Anglo research (AR) experience with J integrated ... · Anglo research (AR) experience with integrated comminution and flotation plant modelling Collection of Scada data to assist

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Introduction

Anglo Research (AR), on behalf of AngloAmerican plc (a sponsor of the AMIRA P9project), has become involved in the AMIRAP9 comminution and flotation modellingprojects over the last few years. During thisperiod, AR has carried out detailed samplingand measurement campaigns in both thecomminution and flotation concentrators ofvarious Anglo base metal operations on anindividual basis, and integrated surveys asdescribed in Table I.

The focus of this paper is not to explore indetail any particular aspect of this process.Instead the authors aim to describe (briefly) a

number of activities that comprise a typicalmodelling campaign, and to provide examplesand evidence of how processing plants havebenefited from this integrated surveyingapproach.

The on-site work described in this paperwas carried out by AR metallurgists with theassistance of the personnel working on the sitein question. JKTech staff were present duringthe first campaigns for both comminution andflotation, which also functioned as a trainingexercise. As a result, AR now carries outtypical P9 exercises, although JKTech staffhave sometimes remained involved as aconsultant. AR has gained experience duringthe course of this project which has helpedthese exercises to become increasinglyefficient.

Description of modelling process

Typically, prior to the actual samplingcampaign, a first trip to the site is made toinform all concerned about the aims of theexercise as well as to meet potential assistantsand to gather information about the circuit. Ageneric planning document is issued at the siteto familiarize personnel with the surveyprocess, equipment, analyses required, etc.Thereafter, a further four to six weeks arenormally required to allow staff at the plant toprepare for the exercise. This usually involvescreating access for sampling andmeasurements with the mills, cyclones and theflotation cells. A typical integrated samplingexercise takes on average one week, duringwhich the following activities are carried out:

➤ detailed survey of every available streamin the comminution circuit

➤ additional sampling for BWI, RWI and JKdrop weight tests

➤ measurement of mill dimensions, linersspecs, etc.

Anglo research (AR) experience withintegrated comminution and flotation plant modellingby S. Naik* and W. van Drunick*

Synopsis

To date, Anglo Research has carried out several projects, based onthe techniques developed in the AMIRA P9 (AMIRA InternationalLimited), to develop comminution and flotation models for severalAnglo Base Metals and, to a lesser degree, Anglo Platinum flotationplants.

Plant optimization is achieved through steady-state sampling,model-fitting and simulation, using the JK SimMet and JK SimFloatsoftware packages. The surveys can vary depending on the needs ofthe plant/client, and can therefore include as little as a single pieceof equipment, an entire milling/flotation circuit, or a completecircuit from primary comminution to final flotation concentrate (i.e.an ‘integrated survey’).

The presence of the AR team on site usually precipitates face-value observations that can lead to initial improvements. This stemsfrom the knowledge-sharing on best practice that takes place inparallel with the surveys. Numerous (reasonable) changes can thenbe simulated. These indicate the optimization step most likely toresult in the best efficiency improvements. The simulations caninclude changes in the flow sheet, ore characteristics, mill loads,grinding media size, grind, flotation residence time and air hold-up.Plant surveys can result in process changes that provide the plantwith higher plant tonnage, improved plant grind, additionalflotation recovery and/or improved product grade. An additionalbenefit of performing an integrated plant survey is that the effectsof an optimization change in the comminution circuit can besimulated using the flotation model.

This paper describes a number of case studies of individual andintegrated plant surveys conducted by AR for Anglo Base Metals.

* Anglo Research.© The Southern African Institute of Mining and

Metallurgy, 2007. SA ISSN 0038–223X/3.00 +0.00. This paper was first published at the SAIMMConference, Base Metals 2007, 23–27 July 2007.

641The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER OCTOBER 2007 ▲

SAIMM_Oct_37-46:Template Journal 11/5/07 8:11 AM Page 641

Anglo research (AR) experience with integrated comminution and flotation plant modelling

➤ Collection of Scada data to assist in mass balancing,power modelling, steady state and process controlanalyses, etc.

