1. business and industrial
2. energy
3. coal

HOME
CHAPTER 1
The Eureka Mine – An Example of How to Identify and
Solve Problems in a Flotation Plant
Christopher Greet
MAusIMM, Manager Metallurgy – Minerals Processing Research, Magotteaux Australia Pty Ltd, 31
Cormack Road, Wingfield SA 5013. Email: christopher.greet@magotteaux.com
Chris commenced his working life as a Trainee Metallurgist at Bradken’s Adelaide steel foundry in 1978. He
subsequently worked in a number of foundries, before becoming a shift foreman at Seltrust’s Teutonic Bore
Mine in 1982. In 1985 he decided to formalise his knowledge by studying for a Bachelor of Engineering in
Metallurgical Engineering at the South Australian Institute of Technology. Upon graduating, he worked as a
Plant Metallurgist at Ok Tedi Mining Limited and Bradken Adelaide, before undertaking a PhD at the Ian
Wark Research Institute in 1992. Since leaving the Wark, Chris has held applied research positions at
Mount Isa Mines Limited, Pasminco, AMDEL, and now with Magotteaux Australia, where he leads the
technical 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
ABSTRACT
As the title implies, this chapter will provide, by way of example, a
methodology for identifying and solving problems in a flotation plant. To
do this a ‘mythical’ concentrator (The Eureka Mine) will be described
and used to demonstrate to the reader how to go about the process of
identifying where the losses of valuable mineral occur, and what gangue
species are diluting the concentrate. It is intended that this chapter be
used in parallel with subsequent chapters to guide the reader through the
steps involved in the process of optimising the plant.
INTRODUCTION
Many of us have, at one time or another, flicked through the
newspaper or searched online for a job, and stumbled upon an ad
not unlike the one that appears below. Whether we are jaded with
our current role, looking for a step up to the next level, or wanting
Flotation Plant Optimisation
a new challenge, we prepare our resume and send it off in the
vain hope that we may be the successful candidate.
With the interview process out of the way, the waiting and self
doubt start. And, after what seems an eternity you receive a phone
call 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 a
right way and a wrong way to do this. The first thing to remember
is that this place has a history, and the people you are going to
work with have been here much longer than you have. So,
communication and respect are keys to your success. You need to
discover the history of the concentrator, and discuss its operation
with other members of staff (operators, metallurgical technicians,
shift foremen, plant metallurgists, the chemist, mechanical and
electrical maintenance, the mine (ie geologists and mining
engineers) and supply). They will all give you their perspective,
Spectrum Series 16
1
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 the
process,
• develop, test, evaluate and implement process
improvements,
• identify and evaluate new technologies that will improve
To assist you in this journey the performance of the Eureka
Mine will be scrutinised and used as an example. The Eureka
Mine treats a complex polymetallic sulfide ore supplied from an
underground mine, producing three saleable concentrates. This
chapter provides a description of the Eureka Concentrator, and its
metallurgical performance since commissioning. Then provides,
by example, the methodology used to collect plant data, analyse
and interpret it to determine where the metallurgical problems lie.
our process, and
THE EUREKA MINE
• be actively involved in our future ores testing program.
Operating in a climate of continuous improvement, you will
be required to actively participate in the promotion of safe
work practices, have good interpersonal skills and a
professional work ethic.
Skills and experience
The successful applicant candidate will have the following
attributes:
• Bachelor of Engineering – Metallurgy or equivalent,
• five years or more process experience,
• a working knowledge of grinding and flotation
processes,
•
•
•
•
good communication skills,
Extensive geological surveying of the region north of Laylor
River by Stockade Resources Limited resulted in the discovery of
the Eureka deposit in 1990. Throughout 1991 and 1992, drilling
continued to delineate the deposit. Preproduction geological
studies indicated probable reserves of approximately 25 million
tonnes of greater than 15 per cent zinc plus lead, with
economically significant copper, silver and gold grades.
A one million tonnes per annum processing plant was
commissioned by Stockade in 1995 to treat ore with an average
head grade of 0.4 per cent copper; three per cent lead; 12 per cent
zinc; 130 g/t silver; and 2.0 g/t gold.
Geology
proven problem solving skills,
good time management skills, and
have sound leadership qualities and management skills.
Employment conditions
Eureka Mining Limited is a wholly owned subsidiary of
Stockade Resources Limited, an Australian based mining
and exploration company with interests in Australia, New
Guinea, Zambia and Peru.
Reporting to the Metallurgy Manager, the successful
candidate will work closely with production to maintain and
improve plant performance. This is a residential position,
with an attractive renumeration package commensurate with
your qualifications and experience.
To apply
Please submit your application including a cover letter and
current copy of your resume via email to ...
and you need to respect their point of view. You will also work
out who among these people hold the knowledge, the history; the
real story of your concentrator.
While you are establishing relationships you need to determine
what (if any) data exists that will help you to develop a technical
perspective of how your concentrator performs.
The focusing questions in any process improvement strategy
are:
• Where and how do the losses of valuable mineral occur?
• What gangue minerals are diluting the concentrate and how
did they get there?
The intention of this book is to provide you with a sequence of
logical steps to follow so that you can collect the necessary data
to be able to define the problem(s) within your operation. Once
the problems are defined, you can then prioritise them and
develop experimental strategies that may lead to solutions that
can be implemented in the plant.
2
Location and history
The Eureka volcanogenic massive sulfide deposit occurs within
the Laylor-Eureka Volcanic sequence of the Mount Rush
Volcanics. The deposit was formed when hot mineralised
solutions were spewed out on to the ocean floor and were rapidly
quenched by the surrounding seawater. Hence, the sulfide
minerals that were precipitated from solution formed very fine
crystals and intricate mineral textures. Subsequent geological
changes to the orebody were few; therefore many of the original
fine grain textures remained intact.
