledge captureThe project engineer (integrator) must have the ability tointeract with all levels of an operation to ensure the bestoperating practices are obtained. The integrator must bringvalue to the table in this process. Integrators begin from alogic template (i.e. a distillation of the operating states andrules utilized successfully in over 40 SAG milling circuits).The operation and integrator collaborate to arrive at aconsensus that blends the template with the site bestpractices, resulting in an improved control philosophy ascurrent operational practices receive a critical revision.

Having achieved consensus, the next step is to code thelogic, but this should not result in a black box. The MECtoolkit addresses this issue by displaying the logic in areadable, graphical manner. The coding is also subjected tostringent QA/QC procedures to ensure that the logicstructure meets maintainability standards. Such guidelines

are critical to efficient support and ,so that years later,engineers should not need to take too much time in‘deciphering’ code.

Site installation and coarse tuning

Installation and connectivity checks at site are followed bycoarse tuning of the controller. The integrator engages theoperators to critique the expert control. Placing an expertonline, usually takes less than a week; however, asufficiently long period is required to tune for differentscenarios.

Operations critique

The expert runs under close supervision from site personnelfor an extended period of 2 to 4 weeks to allow for critiqueby site, while allowing for full remote support. Again, thisis a collaborative process that builds acceptance.

Final tuning and handover

The tuning of the application is finalized, and a statisticalevaluation is performed to ensure that no ‘holes’ exist in thelogic and that every operational state of the process is,therefore, accounted for. All relevant site personnel receivehands on training in the operation and maintenance of thesystem. The performance of the system is reviewed withoperations in preparation for handover, and all the logic isreviewed with the system administrators. Performanceguarantees, including acceptance and utilization, are tested.The site is supported by Gunter Metzner integrators at alltimes until final acceptance.

Project implementation risks

At North Mara, a number of critical factors needed to bedealt with effectively for the successful completion of theproject, and to ensure the long-term sustainability of theexpert system application.

Site roster turn around and personnel turnoverNorth Mara is a remote mine site where personnel work ona fly in fly out roster. This meant that key expert projectteam members were not always available. Careful planningof the tuning site visits was done to ensure sufficientexposure by all team members.

A high turnover of staff also impacted the project,highlighting the importance of training more than oneapplication administrator as part of the project team.

Ease of use of the application, becomes even morecritical when faced with these challenges, while theidentification and maintenance of site champions is crucial.

Operators new to expert control

Plant operators were unfamiliar with the use of advancedcontrol systems, so integrators placed high priority onensuring that sufficient time was spent with each crew.General principles underlying the expert system weretaught to each crew, as were the operating procedures. Thistraining provided the operators with understanding as wellas the confidence to critique the application. If an advancedcontrol system is perceived as a black box by control roomoperators, the system will be disabled whenever a particularaction implemented by the system is not understood, and ahigh utilization rate will not be achieved5.Figure 2. Project implementation methodology

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Critical to winning over ‘new operators’ is a humaninterface designed to clearly convey how (what part of thelogic fired) and why an action has been taken (whatoperational state is being interpreted and what setpoint hasbeen changed and by how much). The tools used to achievethis include graphs, action message queues, and a graphicalrepresentation of which part of the expert logic is active.

Remote site supportDuring the North Mara project, a remote supportconnection was provided. Such remote connections arebecoming the norm. They dramatically reduce resolutiontime and improve site confidence and system utilization. AtNorth Mara, this was a critical aspect of the project’ssuccess.

Expert system control strategyThe control strategy must be tailored to the project controlobjectives. The following objectives were set at NorthMara:

• SAG mill power draw to be kept stable and in a givenrange

• Continuously maximize throughput • Keep the grinding circuit product within plant

specifications• Expert system utilization rate in excess of 95%.

Measured and manipulated variablesThe measured variables available to the trend basedreasoning of the expert were:

• Mill power draw• Mill load• Discharge sump level• Cyclone feed density• Pebble recycle rate.The following variables were manipulated by the control

strategy to achieve the desired control objectives:

SAG feedrate

Manipulation of the SAG feedrate setpoint, results in theapron feeders’ speeds responding to effect the change in thefeedrate.

Mill discharge density

Manipulation of the mill discharge density setpoint resultsin a corresponding change in the mill inlet dilution water.