➤ Detailed survey of every stream in the flotation circuit(for model building)

➤ Laboratory batch flotation tests ‘hot floats’ of criticalstreams (to collect additional kinetic data)

➤ Cell hydrodynamic measurements (to characterize thecell behaviour);

- Superficial gas velocity (Jg)- Air hold-up- Bubble size

➤ Residence time studies (to confirm mass balance and todetermine cell mixing characteristics)

➤ Alternate circuit survey (for additional modelvalidation, if possible).

For the sampling exercise itself, AR has found it essentialto do as much work as possible on a single day on each ofthe comminution and flotation circuits. To date, experiencehas shown that no plant operation is sufficiently stable toallow consistent detailed sampling from day to day. Typically,few plants can be relied upon to have less than a five per centrelative change in head grade from one day to the next, andwhilst this may have only a limited effect on the overallperformance of the plant, the change in assays from samplesof individual cells can be marked. Hence AR involves asmany people as possible in the survey. Certain measurementssuch as bubble size and superficial gas velocity, for which noslurry samples are taken, are less sensitive to ore changes.These measurements can be made on other days, althoughideally they are best taken on the same day. The slurrysamples, which represent a snap-shot of the process, are usedto model the equipment and the process.

The aim of this exercise is to generate an accuratedetailed mass balance of the plant to fit parameters formodelling. This differs to some extent from the aim ofobtaining an overview of average plant performance.Although this is contrary to conventional wisdom, experienceto date has shown that single-cut ‘snap-shot’ sampling canbe as good as, if not better than, multi-composite sampling.

In addition, mine management must be informed that thecurrent models are ore-specific. So, in order for the model tobe of maximum benefit, the mine must attempt wherepossible to run on typical material. If necessary, it is advised

that sampling be postponed if non-typical ore is being runwhen the exercise is conducted. Laboratory batch flotationtests are carried out to collect additional kinetic data formodelling and, in addition, to confirm certain parameterssuch as residence times and additional cleaning steps. Millcrash-stops and grind-outs are conducted to determine milldimensions and load/ball volumes accurately.

In total, approximately 300–350 samples are typicallycollected during the week-long test programme. This requirescareful planning in the sample preparation laboratory. Assayand screening results are generally received approximatelyfive to seven weeks after the end of a campaign. Massbalancing, modelling and analysis typically take a further twoto three months.

Modelling approach

The comminution section is modelled in JK SimMet via arange of available models. The choice of model depends onthe nature of the machine and the quality of the data. For ballmills, the perfectly mixed ball mill model is typically used.Seven different models are available for each of the screenand cyclone modules. SAG/AG mills have four modelsavailable, of which the ‘variable rates’ model is typicallyused. The general spectrum of comminution-type equipmentcan be modelled in the flow sheet, from cone and jawcrushers to HPGRs and the associated classification devices.

The general principle of the flotation modelling approachis the decoupled model of flotation as shown in Equation [1]below. Cell parameters such as bubble surface area flux,residence time, water recovery and the overall recovery canbe measured and calculated for each cell from the massbalance information collected during sampling. To a lesserextent, entrainment and froth recovery can either be predictedor measured. Pi—the fundamental flotation response—is thenfitted as a distribution. Many data points throughout thecircuit, and the accompanying on-site laboratory batchflotation tests, are used to ensure a high degree of confidencein the fit.

The AMIRA P9 recovery model for flotation for species iin a perfectly mixed cell:

[1]

where:Ri = recoveryPi = floatabilitySb = bubble surface area fluxτ = residence timeRf,I = froth recoveryRw = water recoveryENTi = entrainment parameter

Having characterized the ore, AR can use the model tosimulate circuit changes and to assess various ‘what-if’scenarios. However, many of the measurements andintermediate results are often as useful as the final simulatorwhich results from this process. These are discussed in turn.

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642 OCTOBER 2007 VOLUME 107 NON-REFEREED PAPER The Journal of The Southern African Institute of Mining and Metallurgy

Table I

List of AR flotation modelling exercises

Date Operation Circuits : Survey Type

2002 Lisheen Pb, Zn : Flotation2003 Hudson Bay Cu, Zn : Flotation2004 El Soldado Cu : Flotation2004 Las Tortolas Cu : Flotation2004 Mantos Blancos Cu : Flotation2004 Los Bronces Cu : Comminution2005 Lisheen Pb, Zn : Integrated2006 Catalao Nb : Integrated2006 Black Mountain Cu, Pb, Zn : Flotation2007 Copebras P2O5 : Integrated


Modelling results

The aims of a plant model can be either specific or general.For example, one may require a model to determine theoptimal circuit configuration to produce a target grind, gradeor recovery. A general aim would be to analyse the overallperformance of the plant in terms of the ore as well as theperformance of the individual mills/cells and to investigatepossible areas of improvement. In this section, for each of theactivities typically carried out on the plant during a modellingexercise, some examples of results are presented anddiscussed.

