The Eureka Mine – An Example of How to Identify and ... Plant Geet.pdf · To fully appreciate the complexity of flotation at Eureka it ... AND SOLVE PROBLEMS IN A FLOTATION PLANT

ABSTRACT

As the title implies, this chapter will provide, by way of example, amethodology for identifying and solving problems in a flotation plant. Todo this a ‘mythical’ concentrator (The Eureka Mine) will be describedand used to demonstrate to the reader how to go about the process ofidentifying where the losses of valuable mineral occur, and what ganguespecies are diluting the concentrate. It is intended that this chapter beused in parallel with subsequent chapters to guide the reader through thesteps involved in the process of optimising the plant.

INTRODUCTION

Many of us have, at one time or another, flicked through thenewspaper or searched online for a job, and stumbled upon an adnot unlike the one that appears below. Whether we are jaded withour current role, looking for a step up to the next level, or wanting

a new challenge, we prepare our resume and send it off in thevain hope that we may be the successful candidate.

With the interview process out of the way, the waiting and selfdoubt start. And, after what seems an eternity you receive a phonecall or letter telling you that you have got the job.

Congratulations! Now what? You’re new, you have ambition,you have drive, and you want to make your mark! But, there’s aright way and a wrong way to do this. The first thing to rememberis that this place has a history, and the people you are going towork with have been here much longer than you have. So,communication and respect are keys to your success. You need todiscover the history of the concentrator, and discuss its operationwith other members of staff (operators, metallurgical technicians,shift foremen, plant metallurgists, the chemist, mechanical andelectrical maintenance, the mine (ie geologists and miningengineers) and supply). They will all give you their perspective,

Flotation Plant Optimisation Spectrum Series 16 1

CHAPTER 1

The Eureka Mine – An Example of How to Identify andSolve Problems in a Flotation PlantChristopher Greet

MAusIMM, Manager Metallurgy – Minerals Processing Research, Magotteaux Australia Pty Ltd, 31Cormack Road, Wingfield SA 5013. Email: [email protected]

Chris commenced his working life as a Trainee Metallurgist at Bradken’s Adelaide steel foundry in 1978. Hesubsequently worked in a number of foundries, before becoming a shift foreman at Seltrust’s Teutonic BoreMine in 1982. In 1985 he decided to formalise his knowledge by studying for a Bachelor of Engineering inMetallurgical Engineering at the South Australian Institute of Technology. Upon graduating, he worked as aPlant Metallurgist at Ok Tedi Mining Limited and Bradken Adelaide, before undertaking a PhD at the IanWark Research Institute in 1992. Since leaving the Wark, Chris has held applied research positions atMount Isa Mines Limited, Pasminco, AMDEL, and now with Magotteaux Australia, where he leads thetechnical group identifying the impact of grinding chemistry on downstream processing.

Abstract

Introduction

The Eureka Mine

Data Acquisition

Problem Definition

Solution Development and Testing

The Cycle Begins Again

Communication

Conclusions

References

Appendix 1 – The Down-the-Bank Survey

Appendix 2 – Estimated Mineral Assays from Elemental Data

and you need to respect their point of view. You will also workout who among these people hold the knowledge, the history; thereal story of your concentrator.

While you are establishing relationships you need to determinewhat (if any) data exists that will help you to develop a technicalperspective of how your concentrator performs.

The focusing questions in any process improvement strategyare:

• Where and how do the losses of valuable mineral occur?

• What gangue minerals are diluting the concentrate and howdid they get there?

The intention of this book is to provide you with a sequence oflogical steps to follow so that you can collect the necessary datato be able to define the problem(s) within your operation. Oncethe problems are defined, you can then prioritise them anddevelop experimental strategies that may lead to solutions thatcan be implemented in the plant.

To assist you in this journey the performance of the EurekaMine will be scrutinised and used as an example. The EurekaMine treats a complex polymetallic sulfide ore supplied from anunderground mine, producing three saleable concentrates. Thischapter provides a description of the Eureka Concentrator, and itsmetallurgical performance since commissioning. Then provides,by example, the methodology used to collect plant data, analyseand interpret it to determine where the metallurgical problems lie.

THE EUREKA MINE

Location and history

Extensive geological surveying of the region north of LaylorRiver by Stockade Resources Limited resulted in the discovery ofthe Eureka deposit in 1990. Throughout 1991 and 1992, drillingcontinued to delineate the deposit. Preproduction geologicalstudies indicated probable reserves of approximately 25 milliontonnes of greater than 15 per cent zinc plus lead, witheconomically significant copper, silver and gold grades.

A one million tonnes per annum processing plant wascommissioned by Stockade in 1995 to treat ore with an averagehead grade of 0.4 per cent copper; three per cent lead; 12 per centzinc; 130 g/t silver; and 2.0 g/t gold.

Geology

The Eureka volcanogenic massive sulfide deposit occurs withinthe Laylor-Eureka Volcanic sequence of the Mount RushVolcanics. The deposit was formed when hot mineralisedsolutions were spewed out on to the ocean floor and were rapidlyquenched by the surrounding seawater. Hence, the sulfideminerals that were precipitated from solution formed very finecrystals and intricate mineral textures. Subsequent geologicalchanges to the orebody were few; therefore many of the originalfine grain textures remained intact.

Deposit mineralogy

To fully appreciate the complexity of flotation at Eureka it isnecessary to have a rudimentary understanding of the mineralogyof the orebody. Eureka is unusually sulfide rich, and contains arelatively simple mineral suite: 58 per cent pyrite, 20 per centsphalerite, four per cent galena, two per cent arsenopyrite, one percent chalcopyrite, with minor amounts of tetrahedrite. Theremaining 15 per cent of the ore consists of: quartz, barite,calcite, chlorite, sericite and siderite.

Macroscopically the mineral textures are diverse, however, theorebody can be divided into two distinct metal zones. Thedemarcation between the two zones is set, arbitrarily, at 100 g/t ofsilver, and represents a continuous horizon across the deposit.Above this level is the hanging wall enrichment zone characterisedby higher lead, zinc, silver, gold, and arsenic grades.Macroscopically the sulfides within the enrichment zone tend to bebanded and very fine grained. The footwall-depleted zone occursbelow the 100 g/t silver horizon. Pyrite and chalcopyrite are thedominant minerals within this part of the orebody, with reducedlead, zinc, silver, gold, and arsenic grades. The footwall-depletedzone is highly recrystallised, therefore the grain structure iscomparatively coarse when compared with those observed in thehanging wall enrichment zone.

It is important to note that the Eureka orebody is relatively freeof non-sulfide gangue mineralisation. So, pyrite is the dominantgangue mineral, and is associated with all other minerals withinthe deposit. Therefore, the properties of pyrite will influencegreatly the behaviour of all other minerals during processing.

2 Spectrum Series 16 Flotation Plant Optimisation

CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT

SENIOR PROJECT METALLURGIST

The key objectives of this position are to:

• identify opportunities for improvement within theprocess,

• develop, test, evaluate and implement processimprovements,

• identify and evaluate new technologies that will improveour process, and

• be actively involved in our future ores testing program.

Operating in a climate of continuous improvement, you willbe required to actively participate in the promotion of safework practices, have good interpersonal skills and aprofessional work ethic.

Skills and experienceThe successful applicant candidate will have the followingattributes:

• Bachelor of Engineering – Metallurgy or equivalent,

• five years or more process experience,

• a working knowledge of grinding and flotationprocesses,

• good communication skills,

• proven problem solving skills,

• good time management skills, and

• have sound leadership qualities and management skills.

Employment conditionsEureka Mining Limited is a wholly owned subsidiary ofStockade Resources Limited, an Australian based miningand exploration company with interests in Australia, NewGuinea, Zambia and Peru.

Reporting to the Metallurgy Manager, the successfulcandidate will work closely with production to maintain andimprove plant performance. This is a residential position,with an attractive renumeration package commensurate withyour qualifications and experience.

To applyPlease submit your application including a cover letter andcurrent copy of your resume via email to ...

The pyrite textures vary markedly across the deposit from coarserecrystallised grains in the footwall-depleted zone to compactmicrocrystalline masses, spongy and colliform clots, such asmelnokovite (an amorphous pyrite of colloidal origin). Ultra-fineintergrowths of pyrite with other sulfides are common, particularlywith galena and arsenopyrite (Figure 1). The association ofauriferous arsenopyrite with pyrite is also of significance.

Process description

Laboratory testing of the Eureka ore clearly demonstrated that itwas possible to produce saleable copper, lead and zincconcentrates. The flow sheet developed in the laboratory wastested at pilot scale to prove that the process route selected wasrobust, and to produce sufficient quantities of concentrate forsmelter testing. The final Eureka process flow sheet is presentedin Figure 2.

As the ore is mined the mine geologists classify it into threebroad ore types based on texture (ie enrichment zone ore(banded), or footwall depleted zone ore (coarse grained)), andestimated pyrite content. Each ore type is crushed in batches tonominally 100 per cent passing 100 mm, in the underground jawcrusher before being trucked to the surface in 50 tonne dumptrucks. Upon delivery to the run-of-mine (ROM) pad each oretype is stockpiled separately. The ore is fed onto a conveyor beltthat leads to an open stockpile in specific ratios of each ore type.Apron feeders, underneath the open stockpile, feed the blendedore onto the primary mill feed conveyor at nominally 120 t/h.

The primary mill is a low aspect ratio semi-autogenousgrinding (SAG) mill in open circuit. The SAG mill product

discharges into a common sump shared with the ball mill. Thepulp is pumped from the mill discharge sump to cyclones inclosed circuit with a secondary ball mill. The cyclone underflowfeeds the secondary ball mill, and the cyclone overflow reports toflotation feed. The cyclone configuration is designed to produce aP80 of 75 microns.

The secondary cyclone overflow feeds a sequential copper/lead/zinc flotation circuit. Each flotation section consists of arougher/scavenger, with the rougher concentrate reporting to thecleaner circuit. The copper cleaning is achieved without regrinding,and with only one stage of cleaning. The lead rougher concentratefeeds the first of three stages of cleaning. The lead scavengerconcentrate and lead first cleaner tailing are reground and recycledback to the head of the lead rougher. The lead scavenger tailingreports to the zinc circuit feed. The zinc rougher concentratereports to two stages of cleaning. The zinc scavenger concentrateand the zinc first cleaner tailing are reground and recycle back tothe zinc rougher feed. The concentrates produced from the copper,lead and zinc flotation circuits are pumped to thickeners. Thethickened concentrate is filtered. The filter cake is stockpiledbefore loading into rail cars for shipment to the smelter.

The flotation tailing is dewatered, and used as paste backfill inthe underground workings.

Metallurgical performance

The Eureka concentrator was commissioned in December 1995,reaching name plate throughput by July 1996. A further 18months were required to achieve the design concentrate gradesand recoveries. Typical metallurgical performance since 1998 issummarised in Table 1.



FIG 1 - Photomicrographs of various galena ore textures: (A) galena replacement in pyrite matrix, like melnokovite (magnification × 10);(B) galena blebs in pyrite matrix (magnification × 20); (C) galena in crystal voids around pyrite (magnification × 5); and (D) galena

replacement in melnokovite colloform (magnification × 40). (Note: The blue/grey areas are galena and the golden areas are pyrite.)

DATA ACQUISITION

The first step towards a better understanding of how your plant isperforming is being able to measure the plant’s performance. Intoday’s modern concentrator the process can be monitored usinga multitude of sensors, however the data collected from inventorysamples on a shift, daily, weekly and monthly basis, coupled withwell executed metallurgical surveys can be invaluable in definingwhere valuable mineral losses occur and what gangue mineralsare diluting the concentrate.

In the first instance, as the new metallurgist you shouldacquaint yourself with the existing plant data. That is, review theshift mass balance data, interrogate the monthly composite data,and examine any plant surveys that have been conducted in thepast. This analysis should provide some indication of where themetallurgical weaknesses lie in the concentrator. However, do notbe surprised if the only data that is up-to-date and readilyavailable are the shift mass balances. Therefore, it should beconsidered good practice to organise for a plant survey to becompleted reasonably early in the piece so that you can quantifythe metallurgical performance of each section of the plant.

The shift data for the copper, lead and zinc circuits of theEureka Concentrator appear in Figure 3 for the first half of 1998.

An examination of these data suggest that with the exception ofthe lead and zinc concentrate grades the plant performance issomewhat unstable. The copper concentrate grade and recoverytend to fluctuate wildly, lead recoveries are highly varied, withgreater than ten per cent zinc grade in the lead concentrate, andzinc recoveries are more often than not in the low to middle 80s.

Your gut feeling when you look more closely at the copper,lead and zinc recoveries should tell you that they appear lowerthan you would expect, therefore it should be possible to improvethe plant performance. But you don’t know what the limitingfactors are so completing a comprehensive plant survey is inorder.

The next item to decide is what level of detail does the surveyhave to go? This will obviously depend on what work has beencompleted previously. In this example it will be assumed that theavailable data is scattered and incomplete. Therefore, theobjective of the survey will be to collect as much data as possibleto provide you with sufficient information to describe the pulpchemistry and metallurgical performance of the plant. Ideallyboth sets of data can be collected in tandem, and complementeach other. To add further value to your survey the collection ofgas hold-up, superficial gas velocity and bubble size data willprovide information about the hydrodynamics of the flotationcells.



Stream Wt % Grade (%) Recovery (%)

Ag (ppm) Cu Pb Zn Ag Cu Pb Zn

Flotation feed 100.0 130 0.4 3.1 12.4 100.0 100.0 100.0 100.0

Cu concentrate 0.9 3190 25.4 5.2 6.2 21.9 53.2 1.5 0.4

Pb concentrate 3.6 1356 1.2 61.5 10.8 37.6 10.1 70.4 3.1

Zn concentrate 20.0 102 0.3 2.0 53.4 15.9 16.4 13.0 86.9

Final tailing 75.3 42 0.1 0.6 1.6 24.6 20.3 15.1 9.6

TABLE 1Typical metallurgical performance of the Eureka Concentrator since 1998.

Lead 3rd Cleaner Concentrate

Grinding

P80 = 75 microns

Zinc Regrinding

P80 = 38 micronsLead Regrinding

P80 = 38 microns

Copper Circuit Lead Circuit Zinc Circuit

Zn Scavenger Tailing

Zinc 2nd Cleaner Concentrate

Copper Cleaner Concentrate

Feed

Cu Ro

Cu Cl

Pb Ro Pb Scav

Pb 1st

Cl

Pb 2nd

Cl

Pb 3rd

Cl

Zn Ro Zn Scav

Zn 1st

Cl

Zn 2nd

Cl

FIG 2 - The Eureka Concentrator flow sheet.

The metallurgical survey (Chapter 2)

Why don’t metallurgists do surveys?

The author has visited many plants around the world, and it isapparent that there is a wide spectrum of knowledge andexperience. However, plants that conduct frequent, well focusedsurveys and have a clear understanding of their metallurgicalperformance, its strengths and weaknesses, are few and farbetween. The reasons for this are many and varied, but generallyboil down to not knowing how to conduct a survey, and the fearof mass balancing!

What do I have to do?

The objectives of the survey(s) are to provide information aboutrougher/scavenger flotation performance in each of the flotationcircuits, and to examine how the concentrates up grade duringcleaning. In the first instance block surveys of each of theflotation sections (roughers, scavengers and cleaners) for each

product should be conducted. Ideally, a more detaileddown-the-bank survey of the rougher/scavenger sections wouldalso be included. An example of a down-the-bank survey isprovided in Appendix 1. Further, to gain greater appreciation ofhow the circuit operates, more than one plant survey should becompleted over a number of days. In fact, a good strategy tofollow would be one detailed survey and several less detailedblock surveys be completed to give a more balanced view of themetallurgical performance of the plant.