Deposit mineralogy
To fully appreciate the complexity of flotation at Eureka it is
necessary to have a rudimentary understanding of the mineralogy
of the orebody. Eureka is unusually sulfide rich, and contains a
relatively simple mineral suite: 58 per cent pyrite, 20 per cent
sphalerite, four per cent galena, two per cent arsenopyrite, one per
cent chalcopyrite, with minor amounts of tetrahedrite. The
remaining 15 per cent of the ore consists of: quartz, barite,
calcite, chlorite, sericite and siderite.
Macroscopically the mineral textures are diverse, however, the
orebody can be divided into two distinct metal zones. The
demarcation between the two zones is set, arbitrarily, at 100 g/t of
silver, and represents a continuous horizon across the deposit.
Above this level is the hanging wall enrichment zone characterised
by higher lead, zinc, silver, gold, and arsenic grades.
Macroscopically the sulfides within the enrichment zone tend to be
banded and very fine grained. The footwall-depleted zone occurs
below the 100 g/t silver horizon. Pyrite and chalcopyrite are the
dominant minerals within this part of the orebody, with reduced
lead, zinc, silver, gold, and arsenic grades. The footwall-depleted
zone is highly recrystallised, therefore the grain structure is
comparatively coarse when compared with those observed in the
hanging wall enrichment zone.
It is important to note that the Eureka orebody is relatively free
of non-sulfide gangue mineralisation. So, pyrite is the dominant
gangue mineral, and is associated with all other minerals within
the deposit. Therefore, the properties of pyrite will influence
greatly the behaviour of all other minerals during processing.
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
The pyrite textures vary markedly across the deposit from coarse
recrystallised grains in the footwall-depleted zone to compact
microcrystalline masses, spongy and colliform clots, such as
melnokovite (an amorphous pyrite of colloidal origin). Ultra-fine
intergrowths of pyrite with other sulfides are common, particularly
with galena and arsenopyrite (Figure 1). The association of
auriferous arsenopyrite with pyrite is also of significance.
Process description
Laboratory testing of the Eureka ore clearly demonstrated that it
was possible to produce saleable copper, lead and zinc
concentrates. The flow sheet developed in the laboratory was
tested at pilot scale to prove that the process route selected was
robust, and to produce sufficient quantities of concentrate for
smelter testing. The final Eureka process flow sheet is presented
in Figure 2.
As the ore is mined the mine geologists classify it into three
broad ore types based on texture (ie enrichment zone ore
(banded), or footwall depleted zone ore (coarse grained)), and
estimated pyrite content. Each ore type is crushed in batches to
nominally 100 per cent passing 100 mm, in the underground jaw
crusher before being trucked to the surface in 50 tonne dump
trucks. Upon delivery to the run-of-mine (ROM) pad each ore
type is stockpiled separately. The ore is fed onto a conveyor belt
that leads to an open stockpile in specific ratios of each ore type.
Apron feeders, underneath the open stockpile, feed the blended
ore onto the primary mill feed conveyor at nominally 120 t/h.
The primary mill is a low aspect ratio semi-autogenous
grinding (SAG) mill in open circuit. The SAG mill product
discharges into a common sump shared with the ball mill. The
pulp is pumped from the mill discharge sump to cyclones in
closed circuit with a secondary ball mill. The cyclone underflow
feeds the secondary ball mill, and the cyclone overflow reports to
flotation feed. The cyclone configuration is designed to produce a
P80 of 75 microns.
The secondary cyclone overflow feeds a sequential copper/
lead/zinc flotation circuit. Each flotation section consists of a
rougher/scavenger, with the rougher concentrate reporting to the
cleaner circuit. The copper cleaning is achieved without regrinding,
and with only one stage of cleaning. The lead rougher concentrate
feeds the first of three stages of cleaning. The lead scavenger
concentrate and lead first cleaner tailing are reground and recycled
back to the head of the lead rougher. The lead scavenger tailing
reports to the zinc circuit feed. The zinc rougher concentrate
reports to two stages of cleaning. The zinc scavenger concentrate
and the zinc first cleaner tailing are reground and recycle back to
the zinc rougher feed. The concentrates produced from the copper,
lead and zinc flotation circuits are pumped to thickeners. The
thickened concentrate is filtered. The filter cake is stockpiled
before loading into rail cars for shipment to the smelter.
The flotation tailing is dewatered, and used as paste backfill in
the underground workings.
Metallurgical performance
The Eureka concentrator was commissioned in December 1995,
reaching name plate throughput by July 1996. A further 18
months were required to achieve the design concentrate grades
and recoveries. Typical metallurgical performance since 1998 is
summarised 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.)
Flotation Plant Optimisation
Spectrum Series 16
3
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
Copper Circuit
Lead Circuit
Cu Ro
Zinc Circuit
Pb Scav
Pb Ro
Zn Scav
Zn Ro
Feed
Zn Scavenger Tailing
Grinding
st
Zn 1 Cl
P80 = 75 microns
Copper Cleaner Concentrate
Cu Cl
Zn 2nd Cl
Pb 1st Cl
Zinc 2nd Cleaner Concentrate
Zinc Regrinding
P80 = 38 microns
Pb 2nd Cl
Lead Regrinding
P80 = 38 microns
Pb 3rd Cl
Lead 3rd Cleaner Concentrate
FIG 2 - The Eureka Concentrator flow sheet.
TABLE 1
Typical metallurgical performance of the Eureka Concentrator since 1998.
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
DATA ACQUISITION
The first step towards a better understanding of how your plant is
performing is being able to measure the plant’s performance. In
today’s modern concentrator the process can be monitored using
a multitude of sensors, however the data collected from inventory
samples on a shift, daily, weekly and monthly basis, coupled with
well executed metallurgical surveys can be invaluable in defining
where valuable mineral losses occur and what gangue minerals
are diluting the concentrate.