Mill performance and efficiency parameters

A number of measurements and efficiency parameters aremeasured for every comminution campaign. Throughput andpower draw are insufficient in determining the efficiency of amill with respect to its design parameters. The overallperformance of a mill is collectively based on a number offactors, that include: liner design, discharge grate design,percentage of open area at the mill discharge, respectivegrinding media (type, size and loading), product PSD, etc.

Figure 1 below shows a typical mill breakage anddischarge rate curve with respect to size. The discharge ofslurry is most efficient below approximately 400um, afterwhich the slurry discharges more slowly. This is particularlyuseful for altering the mill discharge design for improvedthroughput. In addition to this, the breakage rate curve for aSAG mill displays a “dip” between 20 and 70mm. Thisindicates the critical size material size range, which is criticalto correctly sizing a discharge trommel and also setting therecycle crusher gap, for instance.

Flotation performance parameters: gas dispersionmeasurements

Bubble size

The earlier testwork made use of the McGill Bubble Sizer (seeHernandez-Aguilar et al., 2002 for more details) while morerecently AR has made use of the Anglo Platinum BubbleSizer presented in Figure 2. Although there are published

empirical correlations to predict the bubble surface area flux(e.g. Gorain, Franzidis and Manlapig, 1999) and hence,indirectly, bubble size in mechanical cells, prediction ofbubble size in columns has been less well quantifiedtherefore a measurement is essential.

Figure 3 presents typical bubble sizes of between 1.4–1.8 mm (d32) in the conventional rougher cells. This is inagreement with literature sources (Deglon, Egya-Mensah andFranzidis, 2000; Power, Franzidis and Manlapig, 2000),which indicate that bubble sizes of less than 2mm aresuitable for flotation. Interestingly, in the secondary circuit,the bubble size decreases down the rougher bank: this maybe the result of the frother addition down the bank. The d32of the primary rougher 9 cell was measured as high at2.5mm, indicating frother requirement.

Anglo research (AR) experience with integrated comminution and flotation plant modellingJournal

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643The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER OCTOBER 2007 ▲

Figure 1—SAG mill breakage and discharge functions

Figure 2—Anglo platinum bubble sizer

In (

brea

kage

rat

e)

Dis

char

ge f

unct

ion

0.01 0.1 1 10 100 1000

Size

ln R Discharge Rate

8

7

6

5

4

3

2

1

0

30

25

20

15

10

5

0



Bubble size measurements improve the analystsconfidence in the simulations because of the more accuratedetermination of the bubble surface area flux (Sb). Bubblesize results also demonstrate whether the cells and thereagent regimes are creating the typical bubble sizes neededfor flotation. To date, this has been the case in all the cellsmeasured. The Bubble Size analyser may provide a tool toevaluate different reagent and/or cell conditions. Howeverthis has not been done in any AR studies as yet.

Superficial gas velocity (Jg)The P9 recovery equation contains a parameter Sb, thebubble surface area flux. This requires knowledge of thesuperficial gas velocity Jg and the Sauter mean bubble size(d32). Measuring Jg in a cell also allows the operation of thecell to be compared with that of other cells in the same plantand in other plants (eg Power et al., 2000), and it allows theuse of the cell for other purposes to be predicted insimulations. The Anglo Platinum Bubble Sizer is also used tomeasure this parameter.

Although modern cells are often fitted with air flowmetres, the Jg probe remains useful for calibrating theaccuracy of the measurement. Sometimes several cells use acommon air source: the Jg probe can identify bias in theamount of air fed to each cell. In one case, the Jgmeasurement quickly revealed that the air flow to a certaincell was constrained. Additionally, for self-aerated cells (e.g.Wemco), the Jg probe is essential to measure and/or comparethe air flow rates.