Organisation and communication are the key to successfullycompleting surveys in a concentrator. In the first instance, decideon the sampling points and the type of sample to be taken. A listof samples for the Eureka concentrator is given in Table 2. It isimportant to note that all tailing samples are taken in duplicate toensure sampling consistency, and are used as internal checks(note: the recovery calculation is dependent on the tailing assay).



0.0

15.0

30.0

45.0

60.0

75.0

90.0

1/01/

98

15/0

1/98

29/0

1/98

12/0

2/98

26/02

/98

12/03

/98

26/03

/98

9/04

/98

23/04

/98

7/05

/98

21/05

/98

4/06

/98

18/0

6/98

Time, days

12.0

15.0

18.0

21.0

24.0

27.0

30.0

Cu recovery Cu grade

(a)

0.0

15.0

30.0

45.0

60.0

75.0

90.0

1/01/98

15/01/9

8

29/0

1/98

12/0

2/98

26/0

2/98

12/03/

98

26/0

3/98

9/04

/98

23/0

4/98

7/05

/98

21/0

5/98

4/06

/98

18/06/98

Time, days

0.0

15.0

30.0

45.0

60.0

75.0

90.0

Pb recovery Pb grade Zn grade in Pb con

(b)

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1/01/98

15/01/9

8

29/0

1/98

12/0

2/98

26/0

2/98

12/03/

98

26/0

3/98

9/04

/98

23/0

4/98

7/05

/98

21/0

5/98

4/06

/98

18/06/98

Time, days

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Zn recovery Zn grade Fe grade in Zn con

(c)

0.0

15.0

30.0

45.0

60.0

75.0

90.0

1/01/

98

15/0

1/98

29/0

1/98

12/0

2/98

26/02

/98

12/03

/98

26/03

/98

9/04

/98

23/04

/98

7/05

/98

21/05

/98

4/06

/98

18/0

6/98

Time, days

12.0

15.0

18.0

21.0

24.0

27.0

30.0

Cu recovery Cu grade

(a)

0.0

15.0

30.0

45.0

60.0

75.0

90.0

1/01/98

15/01/9

8

29/0

1/98

12/0

2/98

26/0

2/98

12/03/

98

26/0

3/98

9/04

/98

23/0

4/98

7/05

/98

21/0

5/98

4/06

/98

18/06/98

Time, days

0.0

15.0

30.0

45.0

60.0

75.0

90.0

Pb recovery Pb grade Zn grade in Pb con

(b)

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1/01/98

15/01/9

8

29/0

1/98

12/0

2/98

26/0

2/98

12/03/

98

26/0

3/98

9/04

/98

23/0

4/98

7/05

/98

21/0

5/98

4/06

/98

18/06/98

Time, days

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Zn recovery Zn grade Fe grade in Zn con

(c)

Cu

rec

ov

ery

,%

Cu

gra

de

,%

Pb

an

dZ

ng

rad

e,%

Pb

rec

ov

ery

,%

Zn

rec

ov

ery

,%

Zn

an

dF

eg

rad

e,%

FIG 3 - Time series data for: (A) copper; (B) lead; and (C) zinccircuits.

Samplenumber

Process stream Sample type

1 Flotation feed (ball mill cycloneoverflow)

Half moon cutter

2 Copper rougher concentrate Lip sample

3 Copper rougher tailing A Dip sample

4 Copper rougher tailing B Dip sample

5 Copper cleaner concentrate Lip sample or OSA

6 Copper cleaner tailing A Dip sample

7 Copper cleaner tailing B Dip sample

8 Lead rougher concentrate Lip sample

9 Lead rougher tailing A Dip sample

10 Lead rougher tailing B Dip sample

11 Lead scavenger concentrate Lip sample

12 Lead scavenger tailing A Dip sample

13 Lead scavenger tailing B Dip sample

14 Lead first cleaner concentrate Lip sample

15 Lead first cleaner tailing A Dip sample

16 Lead first cleaner tailing B Dip sample

17 Lead second cleaner concentrate Lip sample

18 Lead second cleaner tailing A Dip sample

19 Lead second cleaner tailing B Dip sample

20 Lead third cleaner concentrate Lip sample or OSA

21 Lead third cleaner tailing A Dip sample

22 Lead third cleaner tailing B Dip sample

23 Zinc rougher concentrate Lip sample

24 Zinc rougher tailing A Dip sample

25 Zinc rougher tailing B Dip sample

26 Zinc scavenger concentrate Lip sample

27 Zinc scavenger tailing A Dip sample or OSA

28 Zinc scavenger tailing B Dip sample or OSA

29 Zinc first cleaner concentrate Lip sample

30 Zinc first cleaner tailing A Dip sample

31 Zinc first cleaner tailing B Dip sample

32 Zinc second cleaner concentrate Lip sample or OSA

33 Zinc second cleaner tailing A Dip sample

34 Zinc second cleaner tailing B Dip sample

TABLE 2The sampling points and sample type for the metallurgical survey

of the Eureka flotation circuit.

There are other process streams that you may consider collecting.For example, the copper rougher feed, lead rougher feed, and zincrougher feed, just to name a few, that are redundant samples inthe mass balance, but do provide valuable data for checkingassays, and ensuring that the mass balanced data makes sense.

With the sample list decided upon it is necessary to brief theteam conducting the survey, prepare the equipment, and ensurethat the plant is running in a fashion that will allow it to besurveyed. During this briefing, delegate tasks to each persontaking part in the survey so that:

• sufficient sample buckets with lids are cleaned, weighed andlabelled;

• the sampling equipment is checked, clean and ready for use;

• each person taking part in the survey knows what is expectedof them; and

• set the ground rules for the survey (ie number of rounds overwhat time interval).

Further, it is wise at this point to inform operations of yourintentions to conduct a plant survey(s). Give then the reasons forconducting the surveys and check that the plant will be availableat the time you anticipate the survey(s) will be completed. Youalso need to inform the chemical laboratory that you intend tosubmit a large number of samples from a plant survey, and youwould like to have them assayed for a range of elements.

The day before conducting the survey it is wise to visit the plantand inspect the flotation circuit to make sure that all thoseparticipating in the survey know where the sampling points are thatthey are responsible for. It is also a time when flotation cell lips canbe checked for build-up and cleaned in readiness for the survey. Ofequal importance during the plant inspection is to ensure that thework area is free of hazards, and is safe to work in.

On the day of the survey, attend the morning productionmeeting to establish how the plant has been operating overnightand inform operations of your intentions to survey the plant. Onceyou have the all clear commence setting up for the survey, set outthe buckets in the correct positions, station the samplingequipment accordingly, check the flotation cell lips and cleanthem again. Check again that your team is familiar with the taskahead of them.

Now that you are ready, go to the control room and check onthe status of the concentrator. Ensure that the feed tonnage issteady, the feed grades are steady, and the circuit has notexperienced a major disturbance in the last few hours. Once youare satisfied that the plant is running smoothly you can start thesurvey.

It is suggested that a survey of this type be conducted over anumber of hours, whereby multiple rounds of samples are collectedto form a composite of the sampling period. In this particular case,the survey was conducted over a three hour period, during whichtime four ‘cuts’ from each sampling point were collected randomly.Once the metallurgical survey is completed, the samples aregathered together and taken to the laboratory.

What data do I need?

In the laboratory the samples are weighed (to determine the wetweight), filtered, dried, weighed, prepped and submitted forassay. A summary of this data is given in Table 3. The wet anddry weights are used to determine the per cent solids of eachsample, and calculate a water balance.

In terms of assays, apart from those pertaining to the valuableminerals you are separating from the gangue (in Eureka’s case –copper, lead, zinc, silver and gold), it is also wise to assay forother elements. For example, iron and sulfur, to aid in performingmineral conversions so that information about the sulfide ganguecan be extracted from the elemental assays, and deleterious trace

elements like arsenic, antimony, bismuth, mercury and/orcadmium. Knowing how these penalty elements deport to theconcentrate can lead to a way of minimising their recovery, whichtranslates to a potential decrease in smelter penalties.

Other data that can be useful when analysing the plant surveymass balance are:

• the throughput at the time of the survey,

• reagent additions and other plant operating parameters (ie airflow rates and pulp levels),

• OSA readings, and

• information about the ore being treated.

In the context of a one off survey some of these pieces ofinformation may not be of great value. However, when theanalysis is extended to include other surveys on other ore blends,circuit configurations, reagent suites, these data provide a vitallink in the comparison.

How do I analyse the data collected (Chapter 3)?

Once the assays have returned from the chemical laboratory thefun starts! You will be confronted with a list of numbers notunlike that presented in Table 4. Initially, this may be a littledaunting, but once you have organised the data into a logicalformat, and brought order to the chaos you will be in a position tomass balance this survey.

At the beginning of the mass balancing process it is necessary tomake sure that you have received all the assays and that they are ingood order. That is, do the tailing assays match? Do theconcentrate assays follow a logical trend (for example, the rougherconcentrate grade is higher than the scavenger concentrate, thecleaner concentrate grades increase as you pass from the first to thethird cleaner)? Once you have satisfied yourself that the assays aregood you can start the mass balancing process.

Remember, if a water balance of the flotation circuit is requiredthis can be achieved using the per cent solid values for eachprocess stream. That is, you treat the per cent solids as an assay,and include it in your mass balance calculations.

There are numerous mass balancing packages available, but thebasic steps involved in completing the mass balance are the samefor all. Unfortunately, it is not a case of plugging the numbers inand pressing ‘GO’, as invariably this leads to process streamshaving zero flow!

The basic steps to following when mass balancing a survey aregiven below. A more detailed explanation is given in Chapter 3:Mass Balancing Flotation Data by Rob Morrison.

Step 1

Determine what the plant throughput was when the survey wascompleted. If the feed tonnage is not available, assign the freshflotation feed a value of 100 per cent. It is further assumed thatyou have a high level of confidence in this value.

Step 2

Balance the ‘outer’ circuit. That is, complete a mass balance ofthe feed, final concentrate and tailing for the copper, lead andzinc circuits (Figure 4). This balance will provide estimates of thetonnes of copper, lead and zinc concentrates produced. Thesevalues can be used in subsequent balances to estimate tonnages ofthe internal process streams within rougher, scavenger andcleaner circuits.

Assign each process stream a number, and identify each of thenodes. So, the process streams are:

1. flotation feed,



2. copper cleaner concentrate,

3. copper rougher tailing,

4. lead third cleaner concentrate,

5. lead scavenger tailing,

6. zinc second cleaner concentrate, and

7. zinc scavenger tailing.



Samplenumber

Process stream Sample weight (g) % Solids

Gross wet Bucket Cutter Net wet Dry

1 Flotation feed 2083.5 202.6 1880.9 909.8 48.4

2 Copper rougher concentrate 1961.2 229.2 347.8 1384.2 708.2 51.2

3 Copper rougher tailing A 2509.5 466.3 2043.2 949.7 46.5

4 Copper rougher tailing B 2480.9 466.0 2014.9 936.1 46.5

5 Copper cleaner concentrate 1386.7 225.0 324.8 836.9 536.3 64.1

6 Copper cleaner tailing A 1622.0 202.6 1419.4 144.8 10.2

7 Copper cleaner tailing B 1668.4 202.6 1465.8 141.8 9.7

8 Lead rougher concentrate 4842.2 225.3 341.0 4275.9 2695.1 63.0

9 Lead rougher tailing A 2395.6 464.3 1931.3 812.3 42.1

10 Lead rougher tailing B 2374.1 477.7 1896.4 772.0 40.7

11 Lead scavenger concentrate 1317.1 206.9 340.9 769.3 482.4 62.7

12 Lead scavenger tailing A 2416.1 465.0 1951.1 823.7 42.2

13 Lead scavenger tailing B 2357.1 446.3 1910.8 782.0 40.9

14 Lead first cleaner concentrate 10638.1 190.4 341.1 10106.6 6660.8 65.9

15 Lead first cleaner tailing A 2646.2 234.5 2411.7 1347.1 55.9

16 Lead first cleaner tailing B 2640.5 224.8 2415.7 1343.7 55.6

17 Lead second cleaner concentrate 5832.8 214.4 325.3 5293.1 3547.9 67.0

18 Lead second cleaner tailing A 2814.2 233.6 2580.6 1608.0 62.3

19 Lead second cleaner tailing B 2828.9 228.2 2600.7 1424.7 54.8

20 Lead third cleaner concentrate 2721.8 200.6 341.0 2180.2 1395.7 64.0

21 Lead third cleaner tailing A 2905.2 238.1 2667.1 1635.1 61.3

22 Lead third cleaner tailing B 2881.6 225.8 2655.8 1650.7 62.2

23 Zinc rougher concentrate 2779.3 226.4 341.2 2211.7 1287.8 58.2

24 Zinc rougher tailing A 2090.7 224.9 1865.8 681.5 36.5

25 Zinc rougher tailing B 1992.0 234.7 1757.3 638.8 36.4

26 Zinc scavenger concentrate 1565.6 200.4 324.9 1040.3 500.5 48.1

27 Zinc scavenger tailing A 1983.0 234.9 1748.1 629.1 36.0

28 Zinc scavenger tailing B 1753.0 225.5 1527.5 536.5 35.1

29 Zinc first cleaner concentrate 3945.3 202.4 341.2 3401.7 1947.7 57.3

30 Zinc first cleaner tailing A 2316.8 225.6 2091.2 1004.9 48.1

31 Zinc first cleaner tailing B 2321.4 225.4 2096.0 1005.5 48.0

32 Zinc second cleaner concentrate 4209.1 211.9 341.2 3656.0 2020.2 55.3

33 Zinc second cleaner tailing A 2466.7 225.5 2241.2 1250.5 55.8

34 Zinc second cleaner tailing B 2474.3 234.0 2240.3 1248.2 55.7

TABLE 3Raw data from the metallurgical survey of the Eureka Concentrator.

Cu Circuit Zn CircuitPb Circuit1. Flotation feed

2. Cu Clnr Con

(Final Cu Con) (Final Zn Con)(Final Lead Con)

7. Zn Scan Tail

(Final Tail)

3. Cu scav tailing 5. Pb scav tailing

(1) (2) (3)

4. Pb 3 Clnr Conrd 6. Zn 2 Clnr Connd

FIG 4 - The ‘outer’ circuit.

The nodes are:

1. flotation feed = Cu cleaner concentrate + Cu rougher tailing(or, 1 = 2 + 3);

2. Cu rougher tailing = Pb third cleaner concentrate + Pbscavenger tailing (or, 3 = 4 + 5); and

3. Pb scavenger tailing = Zn second cleaner concentrate + Znscavenger tailing (or, 5 = 6 + 7).

In this case, at the time of surveying the plant, throughput was120 t/h.

An examination of the tailing assays reveals that the assays arereasonably similar and in the expected range (indicating that thesampling was of a good standard). Therefore, taking an averageof the two tailing sample assays is acceptable. However, if oneassay was considerably higher than the other (for example, thecopper cleaner tailing A, in Table 4), one suspects that this dipsample has been contaminated with froth during collection, and

this assay would be rejected from the data set. It is likely thathaving this sample reassayed would only confirm that the samplewas contaminated during collection. However, care must be takenwhen rejecting assays because it may not always the case. Thissample has a similar assay to that obtained for the copper rougherconcentrate, so another possibility may be that samples have beenwrongly labelled.

Having sorted the data, identified the process streams, nodesand feed tonnage, the numbers can be plugged into your massbalancing program. In this case MATBAL, using a Monte Carlosimulation, was employed. The resultant mass balance isprovided in Table 5, which includes a sigma value. If sigma isless than five per cent, then the data are considered to be good.

Step 3

The next step in the mass balance is to balance the internal, or‘inner’ circuits within the copper, lead and zinc flotation circuits.