In the first instance, as the new metallurgist you should
acquaint yourself with the existing plant data. That is, review the
shift mass balance data, interrogate the monthly composite data,
and examine any plant surveys that have been conducted in the
past. This analysis should provide some indication of where the
metallurgical weaknesses lie in the concentrator. However, do not
be surprised if the only data that is up-to-date and readily
available are the shift mass balances. Therefore, it should be
considered good practice to organise for a plant survey to be
completed reasonably early in the piece so that you can quantify
the metallurgical performance of each section of the plant.
The shift data for the copper, lead and zinc circuits of the
Eureka Concentrator appear in Figure 3 for the first half of 1998.
4
An examination of these data suggest that with the exception of
the lead and zinc concentrate grades the plant performance is
somewhat unstable. The copper concentrate grade and recovery
tend to fluctuate wildly, lead recoveries are highly varied, with
greater than ten per cent zinc grade in the lead concentrate, and
zinc 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 lower
than you would expect, therefore it should be possible to improve
the plant performance. But you don’t know what the limiting
factors are so completing a comprehensive plant survey is in
order.
The next item to decide is what level of detail does the survey
have to go? This will obviously depend on what work has been
completed previously. In this example it will be assumed that the
available data is scattered and incomplete. Therefore, the
objective of the survey will be to collect as much data as possible
to provide you with sufficient information to describe the pulp
chemistry and metallurgical performance of the plant. Ideally
both sets of data can be collected in tandem, and complement
each other. To add further value to your survey the collection of
gas hold-up, superficial gas velocity and bubble size data will
provide information about the hydrodynamics of the flotation
cells.
Spectrum Series 16
Flotation Plant Optimisation
90.0
30.0
75.0
27.0
60.0
24.0
45.0
21.0
30.0
18.0
15.0
15.0
0.0
12.0
1/
01
/9
8
15
/0
1/
98
29
/0
1/
9
12 8
/0
2/
9
26 8
/0
2/
9
12 8
/0
3/
9
26 8
/0
3/
98
9/
04
/9
23 8
/0
4/
98
7/
05
/9
21 8
/0
5/
98
4/
06
/98
18
/0
6/
98
Cu recovery, %
(a)
Cu grade, %
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
Time, days
Cu grade
90.0
90.0
75.0
75.0
60.0
60.0
45.0
45.0
30.0
30.0
15.0
15.0
0.0
0.0
Pb recovery
Half moon cutter
Time, days
2
Copper rougher concentrate
Lip sample
Pb grade
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
Zn grade in Pb con
60.0
90.0
50.0
80.0
40.0
70.0
30.0
60.0
20.0
50.0
10.0
40.0
0.0
1/
01
/9
8
15
/0
1/
98
29
/0
1/
98
12
/0
2/
98
26
/0
2/
98
12
/0
3/
98
26
/0
3/
98
9/
04
/9
8
23
/0
4/
98
7/
05
/9
8
21
/0
5/
98
4/
06
/9
8
18
/0
6/
98
Zn recovery, %
Sample type
Flotation feed (ball mill cyclone
overflow)
100.0
Zn recovery
Process stream
1
Zn and Fe grade, %
(c)
TABLE 2
The sampling points and sample type for the metallurgical survey
of the Eureka flotation circuit.
Sample
number
1/
01
/9
8
15
/0
1/
98
29
/0
1/
98
12
/0
2/
98
26
/0
2/
98
12
/0
3/
98
26
/0
3/
98
9/
04
/9
8
23
/0
4/
98
7/
05
/9
8
21
/0
5/
98
4/
06
/9
8
18
/0
6/
98
Pb recovery, %
(b)
Pb and Zn grade, %
Cu recovery
product should be conducted. Ideally, a more detailed
down-the-bank survey of the rougher/scavenger sections would
also be included. An example of a down-the-bank survey is
provided in Appendix 1. Further, to gain greater appreciation of
how the circuit operates, more than one plant survey should be
completed over a number of days. In fact, a good strategy to
follow would be one detailed survey and several less detailed
block surveys be completed to give a more balanced view of the
metallurgical performance of the plant.
Organisation and communication are the key to successfully
completing surveys in a concentrator. In the first instance, decide
on the sampling points and the type of sample to be taken. A list
of samples for the Eureka concentrator is given in Table 2. It is
important to note that all tailing samples are taken in duplicate to
ensure sampling consistency, and are used as internal checks
(note: the recovery calculation is dependent on the tailing assay).
12
Lead scavenger tailing A
Dip sample
Time, days
13
Lead scavenger tailing B
Dip sample
Zn grade
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
Fe grade in Zn con
FIG 3 - Time series data for: (A) copper; (B) lead; and (C) zinc
circuits.
The metallurgical survey (Chapter 2)
20
Lead third cleaner concentrate
Lip sample or OSA
Why don’t metallurgists do surveys?
21
Lead third cleaner tailing A
Dip sample
The author has visited many plants around the world, and it is
apparent that there is a wide spectrum of knowledge and
experience. However, plants that conduct frequent, well focused
surveys and have a clear understanding of their metallurgical
performance, its strengths and weaknesses, are few and far
between. The reasons for this are many and varied, but generally
boil down to not knowing how to conduct a survey, and the fear
of mass balancing!
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
What do I have to do?
The objectives of the survey(s) are to provide information about
rougher/scavenger flotation performance in each of the flotation
circuits, and to examine how the concentrates up grade during
cleaning. In the first instance block surveys of each of the
flotation sections (roughers, scavengers and cleaners) for each
Flotation Plant Optimisation
Spectrum Series 16
5
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
There are other process streams that you may consider collecting.
For example, the copper rougher feed, lead rougher feed, and zinc
rougher feed, just to name a few, that are redundant samples in
the mass balance, but do provide valuable data for checking
assays, and ensuring that the mass balanced data makes sense.