In Figure 4, a comparison of the Jg measurements in aplant shows that there is room for improvement in therougher and cleaner scavenger cells by adding more air.Typically, froth recovery permitting, the cells may performbetter at deeper froth depths and higher air flows.

Air Hold Up (AHU)The principle of the AHU measurement device (shown inFigure 5) is that a sealed volume of aerated pulp is capturedfrom a cell, and then released into a measuring cylinder. Thedifference in volume to that of the sampler corresponds withthe air fraction.

This measurement is useful in residence time determi-nation, because it provides a correction for the volume of airwithin a cell. This improves the accuracy of the models. TheAHU sample serves an additional purpose as material forfroth recovery calculations.

As a diagnostic tool, AHU has had only limitedapplication. Typically, on the plants visited to date, air hold-up and Jg have been approximately proportionally related:cells with low air flows have also had low air hold-ups. Highair hold-up measurements (~30 per cent) can be anindication of high pulp viscosities. However, this has notbeen encountered to date.

Residence time studies

Solution tracer studies are conducted as part of the plantmodelling exercises to determine the liquid residence time invarious stages of the circuit. While these are not the highestpriority in the exercise, they are useful for comparing withthe mass balance, and particularly in determining the waterbalance, which is typically less accurate than either the solidsor mineral balances. Both LiCl and NaCl have been used astracers. The calculation of the required concentration is basedon the minimum needed to make a distinguishable peak,even in the worst-case scenario (when the salt dispersesperfectly).

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Figure 3—Bubble size distribution down the rougher banks

Bub

ble

size

- d

32

3

2.5

2

1.5

1

0.5

0Rghr 1 Rghr 3 Rghr 5 Rghr 7 Rghr 9

Primary Roughers Secondary Roughers

Figure 4—Comparison of Jg values (cm/s) for various Outokumpu tankcells in a circuit

Figure 5—Air Hold Up (AHU) device

Jg (

cm/s

)

2.5

2.0

1.5

1.0

0.5

0.0

ROUGHERS

TYPICAL OKTANK CELL

CLNR. SCAVS 1st CLEANERS


Froth recovery determination

Froth recovery reflects the loss of recovery of attachedparticles in the froth phase. Froth recovery (Rf) of 100% (asassumed in the shallow froths in laboratory batch flotationtests) implies no loss of recovery due to the froth phase. Anumber of techniques are available to measure frothrecovery. In the AR exercises to date, the mass balanceapproach suggested by Alexander, Franzidis and Manlapig(2003) has been adopted. Simultaneous mass balances ofattached and entrained material, using feed, pulp and tailssamples from a flotation cell, combined with a sample fromthe top of the froth, allow the froth recovery of attachedminerals to be calculated. To date owing however, thisapproach has yielded little success, perhaps to the need forextremely accurate sampling. Triplicate samples may beneeded to identify the subtle changes in grade. This is oftennot practical for a complete circuit of many cells. At best, theapproach may work for certain cells, which are then used as indicators for the remaining cells in the circuit. Theimplication of this difficulty in measuring froth recovery consistently is that Rf becomes another fitted variable in the floatability determination fit. This makes it all the moreimportant to collect sufficient data to ensure an accuratemathematical fit.

Detailed survey sampling

Microsoft Excel Solver techniques are used to fit the massbalance flows around the circuit from the assay data of thevarious elements. A common finding in all of these plantmodelling exercises is that a detailed cell-by-cell massbalance is in itself a critical diagnostic tool. For example,Figure 6 shows the mass distribution across the rougherbank, while Figure 7 presents the distribution of copper downthe bank while very little lead is collected.

Additional collector (both SIBX and Sascol 61) has beenadded at the mid-point of the rougher bank. Note how themass pull to the concentrate increases after the addition ofthe reagents, indicating that there may be merit in stageaddition of the reagents for this orebody, in order to improvethe mass pull to the concentrate.

Figure 8 presents the overall recoveries (by size) for oneof the base metal concentrator plants. In this example the useof assay-by-size mass balances identifies the critical fractionswhich account for the losses. A majority of the losses are(approximately 65%) in the primary rougher tail in the +53 μm fraction. In addition, most of the valuable materiallost in the cleaner 7 tails is in the -10 μm fraction (approxi-mately 86%).

The use of mineralogy further identifies the potentialreasons for the losses. Examination of the cleaner tailsstream showed that over 40% of the valuable material in thisfraction is liberated, while the remainder is either locked orattached to the gangue minerals.