Samplenumber

Process stream % Solids Raw assays

Ag (ppm) Cu (%) Pb (%) Zn (%) Fe (%)

1 Flotation Feed 48.4 135 0.43 4.02 15.80 14.30

2 Copper rougher concentrate 51.2 588 16.50 15.20 12.30 14.20

3 Copper rougher tailing A 46.5 92 0.19 4.04 15.40 13.40

4 Copper rougher tailing B 46.5 98 0.21 4.04 15.60 13.60

5 Copper cleaner concentrate 64.1 229 22.40 4.07 7.80 16.10

6 Copper cleaner tailing A 10.2 524 15.45 16.30 11.30 13.80

7 Copper cleaner tailing B 9.7 424 5.50 26.40 18.50 9.00

8 Lead rougher concentrate 63.0 184 0.32 21.42 29.97 8.97

9 Lead rougher tailing A 42.1 43 0.16 1.22 17.90 13.60

10 Lead rougher tailing B 40.7 34 0.16 1.25 16.60 13.50

11 Lead scavenger concentrate 62.7 179 0.36 8.16 39.84 11.57

12 Lead scavenger tailing A 42.2 38 0.14 0.67 15.20 13.90

13 Lead scavenger tailing B 40.9 18 0.15 0.50 14.60 13.70

14 Lead first cleaner concentrate 65.9 600 0.30 44.68 14.10 4.10

15 Lead first cleaner tailing A 55.9 184 0.28 18.50 30.65 8.80

16 Lead first cleaner tailing B 55.6 176 0.28 20.50 30.75 9.00

17 Lead second cleaner concentrate 67.0 960 0.51 50.90 10.10 2.50

18 Lead second cleaner tailing A 62.3 814 0.23 47.30 13.40 3.85

19 Lead second cleaner tailing B 54.8 800 0.27 47.40 13.60 3.95

20 Lead third cleaner concentrate 64.0 1060 1.08 60.00 9.40 1.70

21 Lead third cleaner tailing A 61.3 890 0.32 47.00 11.26 2.40

22 Lead third cleaner tailing B 62.2 900 0.40 49.00 11.34 2.80

23 Zinc rougher concentrate 58.2 53 0.33 1.63 45.10 8.40

24 Zinc rougher tailing A 36.5 16 0.13 0.45 1.72 15.35

25 Zinc rougher tailing B 36.4 15 0.13 0.55 1.74 15.65

26 Zinc scavenger concentrate 48.1 72 0.47 2.23 15.20 14.30

27 Zinc scavenger tailing A 36.0 12 0.09 0.32 0.85 15.32

28 Zinc scavenger tailing B 35.1 13 0.11 0.36 0.87 15.48

29 Zinc first cleaner concentrate 57.3 43 0.33 1.30 55.70 6.00

30 Zinc first cleaner tailing A 48.1 63 0.38 2.02 19.40 12.10

31 Zinc first cleaner tailing B 48.0 60 0.42 1.94 19.60 12.30

32 Zinc second cleaner concentrate 55.3 39 0.29 1.17 58.40 5.30

33 Zinc second cleaner tailing A 55.8 62 0.48 1.60 42.70 8.50

34 Zinc second cleaner tailing B 55.7 64 0.44 1.64 44.70 8.50

TABLE 4Raw assay data from the metallurgical survey of the Eureka Concentrator.

These internal balances are broken down into ‘bite size’ pieces tosimplify the balancing process, and generate estimates oftonnages that can be used in subsequent ‘inner’ circuit balances.

The copper circuit (Figure 5) is comparatively easy to balanceusing the tonnage estimates for the flotation feed and coppercleaner concentrate generated in the ‘outer’ circuit mass balance.In this case the process streams are:

1. flotation feed (120 00 t/h),

2. copper rougher concentrate,


4. copper cleaner concentrate (1.21 t/h), and

5. copper cleaner tailing.

The nodes are:

1. flotation feed + Cu cleaner tailing = Cu rougher concentrate+ Cu rougher tailing (or, 1 + 5 = 2 + 3); and

2. Cu rougher concentrate = Cu cleaner concentrate + Cucleaner tailing (or, 2 = 4 + 5).

The resultant mass balance assays are provided in Table 6.A similar approach is adopted for the lead circuit (Figure 6)

where the circuit is balanced in four parts:

1. the lead second and third cleaners,

2. the lead first cleaner,

3. the lead scavenger, and

4. the lead rougher/cleaner circuit.

While this arrangement may appear to be counter intuitive, thereasons for moving backwards through the lead circuit are drivenby the need to estimate tonnage figures for the recycle streams.

In Balance 1 (the lead second and third cleaners), by using thethird cleaner concentrate estimated tonnage generated in the‘outer’ mass balance in Step 2 it is possible to estimate thetonnage for the lead first cleaner concentrate and the lead secondcleaner tailing. Using these two values, it is then possible to massbalance the lead first cleaner. This will give tonnage estimates forthe lead rougher concentrate and the lead first cleaner tailing.

Balance 3 uses the tonnage estimate for the lead scavengertailing determined in the ‘outer’ balance in Step 2. The massbalance of the lead scavengers yields an estimate of the tonnagefor the lead scavenger concentrate.

Once these three ‘inner’ mass balances are complete it ispossible to fix certain tonnages (ie copper rougher tailing, leadscavenger tailing, lead first, second and third cleaner concentrates),and complete a mass balance of the lead circuit. The processstreams used for this balance are:


2. lead rougher concentrate,

3. lead rougher tailing,

4. lead scavenger concentrate,


6. lead first cleaner concentrate,

7. lead first cleaner tailing,

8. lead second cleaner concentrate,

9. lead second cleaner tailing,

10. lead third cleaner concentrate, and

11. lead third cleaner tailing.



No Stream t/h Adjusted assays (%)

Ag (ppm) Cu Pb Zn Fe

1 Flotation feed 120.00 0.09 135 0.43 3.99 15.04 13.17

2 Cu cleaner concentrate 1.21 3.31 229 22.40 4.07 7.80 16.11

3 Cu rougher tailing 118.79 0.10 95 0.21 3.99 15.12 13.14

4 Pb 3rd cleaner concentrate 6.76 2.19 1060 1.07 61.01 9.42 1.70

5 Pb scavenger tailing 112.03 0.16 28 0.15 0.55 15.46 13.83

6 Zn 2nd cleaner concentrate 27.70 2.14 39 0.29 1.18 59.92 5.35

7 Zn scavenger tailing 84.33 0.73 12 0.11 0.34 0.86 16.62

TABLE 5Adjusted assays for the ‘outer’ circuit mass balance.

Cu rougher (1)

Cu cleaner (2)

1. Flotation feed

4. Cu cleaner concentrate

(Final Cu Con)

5. Cu cleaner tailing2. Cu rougher concentrate

3. Cu rougher tailing

FIG 5 - The copper circuit ‘inner’ mass balance.

And, the nodes are:

1. Cu rougher tailing + Pb scavenger concentrate + Pb firstcleaner tailing = Pb rougher concentrate + Pb roughertailing (or, 1 + 4 + 7 = 3 + 3);

2. Pb rougher tailing = Pb scavenger concentrate + Pbscavenger tailing (or, 3 = 4 + 5);

3. Pb rougher concentrate + Pb second cleaner tailing = Pbfirst cleaner concentrate + Pb first cleaner tailing (or, 2 + 9= 6 + 7);

4. Pb first cleaner concentrate + Pb third cleaner tailing = Pbsecond cleaner concentrate + Pb second cleaner tailing (or,6 + 11 = 8 + 9); and

5. Pb second cleaner concentrate = Pb third cleanerconcentrate + Pb third cleaner tailing (or, 8 = 10 + 11).

The mass balanced assays for the lead circuit are given inTable 7.

The same approach is employed when mass balancing the innercircuits for zinc flotation (Figure 7). The first balance (Balance 1)examines the zinc first and second cleaner, while the second mass

balancing (Balance 2) step involves the zinc scavenger circuit.When these two internal mass balances are complete, it ispossible to mass balance the zinc circuit while fixing certainflows. The process streams used for this balance are:


2. zinc rougher concentrate,

3. zinc rougher tailing,

4. zinc scavenger concentrate,

5. zinc scavenger tailing,

6. zinc first cleaner concentrate,

7. zinc first cleaner tailing,

8. zinc second cleaner concentrate, and

9. zinc second cleaner tailing.

And, the nodes are:

1. Pb scavenger tailing + Zn scavenger concentrate + Zn firstcleaner tailing = Zn rougher concentrate + Zn roughertailing (or, 1 + 4 + 7 = 3 + 3);



5. Pb scavenger tailing3. Pb rougher tailing1. Cu rougher tailingPb rougher

(1)

Pb scavenger

(2)

Pb 1st cleaner

(3)Pb 1st cleaner feed 7. Pb 1st cleaner tailing

Pb rougher feed

4. Pb scavenger concentrate

Pb 2nd cleaner

(4)

Pb 3rd cleaner

(5)

10. Pb 3rd cleaner concentrate

(Pb final con)

2. Pb rougher concentrate

6. Pb 1st cleaner concentrate

Pb 2nd cleaner feed

8. Pb 2nd cleaner concentrate

9. Pb 2nd cleaner tailing

11. Pb 3rd cleaner tailingPb 3rd cleaner feed

Balance 1

Balance 3

Balance 2

FIG 6 - The ‘inner’ mass balances for the lead circuit.


Ag (ppm) Cu Pb Zn Fe

1 Flotation feed 120.00 0.10 135 0.44 3.99 15.04 13.17

2 Cu cleaner con 2.39 1.23 588 14.78 15.20 12.75 13.10


4 Cu cleaner con 1.21 0.09 229 23.63 4.14 7.71 16.83

5 Cu cleaner tailing 1.18 2.52 424 5.59 26.69 18.00 9.22

TABLE 6Adjusted assays for the copper circuit ‘inner’ mass balance.

2. Zn rougher tailing = Zn scavenger concentrate + Znscavenger tailing (or, 3 = 4 + 5);

3. Zn rougher concentrate + Zn second cleaner tailing = Znfirst cleaner concentrate + Zn first cleaner tailing (or, 2 + 9= 6 + 7); and

4. Zn first cleaner concentrate = Zn second cleaner concentrate+ Zn second cleaner tailing (or, 6 = 8 + 9).

The adjusted assays for the zinc circuit are provided in Table 8.

Step 4

With the inner mass balances complete it is now possible to massbalance the whole circuit. The resultant mass balance is given inTable 9. When reviewing your mass balance it is always a goodidea to check that the internal workings of the circuit arebalanced. For example, in the copper circuit, the feed to the

copper rougher is actually the fresh flotation feed plus the coppercleaner tailing. The sum of these recovery values should be thesame as the copper rougher concentrate plus the tailing. That is,for copper:

Rflotation feed + RCu cleaner tailing = RCu rougher con + RCu rougher tailing

100.00 + 12.54 = 66.15 + 46.39

112.54 = 112.54

The balance holds, so we can have confidence that the massbalancing calculations have been completed correctly.

What does it mean? (Chapter 2)

With the mass balance completed it is time to analyse the data anddetermine what it means. From this analysis it is possible toestablish what and where the weaknesses are in the circuit, and




Ag Cu Pb Zn Fe


2 Pb rougher con 134.72 1.66 208 0.32 21.53 29.91 8.81

3 Pb rougher tailing 122.16 0.09 40 0.16 1.23 17.25 13.68

4 Pb scavenger con 10.14 1.34 179 0.36 8.20 39.79 11.55


6 Pb 1st cleaner con 110.85 0.10 631 0.30 44.67 13.63 3.91

7 Pb 1st cleaner tailing 127.95 1.17 162 0.28 19.48 30.99 9.18

8 Pb 2nd cleaner con 31.98 0.09 940 0.51 50.72 10.54 2.45

9 Pb 2nd cleaner tailing 104.08 0.11 602 0.25 43.65 13.91 4.06

10 Pb 3rd cleaner con 6.75 0.09 1077 1.08 60.39 9.35 1.71

11 Pb 3rd cleaner tailing 25.23 0.11 903 0.36 48.13 10.86 2.65

TABLE 7Adjusted assays for the ‘inner’ lead circuit mass balance.

5. Zn scavenger tailing3. Zn rougher tailing1. Pb rougher tailingZn rougher

(1)

Zn scavenger

(2)

Zn 1st cleaner

(3)Zn 1st cleaner feed 7. Zn 1st cleaner tailing

Zn rougher feed

4. Zn scavenger concentrate

Zn 2nd cleaner

(4)

8. Zn 2nd cleaner concentrate

(Zn final con)

2. Zn rougher concentrate

6. Zn 1st cleaner concentrate

Zn 2nd cleaner feed 9. Zn 2nd cleaner tailing

Balance 1

Balance 2

FIG 7 - The ‘inner’ mass balances for the zinc circuit.

determine what additional tests are required to give more definitionto the data. As the mass balance was completed using elementalassays it is now possible to use this data to calculate what ishappening on a mineral basis. That is, the mass balancedelemental assays can be converted to minerals by making certainassumptions about the elemental composition of the variousminerals of interest within your system. Appendix 2 provides anexample of these element to mineral calculations. The conversionto minerals allows you to examine the flotation behaviour of theiron sulfide and non-sulfide gangue species. Combining thecalculated non-sulfide gangue mass balanced data with the waterrecovery data it is possible to gain an appreciation of how theseminerals are being recovered (ie entrainment). Chapter 2: Existing

Methods for Process Analysis by Bill Johnson provides a detaileddescription of how to analyse and interpret your plant survey data.

The copper grade/recovery curve for the copper circuit appearsin Figure 8. This data, in conjunction with Table 9, indicates thatthere is a small circulating load (about 12 per cent) of copperreturning to the rougher feed via the copper cleaner tailing. Thisis not a major concern. However, the poor selectivity forchalcopyrite against galena and sphalerite during copper roughing(ie the high lead and zinc grades) is an issue, and requires furtherinvestigation.

Figure 9 contains the lead grade/recovery curve for the Eurekacircuit. It is immediately obvious that there is a large circulatingload of galena within the lead circuit centred around the lead first




Ag Cu Pb Zn Fe


2 Zn rougher con 43.74 0.07 53 0.33 1.52 44.32 7.98

3 Zn rougher tailing 89.83 0.18 16 0.13 0.47 1.74 15.69

4 Zn scavenger con 5.51 2.56 72 0.47 2.26 15.17 14.29

5 Zn scavenger tailing 84.32 0.09 12 0.11 0.36 0.86 15.78

6 Zn 1st cleaner con 37.44 0.09 43 0.33 1.32 54.80 6.15

7 Zn 1st cleaner tailing 16.04 0.10 63 0.40 2.04 19.55 12.53

8 Zn 2nd cleaner con 27.70 0.09 39 0.29 1.22 58.66 5.34

9 Zn 2nd cleaner tailing 9.74 0.47 64 0.46 1.61 43.84 8.42

TABLE 8Adjusted assays for the ‘inner’ zinc circuit mass balance.