With the sample list decided upon it is necessary to brief the
team conducting the survey, prepare the equipment, and ensure
that the plant is running in a fashion that will allow it to be
surveyed. During this briefing, delegate tasks to each person
taking part in the survey so that:
• sufficient sample buckets with lids are cleaned, weighed and
labelled;
• the sampling equipment is checked, clean and ready for use;
• each person taking part in the survey knows what is expected
of them; and
• set the ground rules for the survey (ie number of rounds over
what time interval).
elements like arsenic, antimony, bismuth, mercury and/or
cadmium. Knowing how these penalty elements deport to the
concentrate can lead to a way of minimising their recovery, which
translates to a potential decrease in smelter penalties.
Other data that can be useful when analysing the plant survey
mass balance are:
• the throughput at the time of the survey,
• reagent additions and other plant operating parameters (ie air
flow 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 of
information may not be of great value. However, when the
analysis is extended to include other surveys on other ore blends,
circuit configurations, reagent suites, these data provide a vital
link in the comparison.
Further, it is wise at this point to inform operations of your
intentions to conduct a plant survey(s). Give then the reasons for
conducting the surveys and check that the plant will be available
at the time you anticipate the survey(s) will be completed. You
also need to inform the chemical laboratory that you intend to
submit a large number of samples from a plant survey, and you
would like to have them assayed for a range of elements.
The day before conducting the survey it is wise to visit the plant
and inspect the flotation circuit to make sure that all those
participating in the survey know where the sampling points are that
they are responsible for. It is also a time when flotation cell lips can
be checked for build-up and cleaned in readiness for the survey. Of
equal importance during the plant inspection is to ensure that the
work area is free of hazards, and is safe to work in.
On the day of the survey, attend the morning production
meeting to establish how the plant has been operating overnight
and inform operations of your intentions to survey the plant. Once
you have the all clear commence setting up for the survey, set out
the buckets in the correct positions, station the sampling
equipment accordingly, check the flotation cell lips and clean
them again. Check again that your team is familiar with the task
ahead of them.
Now that you are ready, go to the control room and check on
the status of the concentrator. Ensure that the feed tonnage is
steady, the feed grades are steady, and the circuit has not
experienced a major disturbance in the last few hours. Once you
are satisfied that the plant is running smoothly you can start the
survey.
It is suggested that a survey of this type be conducted over a
number of hours, whereby multiple rounds of samples are collected
to form a composite of the sampling period. In this particular case,
the survey was conducted over a three hour period, during which
time four ‘cuts’ from each sampling point were collected randomly.
Once the metallurgical survey is completed, the samples are
gathered together and taken to the laboratory.
How do I analyse the data collected (Chapter 3)?
What data do I need?
Step 2
In the laboratory the samples are weighed (to determine the wet
weight), filtered, dried, weighed, prepped and submitted for
assay. A summary of this data is given in Table 3. The wet and
dry weights are used to determine the per cent solids of each
sample, and calculate a water balance.
In terms of assays, apart from those pertaining to the valuable
minerals you are separating from the gangue (in Eureka’s case –
copper, lead, zinc, silver and gold), it is also wise to assay for
other elements. For example, iron and sulfur, to aid in performing
mineral conversions so that information about the sulfide gangue
can be extracted from the elemental assays, and deleterious trace
Balance the ‘outer’ circuit. That is, complete a mass balance of
the feed, final concentrate and tailing for the copper, lead and
zinc circuits (Figure 4). This balance will provide estimates of the
tonnes of copper, lead and zinc concentrates produced. These
values can be used in subsequent balances to estimate tonnages of
the internal process streams within rougher, scavenger and
cleaner circuits.
Assign each process stream a number, and identify each of the
nodes. So, the process streams are:
6
Once the assays have returned from the chemical laboratory the
fun starts! You will be confronted with a list of numbers not
unlike that presented in Table 4. Initially, this may be a little
daunting, but once you have organised the data into a logical
format, and brought order to the chaos you will be in a position to
mass balance this survey.
At the beginning of the mass balancing process it is necessary to
make sure that you have received all the assays and that they are in
good order. That is, do the tailing assays match? Do the
concentrate assays follow a logical trend (for example, the rougher
concentrate grade is higher than the scavenger concentrate, the
cleaner concentrate grades increase as you pass from the first to the
third cleaner)? Once you have satisfied yourself that the assays are
good you can start the mass balancing process.
Remember, if a water balance of the flotation circuit is required
this can be achieved using the per cent solid values for each
process 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 the
basic steps involved in completing the mass balance are the same
for all. Unfortunately, it is not a case of plugging the numbers in
and pressing ‘GO’, as invariably this leads to process streams
having zero flow!
The basic steps to following when mass balancing a survey are
given 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 was
completed. If the feed tonnage is not available, assign the fresh
flotation feed a value of 100 per cent. It is further assumed that
you have a high level of confidence in this value.
1.
Spectrum Series 16
flotation feed,
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE 3
Raw data from the metallurgical survey of the Eureka Concentrator.