Laboratory batch flotation testsLaboratory batch flotation tests are carried out on criticalsamples, typically feed, concentrates and tails, from eachstage of the process, using exact duplicates of the samplessent for screening, chemical and mineralogical analysis. Thekinetic recoveries measured in these tests provide furtherinformation, which is used to increase confidence in the fit ofthe ore’s floatability distribution. Typically 10–15 laboratoryflotation tests are carried out per circuit, and these generatean additional 112 to 160 data points which can be used inthe model fitting. The results of the laboratory batch flotationtests on the rougher tails stream are an indication of thecontent of ‘non-floating’ material in the ore. Valuables whichare not recovered in these tests in the ten per cent frothrecovery conditions of the laboratory batch cell areconsidered non-floating. This is a useful number with whichto evaluate the actual plant recovery, and to quantify anypotential scope for improvement in the circuit. Figure 9presents the possible recoveries from the feed andconcentrate samples as well as the potential to recover someof the valuables from the exit streams.

The laboratory tests are also critical in evaluating ‘nodes’at which there is a change in floatability, for example atpoints in the circuit where there is reagent addition or aregrind mill. In general, AR work to date suggests thatreagent addition within circuits has only limited effect onfloatability. The effect of regrinding is demonstrated in


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The Journal of The Southern African Institute of Mining and Metallurgy VOLUME 107 NON-REFEREED PAPER OCTOBER 2007 645 ▲

Figure 6—Mass recoveries down the primary rougher bank

Mas

s p

ull

(%)

1.20

1.00

0.80

0.60

0.40

0.20

0.00RC1 RC2 RC3

Cell #

RC4RC5 RC6 RC7

RC8 RC9

Mass Pull



Figure 10, which shows kinetic recovery curves before andafter a regrind mill. The finer grind leads to an improvementin both the kinetics and recovery.

Model fittingOnce the mass balance of the comminution circuit has beencompleted, it is then possible to model-fit the circuit. This isdone by fitting parameters to the equipment models, usuallystarting with an intelligent estimation of the parameter. Thefeed data for the equipment are then entered into the model,and a good fit is obtained when the model accurately predictsthe sampled product of the equipment. Figure 11 shows thetypical range of parameters fitted to a hydrocyclone and aSAG mill.

With respect to the flotation results, once all the details ofthe measurements and the mass balance and laboratoryflotation test data have been collected, the next stage in theprocess is to fit the floatability distribution of the feed ore. Anexample of a floatability distribution for galena and sphaleritein a lead circuit is shown in Table II. With a feed of thisfloatability distribution, it will not be possible to recover theslow-floating galena without also recovering approximatelynine per cent of the sphalerite. This is an example of how thisapproach helps to quantify and hence identify the causes ofproblems within a circuit.

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Figure 7—Base metal distribution down the rougher bank

Figure 8—Distribution by size class

Figure 9—Pb Recoveries in a batch flotation cell

Dis

trib

uti

on

(%

)

Cell No.

Copper

Lead

Lead Copper

30.00

25.00

20.00

15.00

10.00

5.00

0.00

Dis

trib

uti

on

(%

)

120.00

100.00

80.00

60.00

40.00

20.00

0.00Feed Prim Comb conc. RT9 Prim CT7 RC1 Re-Clnr Conc

Stream

Unsized +53 μm

+10 μm –10 μm

77.4

1.000.800.600.400.200.00

Prim CT746.6

21.0

42.949.4

1.92

27.9 28.1

15.011.1

5.4

16.11

21.6

16.5 14.42

0.10.83.6 3.6 0.90.003

32.46

100.00%

Pb

rec

ove

ry

0 2 4 6 8 10 12 14Time (min)

Pb Food Pb Cil 1 Conc Pb Col Tails Pb Scav Tails

1009080706050403020100

Figure 10—Sphalerite recovery in the laboratory batch test before andafter re-grinding

100%

80%

60%

40%

20%

0%0 2 4 6 8 10

Time (min)

Sp

hal

erit

ere

cove

ry (

%)

A


Circuit simulations

The final stage in the process is the development of a circuitsimulator using all the information described in this paper.Once the models have been fitted, simulations can be run ona host of changes in either the process flow sheet, operatingconditions or equipment dimensions. It is also possible toconduct simulations using combinations of all of thesechanges (within reasonable limits). Figure 12 shows a typicalSABC circuit simulation flow sheet in JK SimMet. Thisparticular circuit simulator was used to determine (amongother things) the effect of total plant throughput on the finalgrind.