Feeder ratio

As a result of natural segregation occurring on the coarseore stockpile, it is possible to manipulate the outputs of thefeeders in order to manipulate the ratio of coarse to fine orefeeding the SAG mill.

SAG mill control logic aspectsSag mill operational states can be defined by combining thefuzzy beliefs of the measured variables. Figure 3 shows anexample of an operating state definition for the SAG millmade up of fuzzy belief inputs of the SAG mill power drawand load.

The operational states identified as critical are thenordered in a hierarchical control structure. This results inthe prioritization of the control actions associated with eachstate. See for example the state hierarchy illustrated inFigure 4.

Figure 3. Definition of the SAG overload state using fuzzy beliefs

Figure 4. SAG mill logic hierarchy



The hierarchy demonstrates how states requiringstabilization actions have higher priority than the statesresulting in optimization actions.

The logic hierarchy is the backbone to which rules arelinked; so when a state is active, the rules associated to thatstate may be activated. Once a rule activates, a timed‘wait’is triggered; the ‘wait’ is used to set the maximumfrequency at which a rule can be activated and is alsomonitored to prevent similar actions from being activatedsimultaneously. If, for example, a water setpoint isincreased, this triggers a water increase wait which isassociated with all water increase actions.

Depending on the severity of the operational state, theexpert system can also activate multiple actionssimultaneously. Alternatively, it may organize the controlactions in a hierarchical structure so as to prioritize a seriesof actions. An example of this rule structure is shown inFigure 5.

Each set of control actions is designed to be effective inaddressing the current operational state identified by theexpert system. The magnitude, and type of actionimplemented by the system will be dependent on the fuzzybelief of the variable or group of variables (a state, forexample) that is driving the action. As an example, duringinstances where a high load state is identified as in Figure 5,the control action will decrease the feedrate significantlyand will, furthermore, decrease the SAG discharge density,which, in turn, will result in a corresponding increase inmill inlet dilution water.

When the SAG mill returns to an OK state, the associatedcontrol actions will attempt to gradually optimize the millpower and will, therefore, not be as severe as in the case ofa high load. This is achieved by only implementing small,incremental step increases to the mill feedrate while noadjustments are made to the SAG discharge density.

The combined effect is a system which is flexible andcapable of rapidly identifying the most appropriate actionfor every operational scenario identified, resulting in anoverall improvement of operational stability andoptimization.

Stockpile managementThe expert system’s ability to manipulate the feeder ratiosmakes it possible to control the coarseness of the ore beingfed to the SAG mill. The expert system will increase theratio of finer material in the feed when the ore seems tobecome increasingly hard or increase the ratio of coarsermaterial in order to utilize available power or capacity inthe mill when the opportunity presents itself.

Multiple ore type controlThe milling circuit at North Mara is constantly challengedwith a high degree of variability in ore received from the pitas well as with frequent and extended downtimes on theprimary, secondary and pebble crushers. This requires thatthe SAG mill control strategy be quickly adjusted to rapidlychanging feed conditions.

The expert system makes use of multi state managers thatcan apply different control parameters to the operating logic,depending on the ore type. The ore type is derived through acombination of process measurements. For instance, usinghistorical trends of the crusher running status and SAG millpower draw, it was possible to establish an expected periodof time following a crusher shutdown or breakdown, beforethe SAG mill would start receiving significantly coarser ore.The configuration of running feeders was also indicative ofthe ore characteristics, since the natural segregationoccurring on the stockpile resulted in some feederspredominantly feeding coarser ore than others. It was,therefore, possible to distinguish between and create statesfor ore being coarse and fine (normal).

Based on the expert system’s inference of the ore state,the control actions are adapted automatically. It was foundthat coarse ore caused the SAG mill to be more sensitive tothe size of step changes in the feed rate compared to finerore feeds. The mill power spiked more often whenreceiving coarse ore, but the feed tonnage cuts needed toresolve the situation were smaller for the coarse ore than forthe same scenario with fine ore. By configuring the stepchanges for coarse ore to be smaller than for fine ore in thesame control scenarios, the expert system at North Mara ismore effective in controlling and stabilizing the millingcircuit under varying ore characteristics.

Results and discussionData was collected for a period of 4 months between June2008 and September 2008. The data sets used in theanalysis only include periods of at least 24 hours ofcontinuous plant operation.

The summary of the results using a t-test for two samplemeans are shown in Table I.