No Stream t/h Cu (%) Pb (%) Zn (%) Fe (%)

Grade Recovery Grade Recovery Grade Recovery Grade Recovery

1 Flotation feed 120.00 0.05 0.43 100.00 4.00 100.00 14.92 100 13.06 100.00

2 Cu rougher con 2.39 0.41 14.29 66.15 15.10 7.50 12.76 1.70 13.10 1.99

3 Cu rougher tailing 118.79 0.05 0.20 46.39 4.00 98.97 14.99 99.48 13.02 98.69

4 Cu cleaner con 1.21 0.11 22.74 53.61 4.07 1.03 7.71 0.52 16.84 1.31

5 Cu cleaner tailing 1.18 0.84 5.52 12.54 26.54 6.47 17.99 1.18 9.22 0.69

6 Pb rougher con 134.72 3.12 0.32 85.07 21.44 611.75 29.86 228.56 8.84 77.31

7 Pb rougher tailing 122.16 0.23 0.17 39.52 1.24 31.39 17.40 119.05 13.52 105.65

8 Pb scav con 10.14 3.00 0.36 7.25 8.17 17.72 39.71 23.11 11.58 7.70

9 Pb scav tailing 112.02 0.10 0.14 32.27 0.58 13.67 15.33 95.94 13.70 97.95

10 Pb 1st cleaner con 110.85 0.10 0.30 64.56 44.68 1030.87 13.63 84.37 3.91 27.69

11 Pb 1st cleaner tailing 127.95 2.27 0.28 70.95 19.41 526.44 30.92 225.02 9.21 76.58

12 Pb 2nd cleaner con 31.98 0.10 0.51 31.72 50.75 337.91 10.54 18.84 2.45 5.00

13 Pb 2nd cleaner tailing 104.08 0.11 0.25 50.44 43.64 945.57 13.91 80.84 4.06 26.95

14 Pb 3rd cleaner con 6.75 0.09 1.08 14.12 60.64 85.30 9.36 3.53 1.71 0.74

15 Pb 3rd cleaner tailing 25.23 0.13 0.36 17.60 48.10 252.61 10.86 15.30 2.65 4.26

16 Zn rougher con 43.74 0.39 0.33 27.91 1.48 13.51 44.77 109.42 8.00 22.33

17 Zn rougher tailing 89.93 0.24 0.13 21.96 0.50 9.38 1.74 8.75 16.30 93.53

18 Zn scav con 5.51 3.59 0.47 5.12 2.23 2.58 15.14 4.71 14.26 5.07

19 Zn scav tailing 84.32 0.07 0.10 16.85 0.39 6.79 0.86 4.04 16.44 88.46

20 Zn 1st cleaner con 37.44 0.09 0.33 24.19 1.30 10.16 55.33 115.74 6.17 14.74

21 Zn 1st cleaner tailing 16.04 1.07 0.40 12.49 1.99 6.64 19.52 17.52 12.51 12.83

22 Zn 2nd cleaner con 27.70 0.09 0.29 15.42 1.19 6.88 59.41 91.91 5.38 9.50

23 Zn 2nd cleaner tailing 9.74 0.38 0.46 8.76 1.62 3.28 43.76 23.83 8.41 5.23

TABLE 9Mass balance of copper, lead and zinc circuits of the Eureka Concentrator.

cleaner bank. A closer examination of the lead circuit massbalanced data in Table 9 reveals that accompanying the very largecirculating loads of galena is a large circulating load of sphalerite.This would suggest that there may be liberation issues within thispart of the circuit. Again, this warrants further investigation.

The zinc grade/recovery curve (Figure 10) also reveals that thereare circulating loads of zinc around the zinc first cleaner. While

these loads are not as severe as those noted in the lead circuit,undoubtedly they are having an impact on the zinc metallurgy.Consulting Table 9, it was noted that there may be an issue withiron, particularly around the rougher/scavenger/first cleaner circuit.Further investigation is again needed to define the problem.

It is apparent that there are opportunities for improvement in allthree circuits. Equally, it is obvious that making improvements in



0.0

5.0

10.0

15.0

20.0

25.0

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Cu recovery, %

Cu

gra

de

,%

Flotation feed

Cu cleaner concentrate

Cu rougher concentrate

Cu rougher feed

FIG 8 - The copper grade/recovery curve for the copper circuit within the Eureka Concentrator.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

0.0 200.0 400.0 600.0 800.0 1000.0 1200.0 1400.0 1600.0 1800.0

Pb recovery, %

Pb

gra

de

,%

Flotation feed

Pb 3rd

cleaner concentrate

Pb 2nd

cleaner concentrate

Pb 2nd

cleaner feed

Pb 1st

cleaner concentrate

Pb 1st

cleaner feed

Pb rougher concentrate

Pb rougher feedCu rougher tailing

FIG 9 - The lead grade/recovery curve for the lead circuit within the Eureka Concentrator.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

40.0 60.0 80.0 100.0 120.0 140.0 160.0

Zn recovery, %

Zn

gra

de,

%

Flotation feed

Pb rougher tailing

nd

st

stZn rougher concentrate

Zn rougher feed

Cu rougher tailing

FIG 10 - The zinc grade/recovery curve for the zinc circuit within the Eureka Concentrator.

the copper circuit will have ramifications on both the lead andzinc circuits. And this makes for a good rule of thumb: start at thefront of the circuit and work downstream. The test programs needto be prioritised so that resources can be focused on the projectthat has the greatest potential metallurgical (and financial) benefitto the plant. The survey data indicates that the lead circuit is veryunstable with high circulating loads, and could be impeding boththe lead and zinc circuit performance. It is recommended thatfurther work be completed to determine the recovery-by-size andliberation characteristics through the lead circuit to see whereimprovements may be made.

Sample identification and storage

At the risk of stating the obvious, having a workable sampleidentification and storage system will make life easier. A lack ofattention to detail in this area will lead to mistakes in the assaylaboratory with poor labelling, and can jeopardise the ability tolocate samples for check assaying or for further testing (forexample, sizing or mineralogical examination). Therefore, it isnecessary to set up a system of identification that is both simpleand effective. For example, all surveys are labelled with an ‘S’and numbered consecutively, with each sample in that surveygiven a number. So, if the survey described in this chapter is thetenth survey completed in the Eureka Concentrator itsidentification code would be S10/1 to S10/34, where the samplenumbers 1 to 34 correspond to the sample numbers listed inTable 2. The next survey would be S11, and so on. Flotation testscould be identified according to the operator’s initials, forexample, CG351/1 to CG351/5, which would be read as flotationtest 351 completed by CG with five test products.

Once a numbering system has been put in place it makes iteasy to develop a storage system. In developing your storagesystem you must set some rules regarding the length of time youare going to keep a sample, and then allow the laboratorytechnicians time, periodically, to maintain the storage facility. Forexample, it is wise to keep flotation test produces for nominallythree months and plant surveys for up to 12 months. It is alsowise have a clean up every three months to dispose of samplesthat have passed their storage date.

The pulp chemistry survey (Chapter 6)

Concurrent to the metallurgical survey, pulp chemical data wascollected from the plant to determine the pulp chemical conditions.


The pulp chemical survey involves collecting Eh, pH, dissolvedoxygen, and temperature data from the following process streams:

• SAG mill discharge,

• cyclone underflow,

• ball mill discharge,

• cyclone overflow,

• copper circuit feed,

• lead circuit feed,

• zinc circuit feed, and

• final tailings.

To complete a pulp chemical survey, a sample of slurry is ‘cut’from the process stream of interest, and poured into a smallbeaker. The sample is then stirred gently with the probes fornominally two minutes until equilibrium readings are obtained.The Eh, pH, dissolved oxygen, and temperature data are logged

on a data logger. The logged data can then be downloaded fromthe data logger to a laptop computer where it is able to bemanipulated.

All of the probes should be checked to ensure they are cleanand in good working order, and it is imperative that they arecalibrated before use. The pH probe should be calibrated usingpH buffer solutions seven and ten, if this is the range expected inthe plant. Alternative buffer solutions should be used if the pulppH is outside this range. The Eh probe should be calibrated, forexample, using Zobell solution (1:1 solution of Part A and B =231 mV at 24°C), the dissolved oxygen probe calibrated in a0.2 g/L solution of sodium sulfite for the zero calibration, and air.The temperature probe can be calibrated with the aid of athermometer, using iced and heated water to give a two pointcalibration.

To complete the data set, EDTA extractions should becompleted on the same process streams as the pulp chemistry. Forthe Eureka survey, each stream was ‘cut’ and the sample pouredinto a small wide mouth, screw top sample bottle. The sampleswere taken back to the laboratory, and the wet weight recorded.The EDTA extraction procedure used was:

• stir the pulp sample with a 25 ml syringe to producehomogeneous slurry;

• syringe a 25 ml aliquot of slurry from the sample bottle andweigh;

• inject the contents of the syringe into a 400 ml beakercontaining 250 ml of three per cent (by weight) EDTAsolution, pH modified to 7.5 with sodium hydroxide;

• thoroughly mix the slurry and EDTA solution using amagnetic stirrer, for five minutes;

• filter the slurry using a 0.2 micron millipore filter; and

• submit the filtered EDTA solution for assay.

The remainder of the pulp sample was pressure filtered, and thesolids dried. The dry solids were weighed and submitted forassay.


The pulp chemical data (pH, Eh, dissolved oxygen andtemperature) are stored on a data logger. These data now need tobe downloaded into Excel, massaged and put into a logical formatfor analysis and interpretation.

The samples for EDTA extraction (solids and liquids) areprepared and submitted for assay. Usually these samples areassayed for the base metals of interest; in the case of the EurekaConcentrator, the solids and liquids were assayed for copper,lead, zinc and iron.

As with the metallurgical surveys, other data that can be usefulwhen analysing the EDTA survey are:


• reagent additions and other plant operating parameters (ie airflow rates and pulp levels),



In the context of a one off survey some of these pieces ofinformation may not be of great value. However, when the analysisis extended to include other surveys on other ore blends, circuitconfigurations and reagent suites, these data provide a vital link inthe comparison.

It can be beneficial to collect process water samples routinelyto determine the free ions in solution (ie base metal ions (copper,lead, zinc, iron), calcium, magnesium, sulfate and chloride).



How do I analyse the data?

With the exception of the Eh data the other pulp chemicalmeasurements can be read directly from the measured data. TheEh should be adjusted so that it is referenced against the standardhydrogen electrode (SHE). Chapter 6: Chemical MeasurementsDuring Plant Surveys and Their Interpretation by Stephen Granoprovides an explanation of how to manipulate and analyse thedata you have collected.

One method of analysing the EDTA data is given by Rumballand Richmond (1996), who developed a simple relationship forcalculating the percentage of oxidised mineral present in a pulpfrom EDTA extraction data (Equation 1):

% EDTA extractable MMass of M in EDTA solution

Mass of M in so=

lids× 100 (1)

where:

M is the metal ion under investigation

The data is generally presented graphically with the pulpchemical parameter on the y-axis and the circuit position on thex-axis. The process streams are set to mimic the normal flow ofthe slurry through the circuit. In this case:

1. SAG mill discharge,

2. cyclone underflow,

3. ball mill discharge,

4. cyclone overflow,

5. copper rougher feed,

6. lead rougher feed,

7. zinc rougher feed, and

8. final tailing.

When investigating the pulp chemistry within your circuit oftenthe urge is to sample every stream in the concentrator. While thismay be a good idea initially to get an idea of the variationsbetween process streams, it soon becomes apparent that some ofthis data is redundant. For example, concentrate streams usuallyhave high dissolved oxygen contents and very oxidising pulppotentials because the froth in the concentrate contains significantamounts of air. Therefore, when conducting a pulp chemistrysurvey of the primary grinding/rougher flotation circuitmeasuring the pulp chemistry of the rougher concentrate canproduce numbers that skew the analysis. However, if you are

examining the pulp chemistry of a regrind/cleaner circuit, it isnormal to collect a sample of the fresh concentrate that feeds theregrinding section. It is also good policy when completing surfaceanalysis comparing pairs of streams (ie rougher concentrate androugher tailing), to collect the pulp chemistry of the two processstreams of interest.

What does it mean?

The pH, Eh, dissolved oxygen, and temperature data should beplotted in such a way to represent the pulp flow profile throughthe circuit to assist with the analysis and interpretation of thedata. The profiles through the Eureka circuit are displayed inFigures 11 to 14, respectively.

The pH profile (Figure 11) indicated that grinding occurs atnatural pH (in the range 8.0 to 8.5). The slight reduction in pH inthe ball mill discharge maybe attributed to pyrite oxidation.Sodium metabisulfite (SMBS) was added to the copper rougherfeed, to depress galena during copper flotation. The addition ofSMBS resulted in the pH being reduced to approximately 6.0.The pH was then increased to 8.0 for galena flotation and 11.5sphalerite flotation thought the addition of lime.

Figure 12 contains the Eh profile through the circuit. The Ehshifted to slightly less oxidising pulp potentials as the pulpflowed from the SAG mill through the ball mill, and becamemore oxidising during flotation where air is used as the flotationgas.

The dissolved oxygen profile (Figure 6) through the circuitindicated that the oxygen content of the pulp was negligibleduring both grinding and flotation, registering zero on the meter.It was not until the zinc flotation stage that the dissolved oxygencontent of the pulp was increased to 4.5 ppm in the final tailing.These data suggest that the ore is very reactive and continues toconsume oxygen throughout the process.

The pulp temperature ranged 33 to 35°C across most of thecircuit (Figure 14). The spike in the pulp temperature in the ballmill discharge can be attributed to part of the addition grindingenergy being dissipated as heat.

The Eh-pH data for the grinding and flotation circuits of theEureka Concentrator have been plotted in Figure 15 to determinewhere the reactions are occurring. From the Nernst Equation 2there is a dependence of redox potential on pH:

E En

= °+⎛⎝⎜

⎞⎠⎟

0 05910

.log

αα

Reactants

Products(2)



0.0

2.0

4.0

6.0

8.0

10.0

12.0

SAG

mill

disc

harg

e

Cyc

lone

unde

rflow

Ball m

illdisc

harg

e

Cyc

lone

over

flow

Curo

ughe

r feed

Pbro

ughe

r feed

Znro

ughe

r feed

Znro

ughe

r tailing

Circuit position

pH

FIG 11 - The pH profile through the grinding and flotation circuits of the Eureka Concentrator.

Applying the Nernst equation to water results in a Pourbaixdiagram that describes three domains, separated by lines ofequilibria. The upper most of these is the water-oxygen line(Equation 3), above which water decomposes and oxygen isevolved, and below which water is stable:

E p pHo0 102 21 23 0 015 0 059= + + −. . log . (3)

This can be simplified further (Johnson, 1988; Natarajan andIwasaki, 1973) for an oxygenated aqueous solution with no welldefined redox couples to (Equation 4):



0.0

1.0

2.0

3.0

4.0

5.0

6.0

SAG

mill

disc

harg

e

Cyc

lone

unde

rflow

Ball m

illdisc

harg

e

Cyc

lone

over

flow

Curo

ughe

r feed

Pbro

ughe

r feed

Znro

ughe

r feed

Znro

ughe

r tailing

Circuit position

DO

,p

pm

FIG 13 - Dissolved oxygen profile through the grinding and flotation circuits of the Eureka Concentrator.

0.0

40.0

80.0

120.0

160.0

200.0

SAG

mill

disc

harg

e

Cyc

lone

unde

rflow

Ball m

illdisc

harg

e

Cyc

lone

over

flow

Curo

ughe

r feed

Pbro

ughe

r feed

Znro

ughe

r feed

Znro

ughe

r tailing

Circuit position

Eh

,m

V(S

HE

)

FIG 12 - The Eh profile through the grinding and flotation circuits of the Eureka Concentrator.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

SAG

mill

disc

harg

e

Cyc

lone

unde

rflow

Ball m

illdisc

harg

e

Cyc

lone

over

flow

Curo

ughe

r feed

Pbro

ughe

r feed

Znro

ughe

r feed

Znro

ughe

r tailing

Circuit position

Te

mp

era

ture

,oC

FIG 14 - Temperature profile through the grinding and flotation circuits of the Eureka Concentrator.

E pH020 9 0 059= + −. . (4)

In broad terms, if the changes in Eh and pH result in a lineparallel to the water-oxygen line this means that water equilibriais being maintained. That is, any change in Eh is directlyproportional to a change in pH with a similar relationship to thatexpressed in Equation 4. If the changes in Eh and pH result in aline that is perpendicular to the water-oxygen line then theevidence suggests that oxidative reactions are occurring.

Figure 15 shows that the Eh-pH lines between points 1 to 4 areperpendicular to the water-oxygen line. This indicates thatoxidative reactions are occurring in the grinding circuit. It islikely that these oxidative reactions are corrosion of the grindingmedia and oxidation of the sulfide minerals. As the pH isdecreased across the ball mill it is suggested that pyrite oxidation(an acidic reaction) may be one of the dominant reactions. Thechanges in the Eh-pH curve between Points 4 and 5, 5 and 6, and6 and 7 can be attributed to the addition of reagents that alter thepH of the system. During copper flotation it is the addition ofSMBS, while in the lead and zinc circuits it is the addition oflime.