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Figure 11—Typical model parameters for a hydrocyclone cluster and SAG mill

Table II

Floatability distribution of sphalerite and galena in a lead circuit

Galena SphaleritePi Xi Pi Xi

Fast 0.008675 0.748 0.00786 0.01

Medium 0.00143 0.085

Slow 0.0005 0.078

Non 0 0.174 0 0.905

Total mineral - 1 - 1

Figure 12—Typical SABC circuit simulation flow sheet

1009080706050403020100

Cumulative % Passing - Exp and Cal

%



Figure 13 shows the effect of increased throughput on thecyclone overflow PSD.

AR uses JK SimFloat (Harris et al., 2002) to run theflotation simulations. A JK SimFloat screen is shown inFigure 14.

The first step, once the simulator is working, is to run achanged circuit in order to validate the plant model. At eachsite, a request is made to change the circuit and to survey thischanged circuit. The plant must be prepared to operate thiscircuit until it stabilizes. In general, AR has obtained goodmodel validation when the changed circuit runs on similarore to that used in the original survey. However, there havebeen occasions when the ore has changed, and the predictiondoes not match the actual circuit performance. Thisexperience emphasises the need to do as much sampling aspossible on a single day. Note that the relative changes inperformance predicted by the simulator are believed to be farmore robust and to apply even in cases where the feed ore is

different. However, when comparing the outcome of thechanged circuit with the results of the original detailedsurvey, it is essential to have similar ore so as to evaluate thequantitative effect of the change on plant performance.

A typical basic simulation is to vary the residence time.Although additional cells are often not an option on the plant,this gives an indication of any bottlenecks in the circuit.Other simulations which are typically tested, and are morelikely to be possible to implement in reality, are:

➤ changed air flows➤ water addition➤ open circuit/closed circuit cleaning and the tails return

point➤ rougher-scavenger split in banks, and➤ multiple stages of cleaning.

An example of an AR simulation which was implementedin practice is shown in Figure 15. The simulation showed thatreturning the first cleaner tails to the fourth rougher cell feed

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Figure 13—Effect of throughput on final circuit grind

Figure 14—Typical JK SimFloat screen showing inputs and the flow sheet in the background

Cu

m %

pas

sin

g

Size (mm)

170 t/h 180 t/h 190 t/h 200 t/h 211 t/h 220 t/h 230 t/h

Mill circuit product size with changing feedrate


would give the same recovery and grade as that achievedwhen it is returned to the feed of the circuit. However, thischange would greatly benefit the plant in terms of theimproved stability of the first two rougher cells, which areresponsible for the majority of the circuit recovery. Hence theplant has implemented and maintained this change.Unfortunately, the benefits achieved through increasedstability are difficult to quantify exactly.

The second example illustrates how the simulator can beused to identify not only the need of additional cleaningcapacity but also where to route the concentrate stream. Theneed for additional retention time was identified in the firstcleaner stage (in a three stage cleaning circuit). However,with the aid of the simulator it became clear that in order tomaintain the final concentrate grade, the concentrate from theadditional cell should be sent back to the feed going to the

first cleaners rather than the feed to the second cleaner stage(i.e. the cell is acting as a cleaner scavenger). The circuitconfigurations are illustrated in Figure 16.

Developments

Although the JK SimMet and JK SimFloat packages are able toproduce reliable simulations, the development of the modelsis still on going. Much of the research and development ofthe models is done through the sponsor-funded research ofAmira P9.

The original JK mill and cyclone models have beenradically improved in recent decades, and the current versionof JK SimMet includes the best of these models. However,owing to the different aspect ratios of mills and the very largediameter of the mills that are currently being built, the


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Figure 15—Circuit change as a result of simulation

Figure 16—Addition of additional cleaning capacity

Additional Cleaner ConcentrateTo Cleaner 1 Feed

CircuitFeed

Roughers

Final Cleaners

First Cleaners

2nd Cleaners

FinalConc

Scavengers



models are constantly being upgraded. Powell is currentlydeveloping the UCM (Unified Comminution Model), whichaims to account for a broader range of mill dimensions,including the effects of size on the breakage rate of an orecomponent and also the effect of segregation in low aspectratio (poorly-mixed) mills.