Sample
number
Process stream
Sample weight (g)
Gross wet
Bucket
% Solids
Cutter
Net wet
Dry
1880.9
909.8
48.4
347.8
1384.2
708.2
51.2
46.5
1
Flotation feed
2083.5
202.6
2
Copper rougher concentrate
1961.2
229.2
3
Copper rougher tailing A
2509.5
466.3
2043.2
949.7
4
Copper rougher tailing B
2480.9
466.0
2014.9
936.1
46.5
5
Copper cleaner concentrate
1386.7
225.0
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
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
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
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
17
Lead second cleaner concentrate
5832.8
214.4
18
Lead second cleaner tailing A
2814.2
19
Lead second cleaner tailing B
2828.9
20
Lead third cleaner concentrate
2721.8
200.6
21
Lead third cleaner tailing A
2905.2
22
Lead third cleaner tailing B
2881.6
23
Zinc rougher concentrate
2779.3
226.4
24
Zinc rougher tailing A
2090.7
25
Zinc rougher tailing B
1992.0
26
Zinc scavenger concentrate
1565.6
200.4
27
Zinc scavenger tailing A
1983.0
234.9
28
Zinc scavenger tailing B
1753.0
225.5
29
Zinc first cleaner concentrate
3945.3
202.4
30
Zinc first cleaner tailing A
2316.8
31
Zinc first cleaner tailing B
2321.4
32
Zinc second cleaner concentrate
4209.1
211.9
33
Zinc second cleaner tailing A
2466.7
225.5
34
Zinc second cleaner tailing B
2474.3
234.0
324.8
341.0
340.9
341.1
2415.7
1343.7
55.6
5293.1
3547.9
67.0
233.6
2580.6
1608.0
62.3
228.2
2600.7
1424.7
54.8
2180.2
1395.7
64.0
238.1
2667.1
1635.1
61.3
225.8
2655.8
1650.7
62.2
2211.7
1287.8
58.2
224.9
1865.8
681.5
36.5
234.7
1757.3
638.8
36.4
1040.3
500.5
48.1
1748.1
629.1
36.0
1527.5
536.5
35.1
3401.7
1947.7
57.3
225.6
2091.2
1004.9
48.1
225.4
2096.0
1005.5
48.0
3656.0
2020.2
55.3
2241.2
1250.5
55.8
2240.3
1248.2
55.7
3. Cu scav tailing
325.3
341.0
341.2
324.9
341.2
341.2
5. Pb scav tailing
Cu Circuit
(1)
Pb Circuit
(2)
Zn Circuit
(3)
2. Cu Clnr Con
(Final Cu Con)
4. Pb 3rd Clnr Con
(Final Lead Con)
6. Zn 2nd Clnr Con
(Final Zn Con)
1. Flotation feed
7. Zn Scan Tail
(Final Tail)
FIG 4 - The ‘outer’ circuit.
2.
copper cleaner concentrate,
5.
lead scavenger tailing,
3.
copper rougher tailing,
6.
zinc second cleaner concentrate, and
4.
lead third cleaner concentrate,
7.
zinc scavenger tailing.
Flotation Plant Optimisation
Spectrum Series 16
7
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
this assay would be rejected from the data set. It is likely that
having this sample reassayed would only confirm that the sample
was contaminated during collection. However, care must be taken
when rejecting assays because it may not always the case. This
sample has a similar assay to that obtained for the copper rougher
concentrate, so another possibility may be that samples have been
wrongly labelled.
Having sorted the data, identified the process streams, nodes
and feed tonnage, the numbers can be plugged into your mass
balancing program. In this case MATBAL, using a Monte Carlo
simulation, was employed. The resultant mass balance is
provided in Table 5, which includes a sigma value. If sigma is
less than five per cent, then the data are considered to be good.
The nodes are:
1.
flotation feed = Cu cleaner concentrate + Cu rougher tailing
(or, 1 = 2 + 3);
2.
Cu rougher tailing = Pb third cleaner concentrate + Pb
scavenger tailing (or, 3 = 4 + 5); and
3.
Pb scavenger tailing = Zn second cleaner concentrate + Zn
scavenger tailing (or, 5 = 6 + 7).
In this case, at the time of surveying the plant, throughput was
120 t/h.
An examination of the tailing assays reveals that the assays are
reasonably similar and in the expected range (indicating that the
sampling was of a good standard). Therefore, taking an average
of the two tailing sample assays is acceptable. However, if one
assay was considerably higher than the other (for example, the
copper cleaner tailing A, in Table 4), one suspects that this dip
sample has been contaminated with froth during collection, and
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.
TABLE 4
Raw assay data from the metallurgical survey of the Eureka Concentrator.
Sample
number
Process stream
% Solids
Raw assays
Pb (%)
Zn (%)
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
8
Ag (ppm)
Cu (%)
Spectrum Series 16
Fe (%)
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE 5
Adjusted assays for the ‘outer’ circuit mass balance.
No
Stream
t/h
1
Flotation feed
2
Cu cleaner concentrate
3
Cu rougher tailing
4
Pb 3rd cleaner concentrate
5
Pb scavenger tailing
6
Zn 2nd cleaner concentrate
7
Zn scavenger tailing
Adjusted assays (%)
120.00
Ag (ppm)
Cu
Pb
Zn
Fe
0.09
135
0.43
3.99
15.04
13.17
1.21
3.31
229
22.40
4.07
7.80
16.11
118.79
0.10
95
0.21
3.99
15.12
13.14
6.76
2.19
1060
1.07
61.01
9.42
1.70
112.03
0.16
28
0.15
0.55
15.46
13.83
27.70
2.14
39
0.29
1.18
59.92
5.35
84.33
0.73
12
0.11
0.34
0.86
16.62
1. Flotation feed
3. Cu rougher tailing
Cu rougher (1)
2. Cu rougher concentrate
5. Cu cleaner tailing
Cu cleaner (2)
4. Cu cleaner concentrate
(Final Cu Con)
FIG 5 - The copper circuit ‘inner’ mass balance.
These internal balances are broken down into ‘bite size’ pieces to
simplify the balancing process, and generate estimates of
tonnages that can be used in subsequent ‘inner’ circuit balances.
The copper circuit (Figure 5) is comparatively easy to balance
using the tonnage estimates for the flotation feed and copper
cleaner 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,
3.
copper rougher tailing,
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
In Balance 1 (the lead second and third cleaners), by using the
third cleaner concentrate estimated tonnage generated in the
‘outer’ mass balance in Step 2 it is possible to estimate the
tonnage for the lead first cleaner concentrate and the lead second
cleaner tailing. Using these two values, it is then possible to mass
balance the lead first cleaner. This will give tonnage estimates for
the lead rougher concentrate and the lead first cleaner tailing.