The EDTA extractable copper, lead, zinc and iron data arepresented in Figure 16. The EDTA extractable copper profile(Figure 9) suggested that the percentage of oxidised chalcopyrite

remained negligible throughout the grinding circuit. The per centEDTA extractable copper increased during the copper flotationstep to approximately 2.5 per cent and this increases to 17 percent in the zinc rougher feed, which can be attributed to theaddition of copper sulfate for sphalerite activation.

The first observation to be made is that the percentage of EDTAextractable lead is at least an order of magnitude greater than thevalues reported for zinc and iron. The percentage of EDTAextractable lead is higher as galena is a reactive mineral when incontact with other sulfide minerals, particularly pyrite. The EDTAextractable lead profile shows that the percentage of oxidisedgalena remained approximately constant through the grindingcircuit, with values of around 1.0 per cent. The per cent EDTAextractable lead increased in the cyclone overflow, and remainsreasonably constant through the copper circuit. After the leadflotation stage the lead scavenger tailing per cent EDTA extractablelead was 17 per cent, and then increased to almost 40 per cent afterzinc flotation. These data suggest that the lead species that remainin the lead scavenger tailing are more heavily oxidised.

The EDTA extractable zinc profile exhibits a very similar trendto that observed for lead. That is, through the grinding circuit thepercentage EDTA extractable zinc remained approximatelyconstant, with values of around 0.1 per cent. This indicated thatsphalerite oxidation was largely unchanged through this circuit.



0.0

7.0

14.0

21.0

28.0

35.0

42.0

SAG

mill

disc

harg

e

Cyc

lone

unde

rflow

Bal

l mill

disc

harg

e

Cyc

lone

over

flow

Cu

roug

her fe

ed

Pb

roug

her fe

ed

Znro

ughe

r feed

Final

tailing

Circuit position

ED

TA

ex

tra

cta

ble

Cu

an

dP

b,%

0.0

0.2

0.4

0.6

0.8

1.0

1.2

ED

TA

ex

tra

cta

ble

Zn

an

dF

e,%

Cu Pb Zn Fe

FIG 16 - The EDTA extractable copper, lead, zinc and iron profiles through the grinding and flotation circuits of the Eureka Concentrator.

-50

0

50

100

150

200

250

4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0

pH

Eh

,m

V(S

HE

)

1

8

7

6

5

4

3

21. SAG mill discharge;

2. Cyclone underflow;

3. Ball mill discharge;

4. Cyclone overflow;

5. Cu rougher feed;

6. Pb rougher feed;

7. Zn rougher feed; and

8. Final tailing

FIG 15 - The Eh-pH curve for the grinding and flotation circuits of the Eureka Concentrator.

There was an increase in the per cent EDTA extractable zincduring the copper and lead flotation circuits (ie from 0.13 to0.37 per cent) and then a significant increase after the zincflotation circuit (to 1.0 per cent). This increase indicated that thezinc species that remained in the zinc scavenger tailing weremore heavily oxidised.

The EDTA extractable iron profile gives the best indication ofthe impact of grinding media corrosion on the system. The EDTAextractable iron through primary grinding ranged from 0.05 to0.16 per cent, and then increased to 0.30 per cent in the cycloneoverflow. That is, the pulp entering the flotation circuit containselevated levels of EDTA extractable iron, presumably aftercontact with the forged grinding media. Interestingly, the levels ofEDTA extractable iron decrease through the flotation circuit. Thisis probably due to the continuing oxidation of the iron species tohigher level iron oxides, which are not soluble in EDTA.

Summary of data acquisition

The metallurgical survey suggests that there are a number ofopportunities for improving the metallurgical performance of theEureka Concentrator. It is suspected that these may be liberationrelated, and further data is required to identify exactly what theliberation characteristics are. The data strongly suggests that thefirst priority should be improving the lead flotation circuit.

The pulp chemistry suggests that the ore is reasonably reactive,particularly during grinding, and consideration should be given toimprove this aspect of plant performance. However, this shouldnot be rectified until the liberation issues have been resolved.

PROBLEM DEFINITION

From the metallurgical survey the circulating load observed in thelead rougher/lead first cleaner circuit were observed to beextremely high (Table 10), for both lead and zinc. In fact, thecopper scavenger tailing contributed only 15.4 and 28.6 per centof the lead and zinc units, respectively, to the lead rougher feed.The recycle from the lead first cleaner tailing contributed by farthe most lead and zinc to the lead rougher feed (81.9 and 64.7 percent, respectively).

Recovery by size (Chapter 2)

The mass balanced data gives you information about the gradesand recoveries in the circuit. The data also provides informationabout the circulating loads. However, to gain a greaterappreciation for where problems occur and their magnitude, it isadvisable that samples from selected process streams be sized,and each size fraction assayed. By mass balancing the sizefractions, the performance of each size fraction can be assessed.

An examination of Table 9 and Figure 9 indicates that the leadrougher/first cleaner section of the plant has a large circulatingload of galena and sphalerite. It is likely that this large circulatingload produces instability within the circuit, and is probably due tocomposite particles. However, at this stage you are only guessing.

You need to gather solid data that tells you which size fractionsare being rejected from the circuit, and what their mineralogicalcharacteristic is. Therefore, the first step in this analysis is toexamine the recovery-by-size around the lead rougher feed.


When you conducted the plant survey you may have had everyintention to have the samples sized so that you can complete arecovery-by-size analysis. However, before having yourmetallurgical technicians launch into a large body of work sizingevery sample collected during the survey it is best to massbalance the plant survey. Sizing samples prior to completing themass balance may waste time, resources and money if the surveydoes not mass balance easily (ie poor sampling). Once you haveestablished that the mass balance is good, you need to decide ifcompleting a recovery-by-size analysis is warranted, and if it is,what samples you would like to have sized. It is not alwaysnecessary to size every sample in the survey. The sampleselection will depend on what the survey reveals about plantperformance. For example, sizing the concentrate and tailingfrom a section will show which size fractions of the valuablespecies are misbehaving (ie not being recovered efficiently), andwhich size fractions of the gangue species are being recovered.Sometimes completing recovery-by-size analysis on a down-the-bank survey can add value because it allows you to determinewhere size fractions are recovered in the circuit, and can be usedto determine the kinetics of various species on a size basis.

With these decisions made you need to retrieve the samplesfrom storage, ensure that you have sufficient sample to completea sizing (generally 200 grams is sufficient), and determine whatsizes you require. If you need to obtain size fractions below 38microns you will need to use a cyclosizer, which will give you sixmore size fractions. However, if cyclosizer is used in concert witha precyclone and a centrifuge the number of size fractionsobtained can be extended to seven.


With the sizing completed you will require the mass collected ineach size fraction. If the sizing has been restricted to a simplesieve analysis, then you will need to determine how much massyou have in each size fraction to ensure that you have sufficientfor assay (usually 5 grams is more than enough). If you do nothave enough sample in each size fraction for an assay you need toeither complete another sizing on this sample, or you combinesize fractions to yield enough mass. However, if you do combinesize fractions you must apply this across all of the samples withinthis data set. Not to do so will make it impossible to complete theanalysis. That is, if you were required to combine the +300, +212and +150 micron fractions in the feed sample, then you will needto do the same for all other samples in the suite of samples forthis survey.

When it comes to sizing the subsieve fractions using aprecyclone/cyclosizer/centrifuge combination, you will need toknow the water temperature, the elutriation time, the water flow



Stream t/h Grade (%) Recovery (%)

Cu Pb Zn Fe Cu Pb Zn Fe

Cu scav tailing 118.8 0.2 4.0 15.0 13.0 46.4 99.0 99.5 98.7

Pb 1st cleaner tailing 128.0 0.3 19.4 30.9 9.2 71.0 526.4 225 76.6

Pb scav concentrate 10.1 0.4 8.2 39.7 11.6 7.2 17.7 23.1 7.7

Pb rougher feed 256.9 0.3 11.8 23.9 11.1 124.6 643.1 347.6 183

TABLE 10Mass balanced data for the lead rougher feed, with respect to flotation feed. Note: Both the lead scavenger concentrate and the lead first

cleaner tailing are recycled back to the head of the lead rougher.

rate and the specific gravity of the mineral being separated. Withthese variables known using the cyclosizer manual (WarmanInternational Limited, 1991) the ‘cut’ size for each cyclone can becalculated. That is, for quartz the cyclone ‘cut’ sizes for thecyclosizer are:

• C1 = 44 microns,



• C4 = 15 microns, and

• C5 = 11 microns.

The precyclone generally ‘cuts’ at about 7 microns. To convertthese ‘cut’ sizes to values that are more representative of mineralssuch as chalcopyrite, galena, sphalerite and pyrite, then the ‘cut’size for quartz must be multiplied by the overall correction factor:

f = fT × fSG × fFM × fET (5)

where:

fT is the temperature correction factor

fSG is the mineral specific gravity correction factor

fFM is the flow meter reading correction factor

fET is the elutriation time correction factor

It is important to note that you should correct for each mineralin the system as each mineral has a different specific gravity. Thecyclone ‘cut’ sizes for each of the cyclosizer cones are given inTable 11.

Further, it must be realised that sizing the -38 micron materialusing the precyclone, cyclosizer and/or the centrifuge represents achange in the way the particles are sized. When using sieves thesizing is achieved by passing the particles over a nest of sieves.Particles that are larger than the screen aperture are retained onthe screen and those that are smaller fall through. However, whensizing using the precyclone, cyclosizer and/or centrifuge sizing iscompleted hydraulically based on size and mineral specificgravity. When the two sizing methods are used together, thetransition point from one technique to the other can lead to someunusual shaped curves which are purely due to the change in thesizing method. To overcome this it is usual to combine the -53micron to +C2 size fractions. This smooths the curves andeffectively removed the transition between sizing methods.

If you are considering completing a mineralogical analysis it isat this point that you would ‘split’ out a small representativeportion from the samples of interest prior to pulverising thesamples for assay. Once you have reviewed the weights in eachsize fraction for each of the samples you have had sized, anddecided which size fractions to combine, and ensured that there isenough sample for assay in each size fraction, the samples can beprepared and submitted of assay.

How do I analyse the data collected?

When the assays return it is important to check that the assays arein good order. This can be done by calculating the head assayfrom the size fraction assays and comparing it with the actualhead assay. If they are in good agreement, then you can assumethat both the sizing and the assays were completed properly.

To construct a graph similar to that presented in Figure 17 thesize and assay data for the copper rougher tailing, the lead firstcleaner tailing, the lead scavenger concentrate and the leadrougher feed are required. To simplify the analysis only the leadassay data will be considered in this example. The starting pointfor determining the recovery-by-size behaviour in this part of thecircuit is the mass balanced data from the plant survey given inTable 9, and for this section of the plant Table 10. Using the massbalanced tonnages for each of the process streams given inTable 10 the size distribution data determined from the sizingprocess (ie the weight per cent data) for each of these samplepoints were applied to yield the tonnes of ore in each size fraction(Table 12). The weight per cent and the assay for each sizefraction are used to calculate the head grade:

Head grade = ×

∑∑=

( )m a

mi i

ii

n

1

(6)

where:

m is the weight per cent is size fraction i

a is the assay for that size fraction

This calculated head grade is then compared with the assay ofthe head sample to determine how well the sizing and assayprocedures have been completed. Unfortunately, in this instancethe mass and assay of the -5.6 micron fraction was calculated bydifference. That is:



Correction factor Mineral

Quartz Chalcopyrite Galena Sphalerite Pyrite

fT 0.9815 0.9815 0.9815 0.9815

fSG 0.7181 0.5038 0.7416 0.6423

fFM 0.9519 0.9519 0.9519 0.9519

fET 1.0063 1.0063 1.0063 1.0063

f 0.6751 0.4737 0.6972 0.6038

Cyclone ‘cut’ size

C1 44 30 21 31 27

C2 33 22 16 23 20

C3 23 16 11 16 14

C4 15 10 7 11 9

C5 11 7 5 8 7

TABLE 11Cyclosizer cyclone ‘cut’ sizes for quartz, chalcopyrite, galena, sphalerite and pyrite.



+22.2+16.7

+11.6+7.6

+5.6-5.6

Total

Cu scav tail

Pb scav con

Pb 1st cleaner tail

Pb rougher feed

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Pb

dis

trib

uti

on

,%

Particle size, microns

FIG 17 - Lead distribution by size in the process streams contributing to the lead rougher feed.

Stream/size Wt % Wt (t/h) Pb parameter (%)

Grade Distribution Recovery to Pb Ro feed

Cu scavenger tailing

+22.2 μm 48.79 57.96 4.06 49.49 7.73

+16.7 μm 6.66 7.91 3.00 5.00 0.78

+11.6 μm 10.18 12.09 1.88 4.78 0.75

+7.6 μm 5.32 6.32 2.54 3.38 0.53

+5.6 μm 3.77 4.48 2.24 2.11 0.33

-5.6 μm 25.28 30.03 5.58 35.24 5.51

Head 100.00 118.79 4.00 100.00 15.62

Pb First cleaner tailing

+22.2 μm 82.44 105.48 22.12 93.96 76.72

+16.7 μm 6.13 7.84 8.84 2.79 2.28

+11.6 μm 3.19 4.08 8.64 1.42 1.16

+7.6 μm 1.17 1.50 5.10 0.31 0.25

+5.6 μm 0.81 1.04 4.10 0.17 0.14

-5.6 μm 6.26 8.01 4.18 1.35 1.10

Head 100.00 127.95 19.41 100.00 81.65

Pb scavenger con

+22.2 μm 70.32 7.13 9.66 83.14 2.26

+16.7 μm 10.48 1.06 8.20 10.52 0.29

+11.6 μm 6.69 0.68 2.62 2.15 0.06

+7.6 μm 2.15 0.22 3.20 0.84 0.02

+5.6 μm 1.37 0.14 3.68 0.62 0.02

-5.6 μm 8.99 0.91 2.49 2.74 0.07

Head 100.00 10.14 8.17 100.00 2.72

Pb rougher feed

+22.2 μm 66.40 170.57 15.46 86.72 86.72

+16.7 μm 6.55 16.82 6.05 2.25 2.25

+11.6 μm 6.56 16.85 3.55 1.97 1.97

+7.6 μm 3.13 8.03 3.03 0.80 0.80

+5.6 μm 2.20 5.65 2.62 0.49 0.49

-5.6 μm 15.16 38.95 5.22 6.68 6.68

Head 100.00 256.88 11.84 100.00 100.00

TABLE 12The lead recovery-by-size data for the lead rougher feed calculated from the copper rougher tailing, lead first cleaner tailing and the

lead scavenger concentrate.

m m mm Hi

n

−−

−

= − ∑5 6 11

1

. μ (7)

and

a

m a m a

mm

H H ii

n

m−

−

−

−

=× − ×⎛

⎝⎜⎞⎠⎟

⎛⎝⎜

⎞⎠⎟∑

5 6

11

1

5 6.

.μ

μ

(8)

where:

mH and aH are the weight per cent and assay for the head

By calculating the weight per cent and assay for the finest sizefraction all the errors associated with the sizing, assaying andsampling are accumulated to this size fraction. In an ideal worldas sample of this material would be collected.

Having worked out the tonnes of ore and the assay for eachsize fraction the distribution-by-size within each process stream,and the recovery-by-size against a particular process stream canbe calculated. The distribution within a process stream is givenby:

Distributionm a

m aai i

H H

= ××

⎛⎝⎜

⎞⎠⎟ ×( )

( )100 (9)

The distribution of lead within each process stream is providedin Table 12. In this case the recovery-by-size is calculated for thelead rougher feed because the copper scavenger tailing, the leadfirst cleaner tailing and the lead scavenger concentrate allcombine to form the lead rougher feed. So, the recovery for eachsize fraction, i, is given by:

Recoveryi a

p pP

n

H H

m a

m a,

( )

( )=

×

×

⎛

⎝

⎜⎜⎜⎜

⎞

⎠

⎟⎟⎟⎟

×=∑

1 100 (10)

where:

P are the process streams of interest

A similar approach is adopted for the more traditionalrecovery-by-size curves presented in Figure 18 and 20.