The data analysis indicates a statistically significantincrease in SAG tons milled of 14.6% with the expertsystem online, while no statistically significant differencebetween the SAG mill power draw is indicated by theanalysis. The standard deviation of the mill power wasfound to be higher when the expert system was offline,indicating that the operation of the expert system results in

Figure 5. Control logic and actions



an overall improvement in SAG mill operational stability.This is also illustrated by the histogram shown in Figure 6.

A histogram of the SAG mill load is shown in Figure 7.The mill load histogram indicates that instances of both

mill overloads and underloads are significantly reducedwith the expert system compared to manual mill operation.

Graphs depicting the relationship between SAG millpower and load for expert system, offline and onlineperiods, are depicted in Figures 8 and 9 respectively.

Figures 7, 8 and 9 clearly illustrate the expert system’seffectiveness in maintaining the SAG mill load within anoptimal band, therefore, preventing mill overloads fromoccurring and consequently minimizing the associatedproduction loss. This ability of the expert system toconstantly maintain mill operation in an optimized loadpower band is the primary reason behind the significantimprovement in mill production rates achieved.

During the second, fine tuning site visit, a number ofchanges and improvements were made to both the logic andsome of the operational parameters of the expert system inresponse to the critique from site personnel. As a result ofthe modifications implemented during this site visit, theutilization rate of the expert system improved to over 96%.As a consequence of this significant improvement, the datacollected over the 2 month period, subsequent to the visit,did not contain sufficient qualifying offline periodsspanning a full 24 hours, for a statistically significantanalysis of performance to be completed.

ConclusionsBased on the results achieved by the expert system installedat the North Mara operation, the following conclusions canbe drawn:

Table ISite visit 1 results

Average % diff. Confidence Standard dev.Online Offline Online Offline

Power (kW) 2930 2964 -1.1% 63.7% 149.5 170.0Tons milled (tons) 361 315 14.6% 99.9% 42.9 67.4

Figure 7. SAG mill load Histogram

Figure 6. SAG mill power histogram



• Expert control significantly improves mill throughput• Overall milling circuit stability increased• Utilization averaged over 96%• High acceptance rate by site personnel is a direct

consequence of not only hands-on training, but also thecollaborative and inclusive nature of the project

• Standardized operation of the SAG mill has reducedoperational variability between shifts

• A considerable advantage for a site which experiencesa large turnover of staff has been the use of the expertas a powerful and consistent training tool.

AcknowledgementsThe authors would like to thank North Mara for their fullsupport and collaboration in the writing of this paper.

References

1. WHITFORD, A.S. and SLOAN R.P. Innovativesolutions in the development of an expert system at

CCI’s Empire mine, 2001: A Mining Odyssey - SMEAnnual Meeting February 26–28, Denver, Colorado.

2. CUSTER, S., KAHL, T., MARTIN, J., and PARKER,S. Barrick Goldstrike grinding expert system. 33rdAnnual Operators Conference, Canadian MineralProcessors, 2001.

3. HODOUIN, D., JAMSA-JOUNELA, S.L.,CARVALHO, M.T., and BERGH, L. State of the artand challenges in mineral processing control. ControlEngineering Practice 9, 2001. pp. 995–1005.

4. FESTA, A., CORNEJO, F., ORRANTE F., ALANIS,R., and GUTIERREZ, B. Expert SystemImplementation at Peñoles Group Concentrators. 42ndAnnual Meeting of the Canadian Mineral Processors,2009.

5. GORDON L. Advanced Process Control‚—FuzzyLogic and Expert Systems, Control Engineering,9/1/2005.

Figure 8. SAG mill power vs mill load for expert offline

Figure 9. SAG mill power vs mill load for expert online



Gunter MetznerRegional Manager, Advanced Systems – Africa & Australasia, SGS

Gunter graduated with a Masters in Systems and Control from the University of Manchester Instituteof Science and Technology (UMIST), and a PhD from the University of the Witwatersrand. He 10years with the Measurement and Control Division at Mintek, working on the control of Milling.Gunter joined the De Beers Group in 1994 to build a research group focussing on automation,developing and deploying applications in intelligent advanced control systems, monitoring anddiagnostics. He eventually became the Manager of the Process R&D section of DebTech. He joinedthe Advanced Systems Group of SGS in 2008, as the Regional Manager – Africa and Australasia.


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