Balance 3 uses the tonnage estimate for the lead scavenger
tailing determined in the ‘outer’ balance in Step 2. The mass
balance of the lead scavengers yields an estimate of the tonnage
for the lead scavenger concentrate.
Once these three ‘inner’ mass balances are complete it is
possible to fix certain tonnages (ie copper rougher tailing, lead
scavenger tailing, lead first, second and third cleaner concentrates),
and complete a mass balance of the lead circuit. The process
streams used for this balance are:
1.
copper rougher tailing,
Cu rougher concentrate = Cu cleaner concentrate + Cu
cleaner tailing (or, 2 = 4 + 5).
2.
lead rougher concentrate,
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:
3.
lead rougher tailing,
4.
lead scavenger concentrate,
2.
5.
lead scavenger tailing,
1.
the lead second and third cleaners,
6.
lead first cleaner concentrate,
2.
the lead first cleaner,
7.
lead first cleaner tailing,
3.
the lead scavenger, and
8.
lead second cleaner concentrate,
4.
the lead rougher/cleaner circuit.
9.
lead second cleaner tailing,
While this arrangement may appear to be counter intuitive, the
reasons for moving backwards through the lead circuit are driven
by the need to estimate tonnage figures for the recycle streams.
Flotation Plant Optimisation
10. lead third cleaner concentrate, and
11. lead third cleaner tailing.
Spectrum Series 16
9
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE 6
Adjusted assays for the copper circuit ‘inner’ mass balance.
No
Stream
Adjusted assays (%)
t/h
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
3
Cu rougher tailing
118.79
0.10
95
0.21
3.99
15.12
13.14
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
Balance 3
Pb rougher feed
Pb rougher
(1)
1. Cu rougher tailing
Pb scavenger
(2)
3. Pb rougher tailing
5. Pb scavenger tailing
2. Pb rougher concentrate
4. Pb scavenger concentrate
Balance 2
Pb 1st cleaner feed
Pb 1st cleaner
(3)
7. Pb 1st cleaner tailing
6. Pb 1st cleaner concentrate
Pb 2nd cleaner
(4)
Pb 2nd cleaner feed
9. Pb 2nd cleaner tailing
8. Pb 2nd cleaner concentrate
Balance 1
Pb 3rd cleaner
(5)
Pb 3rd cleaner feed
11. Pb 3rd cleaner tailing
10. Pb 3rd cleaner concentrate
(Pb final con)
FIG 6 - The ‘inner’ mass balances for the lead circuit.
And, the nodes are:
1.
Cu rougher tailing + Pb scavenger concentrate + Pb first
cleaner tailing = Pb rougher concentrate + Pb rougher
tailing (or, 1 + 4 + 7 = 3 + 3);
2.
Pb rougher tailing = Pb scavenger concentrate + Pb
scavenger tailing (or, 3 = 4 + 5);
3.
4.
Pb rougher concentrate + Pb second cleaner tailing = Pb
first cleaner concentrate + Pb first cleaner tailing (or, 2 + 9
= 6 + 7);
Pb first cleaner concentrate + Pb third cleaner tailing = Pb
second cleaner concentrate + Pb second cleaner tailing (or,
6 + 11 = 8 + 9); and
balancing (Balance 2) step involves the zinc scavenger circuit.
When these two internal mass balances are complete, it is
possible to mass balance the zinc circuit while fixing certain
flows. The process streams used for this balance are:
1.
lead scavenger tailing,
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,
Pb second cleaner concentrate = Pb third cleaner
concentrate + Pb third cleaner tailing (or, 8 = 10 + 11).
8.
zinc second cleaner concentrate, and
The mass balanced assays for the lead circuit are given in
Table 7.
The same approach is employed when mass balancing the inner
circuits for zinc flotation (Figure 7). The first balance (Balance 1)
examines the zinc first and second cleaner, while the second mass
9.
zinc second cleaner tailing.
1.
5.
10
And, the nodes are:
Spectrum Series 16
Pb scavenger tailing + Zn scavenger concentrate + Zn first
cleaner tailing = Zn rougher concentrate + Zn rougher
tailing (or, 1 + 4 + 7 = 3 + 3);
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE 7
Adjusted assays for the ‘inner’ lead circuit mass balance.
No
Stream
t/h
Adjusted assays (%)
Pb
Zn
Fe
1
Cu rougher tailing
118.79
0.05
87
0.20
4.00
14.86
13.17
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
5
Pb scavenger tailing
112.02
0.09
27
0.15
0.59
15.20
13.86
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
2.45
2nd
8
Pb
9
Pb 2nd cleaner tailing
10
11
cleaner con
Ag
Cu
31.98
0.09
940
0.51
50.72
10.54
104.08
0.11
602
0.25
43.65
13.91
4.06
Pb 3rd cleaner con
6.75
0.09
1077
1.08
60.39
9.35
1.71
Pb 3rd cleaner tailing
25.23
0.11
903
0.36
48.13
10.86
2.65
Balance 2
Zn rougher feed
1. Pb rougher tailing
Zn rougher
(1)
3. Zn rougher tailing
Zn scavenger
(2)
5. Zn scavenger tailing
2. Zn rougher concentrate
4. Zn scavenger concentrate
Zn 1st cleaner feed
Zn 1st cleaner
(3)
7. Zn 1st cleaner tailing
6. Zn 1st cleaner concentrate
Zn 2nd cleaner
(4)
Zn 2nd cleaner feed
9. Zn 2nd cleaner tailing
Balance 1
8. Zn 2nd cleaner concentrate
(Zn final con)
FIG 7 - The ‘inner’ mass balances for the zinc circuit.