What does it mean?

Figure 17 contains the distribution by size, for galena, for thethree process streams contributing to the lead rougher feed. Thevast majority of the galena in the lead rougher feed is in thecoarse (+20 micron) lead recycled back from the lead first cleanertailing (80 per cent of the total lead in the lead rougher feed).Thus, the flotation characteristics of the lead rougher/scavengerunit are dominated by the behaviour of this material.

The recoveries versus size data for the lead final concentrateare provided in Figure 18. These data show clearly that the leadfinal concentrate consists predominantly of fine (-20 micron)galena, with greater than 90 per cent lead recovery for theintermediate size fractions (+5/-20 microns). Either side of thissize range the recoveries decrease. The recoveries of gangueminerals (sphalerite, iron sulfides and non-sulfide gangue) arelow across all size fractions.

The galena distribution, by size, of the lead scavenger tailing,with respect to flotation feed, is provided in Figure 19. Clearly thelosses of galena from the lead circuit are bimodal, with significantlosses in the fine (-5 microns) fraction (5.6 per cent) and coarse(+20 microns) fractions (19.9 per cent). Of greatest concernhowever are the losses in the coarse fractions. It is presumed thatthese coarse particles occur as galena deficient/sphalerite richcomposites.

Figure 20 shows the recovery by size data for the zinc finalconcentrate. These data show the peculiar behaviour of the coarse(+20 microns) galena, which exhibits recoveries as high as 60 percent. It is presumed that this behaviour is related to thecomposition of these galena particles (ie galena deficient/sphalerite rich composites).

Mineralogy (Chapter 4)

Once the recovery-by-size data has been analysed and a theoryformed, select streams should be examined mineralogically. Asthis is a relatively expensive process, it is wise to complete thework already discussed, prior to obtaining mineralogical analysis.The recovery-by-size analysis indicates that a significant amount



0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

1.0 10.0 100.0 1000.0

Geometric mean particle size, microns

Rec

ove

ry,%

Lead Zinc Copper Iron Silica

FIG 18 - Recovery-by-size data for the lead final concentrate, with respect to flotation feed for the Eureka Concentrator.

of material is recycled back to the lead rougher feed from the leadfirst cleaner tailing, and that the vast majority of this materialoccurs in the +20 micron size fraction. It is highly likely thatthese particles are galena/sphalerite composites. However, inorder to prove this it is necessary to complete a mineralogicalexamination.


To complete a mineralogical analysis of samples from you plantyou need to provide the company completing the examination foryou with a representative sample of the process stream that hasnot been pulverised. For liberation analysis, the sample providedwill be of a number of closely sized size fractions. If you havecompleted a recovery-by-size analysis you should have preparedthe samples for mineralogical examination at the same time.

With the samples available for analysis it is then necessary tochoose the method by which the mineralogical data will becollected. These methods range from optical microscopy throughto automated methods such as QEMSCAN and MLA. Chapter 4:A Practical Guide to Some Aspects of Mineralogy that EffectFlotation, by Alan Butcher, describes the methods available.


When shipping samples off to a laboratory for mineralogicalanalysis it is wise to provide the mineralogist with a scope ofwork, and assays of the samples that will be examined. The scopeof work will inform the mineralogist of the questions you wouldlike answered. For example, at Eureka you are interested in thelocking and liberation characteristics of galena and sphaleritearound the lead rougher/first cleaner circuit. It is then clear to themineralogist that the examination should provide informationabout the degree of liberation of these minerals, and how they arelocked with other minerals. Supplying the assays for the samplesyou have supplied for examination allows the mineralogist toreconcile his calculated head grade based on the minerals presentback to the assays. It is a check that the mineralogist uses toensure the quality of the data that is provided.


The mineralogical report provided will depend on the techniqueemployed by the mineralogist to examine your samples.Essentially, the data provided should provide nominally the sameinformation. That is, the percentage of liberated mineral within



0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1.0 10.0 100.0 1000.0


Rec

ove

ry,%

Lead Zinc Copper Iron Silica

FIG 20 - Recovery-by-size data for the zinc final concentrate, with respect to flotation feed for the Eureka Concentrator.

178

.3

126.1

89

.2

63.0

48

.8

41.0

28.0

17.6

11.77.96.02.3

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Dis

trib

uti

on

(wit

hre

sp

ec

t

tofl

ota

tio

nfe

ed

),%


FIG 19 - The galena distribution by size, with respect to flotation feed, in the lead scavenger tailing for the Eureka Concentrator.

the sample, the percentage of binary particles present and whatthe two minerals are, and the percentage of more complexcomposite particles measured. Table 13 provides a simplified listof the liberation data for galena in the lead rougher feed of theEureka Concentrator.

What this data means is that of the lead in the +28 micron sizefraction 66 per cent occurs as liberated particles of galena and28 per cent are present as binary composites with sphalerite.Minor amounts of galena are associated with pyrite and occur internary particles.

What does it mean?

Mineralogical examination of strategic process streams feedinginto the lead rougher feed showed that about one third of thegalena in the lead rougher feed occurs as coarse (+20 micron)galena/sphalerite composite particles (Figure 21), and themajority of this material emanates from the lead first cleanertailing. Thus, two thirds of the galena in the lead rougher feed isliberated, which infers that the maximum lead recovery to thefinal lead concentrate, at grade, would be approximately 70 percent. Therefore, galena liberation was considered to be thelimiting factor impinging of improved metallurgical performance.

Problem definition summary

The metallurgical survey suggested that the lead circuit waspotentially unstable with large circulating loads of galena andsphalerite around the lead rougher/first cleaner circuit. The use of

recovery-by-size and liberation analysis has clearly indicated thatthe recovery of +20 micron galena within the lead circuit islimited. The liberation analysis clearly shows that the galena inthe +20 micron fraction is locked with sphalerite.

Thus, the problem is: poor liberation of galena in the +20 micronsize fractions.

SOLUTION DEVELOPMENT AND TESTING

It is apparent that to improve liberation one needs to grind finer.The first step in proving that improved regrinding is required inthe lead circuit is to complete a series of laboratory tests examiningthe impact of regrinding the main feed source to the lead rougher,the lead first cleaner tailing.

Laboratory investigation (Chapter 9)


As the lead first cleaner tailing contributed the highest flow ofmaterial to the lead rougher feed, and this process stream containsa significant percentage of galena/sphalerite composite particlesgreater than 20 microns, it was decided that this process streamwould be the best one to complete laboratory tests examining theimpact of regrinding on lead rougher flotation. It is your job todesign a series of laboratory flotation tests that would identifywhat changes would occur as the lead first cleaner tailing wasreground to progressively finer sizes.



89.228.210.16.0

2.3Head

Liberated

Ga-Sp

Ga-Py

Ga-Gn

Ternary

0

10

20

30

40

50

60

70

80

90

100

Size, microns

Liberation class

Dis

trib

uti

on

FIG 21 - Size by liberation class data for the lead rougher feed. Ga = galena; Sp = sphalerite; Py = pyrite; and Gn = gangue.

Size (μm) Liberation class (%) Total (%)

Liberated Binary with … Ternary

Sphalerite Pyrite Gangue

+89 26 54 5 4 11 100

+28 66 28 3 0 2 100

+10 69 25 5 1 1 100

+6 93 6 0 0 0 100

-6 94 5 0 0 0 100

Head 66 28 3 0 2 100

TABLE 13Galena liberation by size and mineral class for the lead rougher feed of the Eureka Concentrator.

With the laboratory test program designed, sufficient pulp wascollected from the lead first cleaner tailing. In addition to the leadfirst cleaner tailing pulp, a bulk sample of process water was alsocollected. The pulp was filtered and ‘cut’ into 1000 gram lots.The test program called for one test to be completed withoutregrinding, and then tests completed on ore ground for five, ten,15 and 20 minutes. The tests were completed in a 2.5 litreflotation cell. Timed concentrates were collected after 0.5, 1.5,2.0, 2.0, 2.0, 2.0 and 2.0 minutes for a total flotation time of12 minutes. The seven concentrate and tailing samples generatedfor each test were prepped and submitted for assay.


The weights for each of the concentrates and the tailing samplesas well as the assays are required to complete a mass balance foreach of the tests. Additional information such as the pH, reagentadditions and any observations made during the test are alsohelpful.


The weights and assays are to be put into a spreadsheet thatallows you to calculate the cumulative grades and recoveries foreach of the tests. These data can then be used to construct leadgrade/recovery curves (Figure 22). From the mass balanced datait is also possible to determine the kinetics (ie flotation rateconstant and maximum recovery) using Equation 11, and theselectivity for galena against the gangue minerals.

R R e kt= × − −max ( )1 (11)

To assist with interpretation the kinetic data and the selectivitydata can be tabulated. For example, Table 14 shows the leadconcentrate grade, as well as the zinc and iron sulfide recoveries,at 80 per cent lead recovery. Selectivity curves can also be drawn.

What does it mean?

Laboratory lead rougher flotation tests conducted on strategicprocess streams suggested that the introduction of regrindingsignificantly improved the position of the lead grade/recoverycurve. Figure 22 shows that as the degree of regrinding increasedthe position of the lead grade/recovery curve improved. Forexample, at 80 per cent lead recovery the lead grade wasincreased from 30.8 per cent for the test conducted withoutregrinding, to 42.2 per cent after 20 minutes regrinding. Theimprovement in lead grade was due to better selectivity for galenaagainst sphalerite and iron sulfides (Table 14).

In the laboratory, regrinding improved lead metallurgy, andwith the improved selectivity for galena against sphalerite it isexpected that zinc metallurgy would also improve.

Plant trial (Chapter 10)

As the existing lead regrind mill was inadequate for this duty, alarger mill would be required if improvements in metallurgicalperformance were to be realised. In light of the very firstcomment made at the beginning of this chapter about utilising the



25.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Pb recovery, %

Pb

gra

de

,%

No regrinding 5 minutes regrinding 10 minutes regrinding

15 minutes regrinding 20 minutes regrinding

FIG 22 - Lead grade/recovery curves for lead rougher flotation tests conducted on lead first cleaner tailing, demonstrating the effectof regrinding.

Test description Pb grade (%) Diluent recovery (%)

Zn IS

No regrinding 30.8 71.2 51.6

5 minutes regrinding 34.3 61.9 37.2




TABLE 14Lead grade and diluent recoveries, at 80 per cent lead recovery, for lead rougher flotation tests conducted on lead first cleaner tailing

demonstrating the impact of regrinding.

resources around you, and especially people, this problem wasdiscussed with the operations team and a solution was quicklyfound. As capital was not readily available an experiencedflotation operator suggested swapping the lead and zincregrinding mills, as the zinc regrinding mill was larger and moreable to cope with the higher tonnages experienced in the leadcircuit.

Redirecting the lead scavenger concentrate and lead firstcleaner tailing process streams to the zinc regrind mill wasaccomplished through a simple change in the pipe work. Theexisting pipe work to the old lead regrind mill was left intact sothat a plant trial could be conducted. A randomised block on/offtrial was designed, following the guidelines prescribed by TimNapier-Munn in Chapter 10: Designing and Analysing PlantTrials, that would use the shift mass balanced data to statisticallyanalyse the metallurgical performance of the Eureka Concentratorwith and without effective lead regrinding.

When the new lead regrind mill was turned on the impact onlead and zinc metallurgy was immediate and dramatic. Becauseof the magnitude of the improvements the on/off trial wasabandoned. However changes of this magnitude are not alwaysapparent, and strong statistical data analysis is often required asproof that there is a statistically confident change in the plantwhich was due to your implemented solution. In some cases,trials can run for a number of months but persistence pays offwhen you achieve the result you were aiming for.

The lead recovery to final concentrate from June 1998 throughto February 2000 is presented in Figure 23. It is apparent that thelead recovery has increased on average from 65 per cent up to81.5 per cent. The most significant improvement to lead recoverywas achieved through the application of an effective regrindingstage.

Despite not undertaking the randomised block trial as firstthought, it was possible to complete a statistical analysis usingplant data from before and after the change. Using the Studentt-test to analyse the data showed that installing effective leadregrinding produced the following results:

• the lead recovery was improved by 16.5 ± 2.5 per cent, withgreater than 99 per cent confidence;

• the lead concentrate grade improved by 4.0 ± 0.8 per cent,with greater than 99 per cent confidence; and

• the zinc grade in the lead concentrate was reduced by 1.8 ±0.6 per cent, with greater than 99 per cent confidence.

These changes to the lead circuit resulted in a decrease in theamount of sphalerite reporting to the lead concentrate, and shiftedthis material into the zinc circuit. Hence, zinc metallurgy was alsoimproved, with:

• the zinc recovery increased by 1.5 ± 0.4 per cent, withgreater than 99 per cent confidence; and

• the zinc concentrate grade increased by 2.1 ± 0.5 per cent,with greater than 99 per cent confidence.

Other statistical methods (for example, comparison ofregression lines) produced similar values.

THE CYCLE BEGINS AGAIN

Despite the application of effective regrinding in the lead circuit,and the massive improvements in metallurgical performance,there is still room for improvement. There is not time to rest onyour laurels, as the Senior Project Metallurgist it is your job toidentify problems and develop cost-effective solutions toimplement in the plant. You’ve only solved part of the problem,and opportunities for further improvement are available. So, sorrybut the Senior Project Metallurgist’s work is never finished! Thekey areas where efforts can be made to improve metallurgy are:

• the optimisation of both the lead and zinc regrindingcircuits, to improve liberation and subsequently concentrategrades and recoveries;

• the optimisation of chemistry for flotation to improveselectivity of valuable minerals against gangue minerals, andconsequently increased concentrate grades; and

• to continue minimising variations in the process.

More surveys

To move forward it is necessary to continue monitoring theprocess through:

• analysis of shift mass balances,

• analysis of monthly composite samples (by size andliberation class),



50.0

55.0

60.0

65.0

70.0

75.0

80.0

85.0

90.0

7-J

an

-98

28

-Ja

n-9

8

18

-Fe

b-9

8

11

-Ma

r-9

8

1-A

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22

-Ap

r-9

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8

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un

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24

-Ju

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8

15

-Ju

l-9

8

5-A

ug

-98

26

-Au

g-9

8

16

-Se

p-9

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7-O

ct-

98

28

-Oct-

98

18

-No

v-9

8

9-D

ec-9

8

30

-De

c-9

8

20

-Ja

n-9

9

10

-Fe

b-9

9

3-M

ar-

99

24

-Ma

r-9

9

14

-Ap

r-9

9

5-M

ay-9

9

26

-Ma

y-9

9

16

-Ju

n-9

9

7-J

ul-

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28

-Ju

l-9

9

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-Au

g-9

9

8-S

ep

-99

29

-Se

p-9

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20

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99

10

-No

v-9

9

1-D

ec-9

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22

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c-9

9

12

-Ja

n-0

0

2-F

eb

-00

W/E

Av

era

ge

%R

ec

.

Average Average CL UCL LCL

New Pb Feed distributor

MF4 used as

lead regrind mill

Level on Pb conditioning tank

dropped

Initial Pb froth level control

Complete Pb froth level control

+ new 6" Pb regrind cyclones

+ Dedicated float operators

FIG 23 - Lead recovery versus time data from June 1998 to February 2000. Some milestones are highlighted.

• routine metallurgical and pulp chemical surveys,

• flotation cell characterisation (Chapter 5), and

• the introduction of a future ores testing program(Geometallurgy, Chapter 12).

After all, to measure is to know!

Chemistry

With the improvements in the lead circuit through betterregrinding perhaps it is time to study the chemistry of thesystem.