The majority of the data presented in this paper weresourced from exercises in which the floatability componentapproach was used to represent the different floatabilityclasses of each mineral. The most recent AR exercises haveincluded assay by size analysis to improve the accuracy ofthe models. This is particularly necessary in circuits includinga regrind milling stage, and possibly also column cells, whichare believed to be more sensitive to particle size. In thesecases, the potential uses of the mass balance results will havean additional level of complexity. The further inclusion ofmineralogical analysis, which has considerably aided in theunderstanding of the flotation kinetics, is clearly an exampleof additional complexity. However, it is advised thatmodelling should always proceed sequentially in order ofincreasing complexity (and cost) of analysis. Firstly, this willallow intermediate reporting of results to the plant, andsecondly will provide a check that only appropriate samplesare sent for analysis, as can be the case in the event ofchanging plant head grades for example.

Integrated surveysThe following is a description of a recent example where thebenefit of integrated surveys was evident. On a particularlead-zinc mine, the flotation survey results indicated that theplacement of the re-grind mill was inappropriate. The re-grind mill was treating the column tails, which then fed abank of scavenger cells. The concentrate from these cells wasthen sent to the column feed. Mineralogical examination ofthe column feed showed that a majority of the minerals werein binary phases with pyrite; hence it would be beneficial tore-grind the column feed rather than the column tails toliberate the minerals. This would enable the mine to achievehigher concentrate grades. The staff at the plant werereluctant to accept this change as they felt that the efficiencyof the re-grind mill would be hampered by the additionalmass flow through the mill. However, since the re-grind millwas also surveyed as part of the integrated comminution-flotation survey, the new throughputs were simulated. Thisresults showed that with changes to the steel media load andball size, there was sufficient capacity to treat the columnfeed and obtain the desired grind. The changes wereimplemented by the plant with success.

In addition to this example, there have also been caseswhere the flotation survey has recognised certain size classesthat do not float well (because they are either too coarse ortoo fine). The milling optimization therefore aimed atreducing the amount of material outside the desired sizerange by creating a steeper product PSD.

Throughput is often the desired target of many grindingplants, but the effect of reduced flotation residence time isdifficult to estimate without a flotation model. The integrationof the flotation survey thus makes it possible to determinethe effects of tonnage and grind on recovery and grade. Inaddition, the flotation simulator is able to simulate whichchanges of the cell conditions are able to cater for theanticipated changes.

Conclusions

This paper has given an overview of some the work done andthe results obtained in the AR comminution and flotationmodelling project over the last few years. The potentialbenefit of each of the phases of a typical plant modellingcampaign has been demonstrated.

Comminution circuits are modelled to the extent that theeffects of circuit changes can be confidently anticipated(whether the output is an accurate prediction or a relativechange in the process). The changes that can be simulatedinclude: flowsheet configuration, flow rates, operatingconditions, equipment parameters and ore competency.

The flotation cell characterization equipment is useful indetermining the actual performance of the cells, in terms ofair flow and bubble size. Typically, these results arecompared with industry averages. Advice can be given incases where non-typical performance is measured.

In AR’s experience, the detailed cell-by-cell mass balanceand the laboratory batch flotation tests are often the mostuseful stages of the modelling exercise. Not only are they themost easily understood and trusted by plant management,but they can be produced within a few weeks of receiving thedata and hence are relevant to current plant performance.Assay by size has further enhanced the mass balancingexercise, and also the understanding of the various unitswithin a concentrator.

The comminution and flotation simulation stage providesa means to quantify the effect of changes to complex circuitsin terms of potential benefit. This approach, despite the needfor caution and understanding of the software, offers a viablesolution for plant improvements.

References

ALEXANDER, D., FRANZIDIS, J., and MANLAPIG, E. Froth recovery measurement inplant scale flotation cells, Mineral Engineering, vol. 16, no. 11. 2003.

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Anglo research (AR) experience with J integrated ... · Anglo research (AR) experience with integrated comminution and flotation plant modelling Collection of Scada data to assist