2.
Zn rougher tailing = Zn scavenger concentrate + Zn
scavenger tailing (or, 3 = 4 + 5);
3.
Zn rougher concentrate + Zn second cleaner tailing = Zn
first cleaner concentrate + Zn first cleaner tailing (or, 2 + 9
= 6 + 7); and
4.
copper rougher is actually the fresh flotation feed plus the copper
cleaner tailing. The sum of these recovery values should be the
same 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
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 mass
balance the whole circuit. The resultant mass balance is given in
Table 9. When reviewing your mass balance it is always a good
idea to check that the internal workings of the circuit are
balanced. For example, in the copper circuit, the feed to the
Flotation Plant Optimisation
112.54 = 112.54
The balance holds, so we can have confidence that the mass
balancing calculations have been completed correctly.
What does it mean? (Chapter 2)
With the mass balance completed it is time to analyse the data and
determine what it means. From this analysis it is possible to
establish what and where the weaknesses are in the circuit, and
Spectrum Series 16
11
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE 8
Adjusted assays for the ‘inner’ zinc circuit mass balance.
No
Stream
t/h
Adjusted assays (%)
Pb
Zn
Fe
112.02
0.07
28
0.15
0.57
15.15
13.20
Zn rougher con
43.74
0.07
53
0.33
1.52
44.32
7.98
Zn rougher tailing
89.83
0.18
16
0.13
0.47
1.74
15.69
Zn scavenger con
5.51
2.56
72
0.47
2.26
15.17
14.29
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
1
Pb scavenger tailing
2
3
4
5
Ag
Cu
TABLE 9
Mass balance of copper, lead and zinc circuits of the Eureka Concentrator.
No
Stream
t/h
Cu (%)
Pb (%)
Zn (%)
Grade
Recovery
Grade
Recovery
Grade
Recovery
Grade
Recovery
120.00
0.05
0.43
100.00
4.00
100.00
14.92
100
13.06
100.00
2.39
0.41
14.29
66.15
15.10
7.50
12.76
1.70
13.10
1.99
118.79
0.05
0.20
46.39
4.00
98.97
14.99
99.48
13.02
98.69
1.21
0.11
22.74
53.61
4.07
1.03
7.71
0.52
16.84
1.31
1.18
0.84
5.52
12.54
26.54
6.47
17.99
1.18
9.22
0.69
134.72
3.12
0.32
85.07
21.44
611.75
29.86
228.56
8.84
77.31
0.23
0.17
39.52
1.24
31.39
17.40
119.05
13.52
105.65
3.00
0.36
7.25
8.17
17.72
39.71
23.11
11.58
7.70
0.14
32.27
0.58
13.67
15.33
95.94
13.70
97.95
64.56
44.68
1030.87
13.63
84.37
3.91
27.69
70.95
19.41
526.44
30.92
225.02
9.21
76.58
31.72
50.75
337.91
10.54
18.84
2.45
5.00
0.25
50.44
43.64
945.57
13.91
80.84
4.06
26.95
1.08
14.12
60.64
85.30
9.36
3.53
1.71
0.74
0.36
17.60
48.10
252.61
10.86
15.30
2.65
4.26
0.33
27.91
1.48
13.51
44.77
109.42
8.00
22.33
8.75
16.30
93.53
4.71
14.26
5.07
4.04
16.44
88.46
55.33
115.74
6.17
14.74
6.64
19.52
17.52
12.51
12.83
6.88
59.41
91.91
5.38
9.50
3.28
43.76
23.83
8.41
5.23
1
Flotation feed
2
Cu rougher con
3
Cu rougher tailing
4
Cu cleaner con
5
Cu cleaner tailing
6
Pb rougher con
7
Pb rougher tailing
122.16
8
Pb scav con
10.14
9
Pb scav tailing
112.02
0.10
10
Pb 1st cleaner con
110.85
0.10
0.30
11
Pb 1st cleaner tailing
127.95
2.27
0.28
12
Pb 2nd cleaner con
31.98
0.10
0.51
13
Pb 2nd cleaner tailing
104.08
0.11
14
Pb 3rd cleaner con
6.75
0.09
15
Pb 3rd cleaner tailing
25.23
0.13
16
Zn rougher con
43.74
0.39
17
Zn rougher tailing
89.93
0.24
0.13
21.96
0.50
9.38
1.74
18
Zn scav con
5.51
3.59
0.47
5.12
2.23
2.58
15.14
19
Zn scav tailing
84.32
0.07
0.10
16.85
0.39
6.79
0.86
20
Zn 1st cleaner con
37.44
0.09
0.33
24.19
1.30
10.16
21
Zn 1st cleaner tailing
16.04
1.07
0.40
12.49
1.99
22
Zn 2nd cleaner con
27.70
0.09
0.29
15.42
1.19
23
Zn 2nd cleaner tailing
9.74
0.38
0.46
8.76
1.62
determine what additional tests are required to give more definition
to the data. As the mass balance was completed using elemental
assays it is now possible to use this data to calculate what is
happening on a mineral basis. That is, the mass balanced
elemental assays can be converted to minerals by making certain
assumptions about the elemental composition of the various
minerals of interest within your system. Appendix 2 provides an
example of these element to mineral calculations. The conversion
to minerals allows you to examine the flotation behaviour of the
iron sulfide and non-sulfide gangue species. Combining the
calculated non-sulfide gangue mass balanced data with the water
recovery data it is possible to gain an appreciation of how these
minerals are being recovered (ie entrainment). Chapter 2: Existing
12
Fe (%)
Methods for Process Analysis by Bill Johnson provides a detailed
description of how to analyse and interpret your plant survey data.