Pulp chemistry

The pulp chemistry indicated that the lead rougher circuitoperated in oxidising conditions, with Eh values between 150 and200 mV (SHE). Figure 15 shows the Eh-pH curve for the circuit.The curve is essentially parallel to the water-oxygen line for thelead rougher circuit, and suggested that any changes in Eh or pHwere due to the maintenance of water equilibria. These conditionspoints to a system operating in oxidising conditions.

Solution chemistry

The EDTA extractable lead increased from approximately fiveper cent in the copper rougher tailing up to 17 per cent in the leadscavenger tailing. The EDTA extractable zinc and iron valueswere an order of magnitude lower than those reported for lead.These data suggest that the galena in the lead circuit feed ismoderately oxidised, while the galena remaining in the leadscavenger tailing is heavily oxidised. The main point to comefrom this analysis is that there would appear to be a significantquantity of galena oxidation products present within the pulp,which under the right circumstances could lead to the inadvertentactivation of sphalerite.

Surface chemistry

To confirm your theory developed from the pulp chemistryobservations it is wise to complete surface analysis on selectedsamples. Alan Buckely, in Chapter 8: Surface ChemicalCharacterisation for Identifying and Solving Problems WithinBase Metal Sulfide Flotation Plants, provides details of the typesof surface analysis and how they may be used in your quest formore information.


With the decision made to complete surface analysis to validateyour hypothesis you need to provide the institution conductingthe surface analysis with a scope of work. It is imperative thatyou provide the surface scientist with a focused question thatdefines what you hope to get out of the surface analysis. In yourcase, at the Eureka Mine, you are very interested in knowing thesurface chemistry of liberated sphalerite that reports to the leadrougher concentrate, and how this compares with free sphaleritein the lead rougher tailing. This essentially means that thesurface scientist will examine liberated sphalerite in leadrougher concentrate and tailing process streams to identifydifferences that may explain why some of the liberatedsphalerite is being recovered into the rougher concentrate. Thesurface scientist will suggest the surface analysis technique thatthey feel is best for your application. ToF-SIMS is frequentlyused because of its high surface sensitivity and its ability toanalyse individual particles.


When collecting the samples for surface analysis it is important touse the right sample preparation protocols. The surface scientistwill provide you with these. Generally, once the samples havebeen collected they need to be purged with nitrogen gas toremove oxygen from the pulp to prevent further oxidation. Thesample is then sealed in the sample vial, and snap frozen in liquidnitrogen. The sample pairs are then shipped to the surfaceanalysis facility frozen in a cryogenic container.

Ideally, when you collect the samples for surface analysis youshould complete a block survey of that part of the circuit ofinterest. At Eureka, a block survey of the lead rougher circuitwould be the order of the day. Conducting a survey gives you areference point in terms of metallurgical performance that can beused for comparison purposes. It is also wise to complete a pulpchemical and EDTA extractable metal ion analysis on the sampleprocess streams that the surface analysis samples were collectedfrom. This information should be transmitted to the surfacescientist as it gives them some idea of the chemistry of the systemand helps explain the observations they will make.


The surface scientist will provide you with an analysis of thefindings. You are not expected to know everything! However, it iswise to read the surface analysis report and ask questions. If youdo not understand the analysis of the surface chemical data askthe surface scientist to explain. This will allow you to questionthe surface scientist to determine how they reached theconclusions and help you to understand these techniques, and thevaluable information they can add to an investigation. It is veryimportant to link the surface chemical observations to the pulpchemistry and metallurgical performance of the plant. After allthe data generated will be used to confirm your theory as to whysomething is happening (at Eureka identification of the activatingspecies on liberated sphalerite), and from this you will proposesolutions for testing in the laboratory to solve the problem.

What does it mean?

Surface analysis was completed on samples of lead rougherconcentrate and tailing samples using ToF-SIMS to determine thedominant species on the surfaces of the liberated sphaleriteparticles. The surface analysis investigation indicated that theliberated sphalerite particles contained within the lead rougherconcentrate had statistically more collector, copper, silver andlead species on their surfaces than those reporting to the tailing(Figures 24 and 25).

The conclusions reached from the chemical analysis were that:

• The flotation pulp in the lead rougher circuit operated inoxidising conditions.

• The level of EDTA extractable lead within the lead circuitwas high, indicating the presence of a large quantity ofgalena oxidation species capable of activating sphalerite.

• The surface analysis confirmed the suspicion that theliberated sphalerite was activated by lead ions. Copper andsilver were also observed on the surfaces of the sphaleriteparticles, but in lower concentrations.

These data would suggest that some of the sphalerite is beingrecovered into the lead concentrate because it is activated by leadions. You should propose that laboratory test work be completedexamining the effect of various reagents to depress activatedsphalerite. Should these tests prove positive, and the solution iseconomically sound, then it should be tested in the plant usingstatistically rigorous trial methodology.



COMMUNICATION

The technical aspects of concentrator performance are veryimportant in achieving optimum metallurgy. However, the besttechnical expertise is of little consequence if people andmaintenance issues are not adequately addressed.Process variations are probably the hardest to monitor andcontrol, and can result in operations personnel treating symptomsrather than causes (ie ‘fire fighting’). In most instances processvariations, such as ore hardness, mineralogical changes with oretype, and head grade variation, are very difficult to measureonline, but can be partially overcome with an effective orecharacterisation program (geometallurgy) and good commun-ication between the mine and the mill.

Behavioural variations are often easily identified, but aredifficult to correct because of the human factor. One way ofimproving the behavioural variations within a concentrator is todocument and standardise each task. That is, if each operator

performs the same task the same way, by following as standardwork procedure, then behavioural variations can be minimised.Unfortunately, this is easier said than done.

Another key element in altering behaviour to maximisemetallurgical performance is the development of a proactive,interventionist metallurgical team, such that there are definitemetallurgical targets and procedures prescribed to achieve thesegoals. A change in the perception of the metallurgist is requiredsuch that he should be viewed as a member of the team withspecialist knowledge that is on-call when problems arise.

CONCLUSIONS

At the Eureka Concentrator the use of classical metallurgicaltechniques (recovery- and liberation-by size analysis) hasprovided a high degree of certainty in determining the source ofthe metallurgical problem. Initially galena/sphalerite liberationwas identified as a significant impediment to achieving better



(A) (B)

FIG 25 - ToF-SIMS images of sphalerite particles within the lead rougher concentrate: (A) the zinc ion image; and (B) the lead ion image.

(+) SIMS sphalerite

0

0.05

0.1

0.15

0.2

0.25

0.3

Mg

Ag S

iCa Fe

Cu Zn

FeOH P

b

PbO

H

(+) Fragment

Norm

alis

ed

Inte

nsity

Pb cleaner con

Pb cleaner tail

(-) SIMS sphalerite

0

0.1

0.2

0.3

0.4

0.5

C CH O OH S SO3 SO4 IEX

(-) Fragment

No

rma

lise

dIn

ten

sity

Pb cleaner con

Pb cleaner tail

FIG 24 - Positive and negative mass spectra for sphalerite particles in the lead cleaner concentrate and tailing process streams – confidenceintervals calculated for 95 per cent.

lead and zinc metallurgy. With the application of greaterregrinding capacity the focus shifted from liberation to chemistry.The use of pulp and solution chemistry provided an indicationthat inadvertent activation of sphalerite by lead, copper and silverions was probably the reason for the high recovery of liberatedsphalerite into the final lead concentrate. The judiciousapplication of appropriate surface chemical techniques providedconfirming evidence. The Eureka metallurgists have nowidentified the reasons for the losses of sphalerite from the circuit,and can now develop solutions to this problem for testing in thelaboratory, prior to trialling in the plant.

REFERENCES

Johnson, N W, 1988. Application of electrochemical concepts to foursulphide flotation separations, in Proceedings Electrochemistry inMineral and Metal Processing II, pp 131-149.

Natarajan, K A and Iwasaki, I, 1973. Practical implications of Ehmeasurements in sulphide flotation circuits, in AIME Transactions,256:323-328.

Rumball, J A and Richmond, G D, 1996. Measurement of oxidation in abase metal flotation circuit by selective leaching with EDTA,International Journal of Mineral Processing, 48:1-20.

Warman International Limited, 1991. Cyclosizer Instruction Manual –Particle Size Analysis in the Sub-Sieve Range, Bulleting WCS/2(Warman International Limited: Sydney).

APPENDIX 1 – THE DOWN-THE-BANK SURVEY

Introduction

Often it is of value to complete metallurgical surveys weresamples are collected down a bank of flotation cells. This type ofsurvey is referred to as a down-the-bank survey, and differs froma block survey, as it provides more detailed information about theflotation behaviour of the various species contained within thatbank of flotation cells. In conducting a block survey you collectsamples of the feed, concentrate and tailing from the section ofthe circuit you are interested in. This information provides youwith data about the overall performance of that block of cells.The down-the-bank survey provides detail of the internalworkings of that block of flotation cells.

How do I do a down-the-bank survey?

Chapter 2: Existing Methods for Process Analysis, by BillJohnson provides a detailed description of the two commonmethods employed. An example, using one of those methods isgiven below. The first step in completing a down-the-bank surveyis to identify the samples that you wish to collect. Figure A1 is aschematic of the bank of 12 flotation cells you want to survey.You have decided to divide the flotation cells into groups suchthat you will collect five concentrate samples, in addition to thecombined concentrate sample that the bank produces. The samplelist is given in Table A1.

With the sampling points identified you need to communicateyour intentions to various people involved and prepare yourequipment. The same actions discussed above the Section: TheMetallurgical Survey, What do I have to do? apply to conductinga down-the-bank survey. Communication and organisation are thekey to success.

In terms of sampling equipment you will need:

• dip and lip samplers that are clean and good working order;

• sufficient clean buckets, with lids, that have been tared;

• a stop watch for timing the lip sample collection; and

• a note book to record the times and any other observationsthat may be of use later.

Conducting a down-the-bank survey is usually a job for twopeople. One person collects the sample, and the other measuresand records the time taken to collect each of the timed lipsamples. Prior to conducting the survey it is wise to inspect theflotation bank to be samples, clean the cell lips to ensure that thefroth flows freely, make the work area safe and free of trippinghazards, and establish how the lip sampling is going to occur,then perform a dry run to practice how the timed samples will becollected. One method of sampling that works effectively is forthe sampler to yell ‘GO’ and ‘STOP’ as he starts and finishes thesample collection. On these commands the time keeper starts andstops the stop watch. It is imperative that the two peoplecollecting the timed lip samples agree on the methodology, andwork in concert. So, with all the preparations complete, the plantoperating correctly it is time to conduct the survey.

As with other surveys you would have decided before handhow many ‘cuts’ from each process stream will be taken over aspecified sampling time. The two people detailed to conduct thedown-the-bank survey will then collect the nine samples over theprescribed sampling period. Once the sampling is finished, thesamples are gathered together and taken to the laboratory.



Samplenumber

Sample name Sample type

1 T bank feed Dip sample

2 T bank combined concentrate Lip or OSA sample

3 Con 1 – T bank cell 1 Timed lip sample

4 Con 2 – T bank cells 2 to 4 Timed lip sample



7 Con 5 – T bank cells 11 and 12 Timed lip sample

8 T bank tailing A Dip sample

9 T bank tailing B Dip sample

TABLE A1The sample list for a down-the-bank survey conducted on the

bank of 12 flotation cells pictured in Figure A1.

1. T bank feed 8. T bank tailing

3. Con 1 4. Con 2 5. Con 3 6. Con 4

Cell 11 and 12

2. T bank combined concentrate

7. Con 5

Cell 1 Cell 2 to 4 Cell 5 to 7 Cell 8 to 10

FIG A1 - A bank of 12 flotation cells, and the cell grouping for a down-the-bank survey.


In the laboratory the samples are weighed (to determine the wetweight), filtered, dried, weighed, prepped and submitted forassay. A summary of this data is given in Table A2. The wet anddry weights are used to determine the per cent solids of eachsample, and calculate a water balance. In terms of assays, apartfrom those pertaining to the valuable minerals you are separatingfrom the gangue. In example Eureka’s assayed for: silver, copper,lead, zinc, iron and silica.

You will also need the times recorded for the timed lipsamples, the lip lengths of the flotation cells, and the lip samplecutter length. With these data it will be possible to calculate theflow rate of concentrate from each of the groups of flotation cells.The data are supplied in Table A3.

Other data that can be useful when analysing the plant surveymass balance are:


• reagent additions and other plant operating parameters (ieairflow rates and pulp levels),



In the context of a one off survey some of these pieces ofinformation may not be of great value. However, when theanalysis is extended to include other surveys on other ore blends,circuit configurations, reagent suites, these data provide a vitallink in the comparison.


The assays will be returned to you in a form similar that shown inTable A4. Before proceeding with mass balancing the survey it isnecessary to check these numbers are in good order. In thisexample, the tailing assays are in good agreement, and the silver,copper and lead assays all trend in the right way (ie from higherassays in Con 1 to progressively lower numbers by Con 5).Further, the combined concentrate assay falls within the extremesof Con 1 and Con 5 assays.

Mass balancing this survey involves a number of steps. Thefirst step is to calculated the mass flow rate from the timed lipsamples. The mass flow rate is given by:



No Sample name Weights (g) % solids

Gross Tare Wet Dry

Bucket Lip

1 T bank feed 1585.3 350.0 1235.3 471.9 38.2

2 T bank combined con 1891.1 350.0 245.0 1296.1 331.8 25.6

3 Con 1 – Cell 1 1789.7 350.0 245.0 1194.7 630.8 52.8

4 Con 2 – Cells 2 to 4 2217.0 350.0 245.0 1622.0 361.7 22.3

5 Con 3 – Cells 5 to 7 2049.4 350.0 245.0 1454.4 315.6 21.7

6 Con 4 – Cells 8 to 10 2142.3 350.0 245.0 1547.3 379.1 24.5

7 Con 5 – Cells 11 and 12 1795.6 350.0 245.0 1200.6 187.3 15.6

8 T bank tailing A 1428.8 350.0 1078.8 372.2 34.5

9 T bank tailing B 1610.0 350.0 1260.0 459.9 36.5

TABLE A2Summary of the down-the-bank survey data collected.

No Sample name Sampletime (s)

Length (mm)

Cell lip Cutter lip

3 Con 1 – Cell 1 31.6 1250 × 2 105

4 Con 2 – Cells 2 to 4 28.9 1250 × 6 105

5 Con 3 – Cells 5 to 7 28.4 1250 × 6 105

6 Con 4 – Cells 8 to 10 30.8 1250 × 6 105

7 Con 5 – Cells 11 and 12 39.7 1250 × 4 105

TABLE A3Lip sample details.

No Sample name Assay (%)

Ag (ppm) Cu Pb Zn Fe SiO2

1 T bank feed 98 0.05 2.84 10.7 15.5 21.9

2 T bank combined con 444 0.24 14.20 11.1 23.0 9.2

3 Con 1 – Cell 1 644 0.47 21.40 10.7 21.2 7.6

4 Con 2 – Cells 2 to 4 490 0.28 15.70 11.3 22.9 8.5

5 Con 3 – Cells 5 to 7 406 0.21 12.60 11.3 23.8 9.1

6 Con 4 – Cells 8 to 10 346 0.18 10.60 11.6 24.2 10.0

7 Con 5 – Cells 11 and 12 306 0.18 9.15 11.7 24.3 10.9

8 T bank tailing A 70 0.02 2.04 10.5 14.8 22.6

9 T bank tailing B 72 0.02 2.13 10.6 14.9 22.6

TABLE A4Elemental assays for the down-the-bank survey.

t hDry weight of sample g

Sample time s

number

/,

,= × ×3600

106

of cell lipsCell lip length mm

Cutter lip length mm× ,

,

(A1)

Thus, by substituting the dry weights for each of the timedconcentrate samples from Table A2, and the sample times,number of cell lips, cell lip length and cutter lip length fromTable A3 in Equation (A1), you can calculate the tonnes per hourrecovered into each concentrate. For example, for Con 1 – cell 1Equation (A1) becomes:

t hg

s

mm

mmCon/. ,

. ,

,

,1 6

630 8

31 6

3600

102

1250

105= × × ×

t h t hCon/ . /1 1 71=

So, from the timed lip sample collected from Cell 1, the tonnesper hour recovered are 1.71. The same calculation was completedfor each of the other concentrate. The values obtained are given inTable A5. The tonnage distribution over the five concentratesamples was calculated, and also appear in Table A5.

The next step is to mass balance the ‘outer’ circuit. The processstreams are:

1. T bank feed,

2. T bank combined concentrate, and

3. T bank tailing.

There is only one node in this mass balance:

T bank feed = T bank combined concentrate+ T bank tailing (1 = 2 + 8).

In completing this mass balance it was assumed that the feedtonnage was 100 per cent. The mass balanced data are providedin Table A6. The mass balance has calculated that the tonnes of

concentrate produced is 6.96 tonnes per hour, compared to 11.76determined from the timed lip tonnages. The difference isreconciled by applying the tonnage distribution from Table A5 tothe mass balanced tonnage given in Table A6. Thus, using the‘outer’ circuit mass balanced tonnage new values are calculatedfor each of the concentrates. These new values are presented inTable A7, and are used in the second mass balance incorporatingthe down-the-bank concentrates.

The down-the-bank mass balance has eight process streams:

1. T bank feed,

2. T bank combined concentrate,

3. Con 1 – Cell 1,

4. Con 2 – Cells 2 to 4,



7. Con 5 – Cells 11 and 12, and

8. T bank tailing.

There are two nodes:

1. T bank feed = Con 1 + Con 2 + Con 3 + Con 4 + Con 5 + Tbank tailing (ie 1 = 3 + 4 + 5 + 6 + 7 + 8); and

2. Con 1 + Con 2 + Con 3 + Con 4 + Con 5 = T bankcombined concentrate (ie 3 + 4 + 5 + 6 + 7 = 2).

The mass balance is accomplished by fixing the tonnage valuesfor the T bank feed, and the five down-the-bank concentrates,then using a mass balancing software fitting the data. Thedown-the-bank mass balance is given in Table A8.

The mass balanced down-the-bank data can now be used toconstruct a lead grade/recovery curve for this bank of flotationcells, as shown in Figure A2. This data can also be used togenerate kinetic data examining the flotation rate constant andmaximum recovery of the various species.



No Sample name Tonnage data

Tonnes per hour Distribution

3 Con 1 – Cell 1 1.71 14.55

4 Con 2 – Cells 2 to 4 3.22 27.36

5 Con 3 – Cells 5 to 7 2.86 24.30

6 Con 4 – Cells 8 to 10 3.17 26.91

7 Con 5 – Cells 11 and 12 0.81 6.88

Total 11.76 100.00

TABLE A5The tonnes per hour and distribution of tonnes for the five

concentrates collected in the down-the-bank survey.

No Sample time Wt (%) Adjusted assay (%)

Ag (ppm) Cu Pb Zn Fe SiO2

1 T bank feed 100.00 98 0.04 2.91 10.67 15.48 21.78

2 T bank combined con 6.96 444 0.24 14.20 11.10 23.00 9.23

3 T bank tailing 93.04 72 0.03 2.07 10.63 14.92 22.72

TABLE A6The mass balanced data for the ‘outer’ balance.

No Sample name New tonnes per hour

3 Con 1 – Cell 1 1.01

4 Con 2 – Cells 2 to 4 1.90

5 Con 3 – Cells 5 to 7 1.69

6 Con 4 – Cells 8 to 10 1.87

7 Con 5 – Cells 11 and 12 0.48

Total 6.96

TABLE A7The ‘new’ tonnes per hour for the five concentrates collected in the

down-the-bank survey calculated from the tonnage distributionand the ‘outer’ mass balanced tonnage recovered into the

combined concentrate.

APPENDIX 2 – ESTIMATED MINERAL ASSAYSFROM ELEMENTAL DATA

Introduction

As iron occurs in a variety of minerals present in the Eurekaorebodies’ interpretation of iron deportment from elemental assaydata from flotation tests and plant surveys is a complex issue.Therefore, by making a few simple assumptions regarding thecomposition of the dominant sulfide minerals present in theorebody, it is possible to estimate mineral assays from theelemental assay data.

Assumptions

The first set of assumptions relate to the minerals themselves.That is, the dominant sulfide minerals are: galena; sphalerite,chalcopyrite and pyrite. The lead occurs as galena; the zinc assphalerite; the copper as chalcopyrite; and the iron occurs insphalerite, chalcopyrite and pyrite.

The mineral conversions are based on the assumption that eachmineral is ‘pure’, for example galena is PbS, pyrite is FeS2, etc.

Eureka sphalerites are known to contain, in solid solution,moderate iron levels. In this exercise 3.0 per cent was chosen.This value is an estimate of the average value.

A calculated assay of the non-sulfide gangue (NSG) is made byassuming that everything that is not galena, sphalerite,chalcopyrite, or pyrite is non-sulfide gangue.

Mineral conversions

Each conversion is based on the atomic mass of the elementalcomponents of the mineral.

Galena

Galena contains lead (207.2 amu) and sulfur (32.06 amu),therefore the atomic mass of galena (PbS) is:

PbS = Pb + S

PbS = 207.2 + 32.06 amu

PbS = 239.26 amu

Thus, ‘pure’ galena contains 86.6 per cent lead and 13.4 percent sulfur. The conversion factor (fGa) to convert the lead assayto galena is given by:

fPbS amu

Pb amuGa =

fGa = 239 26

207 2

.

.

fGa =1155.

So, an assay of ten per cent lead is equivalent to 11.6 per centgalena.

The sulfur in galena conversion factor (fSGa) is given by:

fsulfur in PbS

lead in PbSSGa = %

%

f SGa = 13 4

86 6

.

.

f SGa = 0155.

Sphalerite

‘Pure’ sphalerite (contains no iron) is made up of zinc(65.38 amu) and sulfur (32.06 amu), therefore the atomic mass of‘pure’ sphalerite (ZnS) is:

ZnS = Zn + S

ZnS = 65.38 + 32.06 amu

ZnS = 97.44 amu

Thus, ‘pure’ sphalerite contains 67.1 per cent zinc and 32.9 percent sulfur. Unfortunately, Eureka sphalerite contains an averageof 3.0 per cent iron in solid solution. Further, it is assumed that



No Sample name Wt(%)

Weight (%) Recovery (%)

Ag Cu Pb Zn Fe SiO2 Ag Cu Pb Zn Fe SiO2

1 T bank feed 100.00 98 0.04 2.9 10.6 15.5 21.8 100.0 100.0 100.0 100.0 100.0 100.0

2 T bank combined con 7.00 440 0.26 14.0 11.3 23.3 9.1 31.5 42.0 33.6 7.4 10.5 2.9

3 Con 1 – Cell 1 1.02 644 0.47 21.4 10.7 21.2 7.6 6.7 11.2 7.5 1.0 1.4 0.4

4 Con 2 – Cells 2 to 4 1.91 490 0.28 15.7 11.3 22.9 8.5 9.6 12.5 10.3 2.0 2.8 0.7

5 Con 3 – Cells 5 to 7 1.70 406 0.21 12.6 11.3 23.8 9.1 7.0 8.3 7.4 1.8 2.6 0.7

6 Con 4 – Cells 8 to 10 1.88 346 0.18 10.6 11.6 24.2 10.0 6.6 7.9 6.9 2.0 2.9 0.9

7 Con 5 – Cells 11 and 12 0.49 306 0.18 9.2 11.7 24.3 10.9 1.6 2.1 1.5 0.6 0.8 0.2

8 T bank tailing 93.00 72 0.03 2.1 10.6 14.9 22.7 68.5 58.0 66.4 92.6 89.5 97.1

TABLE A8The down-the-bank mass balance.

0.0

5.0

10.0

15.0

20.0

25.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0

Pb recovery, %

OUTER balance data Down-the-bank balanced data

Pb

gra

de

,%

FIG A2 - The lead grade/recovery curve constructed from themass balanced down-the-bank survey data.

the iron replaced zinc in the sphalerite lattice. Thus, thecomposition of Eureka sphalerite is 64.1 per cent zinc, 3.0 percent iron and 32.9 per cent sulfur. Hence, the conversion factor(fSp) to convert the zinc assay to sphalerite is given by:

fZn in sphaleriteSp = 100 00.

%

f Sp = 100 00

641

.

.

f Sp =1 560.

So, an assay of ten per cent zinc is equivalent to 15.6 per centsphalerite. To determine the amount of iron present in sphaleriteit is simply a matter of multiplying the zinc assay by the ratio ofthe per cent iron in the sphalerite and the per cent zinc in thesphalerite. So, the iron in the sphalerite conversion factor (fFeSp) isgiven by:

firon in sphalerite

zinc in sphaleriteFeSp = %

%

fFeSp = 3 0

641

.

.

fFeSp = 0 047.

Thus, for a sample containing ten per cent zinc, the per centiron associated with the sphalerite is 0.47 per cent (ie 10 × 0.047).To check this, convert the zinc assay to per cent sphalerite(ie 10 × 1.560), and multiply by the amount of iron in solidsolution in sphalerite (ie 3.0 per cent), and the per cent ironassociated with sphalerite is 0.47 per cent.

Similarly, the sulfur in the sphalerite conversion factor (fSSp) isgiven by:

fsulfur in sphalerite

zinc in sphaleriteSSp = %

%

f SSp = 32 9

641

.

.

f SSp = 0 513.

Thus, for a sample containing ten per cent zinc, the per centsulfur associated with the sphalerite is 5.13 per cent (ie 10 ×0.513). To check this, convert the zinc assay to per cent sphalerite(ie 10 × 1.560), and multiply by the amount of sulfur in sphalerite(ie 32.9 per cent), and the per cent sulfur associated withsphalerite is 5.13 per cent.

Chalcopyrite

Chalcopyrite is made up of copper (63.55 amu), iron ( 55.85 amu)and sulfur (32.06 amu), therefore the atomic mass of chalcopyrite(CuFeS2) is:

CuFeS2 = Cu + Fe + S

CuFeS2 = 63.55 + 55.85 + (2 x 32.06) amu

CuFeS2 = 183.52 amu

Thus, stoichiometric chalcopyrite contains 34.63 per centcopper, 30.43 per cent iron and 34.94 per cent sulfur. Hence, theconversion factor (fCh) to convert the copper assay to chalcopyriteis given by:

fCuFeS amu

Cu amuCh = 2

fCh = 183 52

63 55

.

.

fCh = 2 888.

So, an assay of ten per cent copper is equivalent to 28.9 percent chalcopyrite. To determine the amount of iron present inchalcopyrite it is simply a matter of multiplying the copper assayby the ratio of the per cent iron in the chalcopyrite and the percent copper in the chalcopyrite. So, the iron in chalcopyriteconversion factor (fFeCh) is given by:

firon in chalcopyrite

copper in chalcopyriteFeCh = %

%

fFeCh = 30 43

34 63

.

.

fFeCh = 0 879.

Thus, for a sample containing ten per cent copper the per centiron associated with the chalcopyrite is 8.8 per cent (ie 10 ×0.879). To check this, convert the copper assay to chalcopyrite (ie10 × 2.888), and multiply this by the amount of iron in solidsolution in chalcopyrite (ie 30.43 per cent), and the per cent ironassociated with chalcopyrite is 8.8 per cent.

Similarly, the sulfur in chalcopyrite conversion factor (fSCh) isgiven by:

fsulfur in chalcopyrite

copper in chalcopyriteSCh = %

%

f SCh = 34 94

34 63

.

.

f SCh =1 009.

Thus, for a sample containing ten per cent copper the per centsulfur associated with the chalcopyrite is 10.1 per cent (ie 10 ×1.009). To check this, convert the copper assay to chalcopyrite (ie10 × 2.888), and multiply this by the amount of sulfur in solidsolution in chalcopyrite (ie 34.94 per cent), and the per centsulfur associated with chalcopyrite is 10.1 per cent.

Pyrite

Assuming that pyrite is the dominant iron sulfide mineral present(ie no pyrrhotite) it is relatively easy to calculate the per centpyrite in a sample. Pyrite contains iron (55.85 amu) and sulfur(32.06 amu), therefore the atomic mass of pyrite (FeS2) is:

FeS2 = Fe + S

FeS2 = 55.85 + (2 x 32.06) amu

FeS2 = 119.97 amu

Thus, ‘pure’ pyrite contains 46.6 per cent iron and 53.4 per centsulfur. Depending on the assay data the pyrite content of the orecan be calculated using either the iron or the sulfur assay.Generally speaking, the estimation of the pyrite concentration ismore accurate using the sulfur assay because it is assumed thatthe vast majority of the sulfur in the ore is associated with thesulfide minerals. Iron, on the other hand, can be present in thesulfides as well as the non-sulfide gangue, for example as ironoxides such as haematite or magnetite, and feldspars to name buta few. Thus, the pyrite conversion factor (fFePy) based on the ironassay is given by:

fFeS amu

Fe amuFePy = 2



fFePy = 119 97

5585

.

.

fFePy = 2148.

So, based on the conversion factor developed from the ironassay, an assay of ten per cent iron is equivalent to 21.5 per centpyrite.

Using sulfur, the pyrite conversion factor (fSPy) is given by:

fFeS amu

S amuSPy = 2

2

f SPy = 119 97

6412

.

.

f SPy =1871.

So, based on the conversion factor developed from the sulfurassay, an assay of ten per cent iron is equivalent to 18.7 per centpyrite.

Unfortunately, iron is also associated with sphalerite andchalcopyrite in the ore, so these contributions must be subtractedfrom the iron assay before determining the pyrite content. Thus,the pyrite conversion factor (fFePy) based on the iron assaybecomes:

f

Fe iron in sphalerite iron in calcopy

FePy = ×

− +

2148.

(% (% % rite))

From the discussion above the conversion factor for iron insphalerite from the zinc assay was determined to be 0.0.47, and asimilar calculation revealed that the conversion factor for iron inchalcopyrite is 0.879. So, the per cent pyrite is given by:

f

Fe Zn Cu

FePy = ×

− × + ×

2148

0 047 0 879

.

(% ( . % . % ))

Thus, if an ore sample contained ten per cent iron, ten per centzinc, and 0.1 per cent copper, the per cent pyrite is 20.28 per cent.

However, if the pyrite conversion factor (fSPy) based on thesulfur assay then:

f S sulfur in galena

sulfur in sphalerit

SPy = × − +1871. (% (%

% e sulfur in chalcopyrite+ % ))

From the discussion above the conversion factor for sulfur ingalena from the lead assay is 0.155; sulfur in sphalerite from thezinc assay was determined to be 0.513, and a similar calculationrevealed that the conversion factor for sulfur in chalcopyrite is1.009. So, the per cent pyrite is given by:

f

S Pb Zn Cu

SPy = ×

− × + × + ×

1871

0155 0 513 1 009

.

(% ( . % . % . % ))

Thus, if an ore sample contained ten per cent sulfur, ten percent lead, ten per cent zinc, and 0.1 per cent copper, the per centpyrite is 6.0 per cent.

Non-sulfide gangue

If it is assumed that galena, sphalerite, chalcopyrite, and pyriteare the only significant sulfide minerals present in the orebody itis possible to estimate the per centage of non-sulfide ganguepresent. This is achieved by subtracting the per cent galena,sphalerite, chalcopyrite, and pyrrhotite from 100 per cent. That is:

% NSG = 100 – (%Ga + %Sp + %Ch + %IS)

Thus, if a sample assayed ten per cent lead, zinc and iron, and0.1 per cent copper, the per cent non-sulfide gangue is 57.8 percent.

Final comment

These element to mineral conversions have been completed basedon a lead/zinc ore, however a similar approach can be applied toother ore types provided the stoichiometry of the minerals in thesystem is known, and the assumptions made in the calculationsare clearly explained.



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