The copper grade/recovery curve for the copper circuit appears
in Figure 8. This data, in conjunction with Table 9, indicates that
there is a small circulating load (about 12 per cent) of copper
returning to the rougher feed via the copper cleaner tailing. This
is not a major concern. However, the poor selectivity for
chalcopyrite against galena and sphalerite during copper roughing
(ie the high lead and zinc grades) is an issue, and requires further
investigation.
Figure 9 contains the lead grade/recovery curve for the Eureka
circuit. It is immediately obvious that there is a large circulating
load of galena within the lead circuit centred around the lead first
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
25.0
Cu cleaner concentrate
Cu grade, %
20.0
15.0
Cu rougher concentrate
10.0
Cu rougher feed
5.0
Flotation feed
0.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
Cu recovery, %
FIG 8 - The copper grade/recovery curve for the copper circuit within the Eureka Concentrator.
70.0
Pb 3rd cleaner concentrate
60.0
Pb 2nd cleaner concentrate
Pb grade, %
50.0
Pb 2nd cleaner feed
st
Pb 1 cleaner concentrate
40.0
Pb 1st cleaner feed
30.0
Pb rougher concentrate
20.0
Cu rougher tailing
10.0
Pb rougher feed
Flotation feed
0.0
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
1600.0
1800.0
Pb recovery, %
FIG 9 - The lead grade/recovery curve for the lead circuit within the Eureka Concentrator.
70.0
nd
60.0
st
Zn grade, %
50.0
Zn rougher concentrate
st
40.0
30.0
20.0
10.0
Zn rougher feed
Pb rougher tailing
Cu rougher tailing
Flotation feed
0.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
Zn recovery, %
FIG 10 - The zinc grade/recovery curve for the zinc circuit within the Eureka Concentrator.
cleaner bank. A closer examination of the lead circuit mass
balanced data in Table 9 reveals that accompanying the very large
circulating loads of galena is a large circulating load of sphalerite.
This would suggest that there may be liberation issues within this
part of the circuit. Again, this warrants further investigation.
The zinc grade/recovery curve (Figure 10) also reveals that there
are circulating loads of zinc around the zinc first cleaner. While
Flotation Plant Optimisation
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 with
iron, 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 all
three circuits. Equally, it is obvious that making improvements in
Spectrum Series 16
13
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
the copper circuit will have ramifications on both the lead and
zinc circuits. And this makes for a good rule of thumb: start at the
front of the circuit and work downstream. The test programs need
to be prioritised so that resources can be focused on the project
that has the greatest potential metallurgical (and financial) benefit
to the plant. The survey data indicates that the lead circuit is very
unstable with high circulating loads, and could be impeding both
the lead and zinc circuit performance. It is recommended that
further work be completed to determine the recovery-by-size and
liberation characteristics through the lead circuit to see where
improvements may be made.
Sample identification and storage
At the risk of stating the obvious, having a workable sample
identification and storage system will make life easier. A lack of
attention to detail in this area will lead to mistakes in the assay
laboratory with poor labelling, and can jeopardise the ability to
locate samples for check assaying or for further testing (for
example, sizing or mineralogical examination). Therefore, it is
necessary to set up a system of identification that is both simple
and effective. For example, all surveys are labelled with an ‘S’
and numbered consecutively, with each sample in that survey
given a number. So, if the survey described in this chapter is the
tenth survey completed in the Eureka Concentrator its
identification code would be S10/1 to S10/34, where the sample
numbers 1 to 34 correspond to the sample numbers listed in
Table 2. The next survey would be S11, and so on. Flotation tests
could be identified according to the operator’s initials, for
example, CG351/1 to CG351/5, which would be read as flotation
test 351 completed by CG with five test products.
Once a numbering system has been put in place it makes it
easy to develop a storage system. In developing your storage
system you must set some rules regarding the length of time you
are going to keep a sample, and then allow the laboratory
technicians time, periodically, to maintain the storage facility. For
example, it is wise to keep flotation test produces for nominally
three months and plant surveys for up to 12 months. It is also
wise have a clean up every three months to dispose of samples
that have passed their storage date.
The pulp chemistry survey (Chapter 6)
Concurrent to the metallurgical survey, pulp chemical data was
collected from the plant to determine the pulp chemical conditions.
What do I have to do?
The pulp chemical survey involves collecting Eh, pH, dissolved
oxygen, and temperature data from the following process streams:
•
•
•
•
•
•
•
•
SAG mill discharge,
• stir the pulp sample with a 25 ml syringe to produce
homogeneous slurry;
• syringe a 25 ml aliquot of slurry from the sample bottle and
weigh;
• inject the contents of the syringe into a 400 ml beaker
containing 250 ml of three per cent (by weight) EDTA
solution, pH modified to 7.5 with sodium hydroxide;
• thoroughly mix the slurry and EDTA solution using a
magnetic 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 the
solids dried. The dry solids were weighed and submitted for
assay.
What data do I need?
The pulp chemical data (pH, Eh, dissolved oxygen and
temperature) are stored on a data logger. These data now need to
be downloaded into Excel, massaged and put into a logical format
for analysis and interpretation.
The samples for EDTA extraction (solids and liquids) are
prepared and submitted for assay. Usually these samples are
assayed for the base metals of interest; in the case of the Eureka
Concentrator, the solids and liquids were assayed for copper,
lead, zinc and iron.
As with the metallurgical surveys, other data that can be useful
when analysing the EDTA survey are:
• the throughput at the time of the survey,
• reagent additions and other plant operating parameters (ie air
cyclone underflow,
ball mill discharge,
flow rates and pulp levels),
cyclone overflow,
• OSA readings, and
• information about the ore being treated.
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 small
beaker. The sample is then stirred gently with the probes for
nominally two minutes until equilibrium readings are obtained.
The Eh, pH, dissolved oxygen, and temperature data are logged
14
on a data logger. The logged data can then be downloaded from
the data logger to a laptop computer where it is able to be
manipulated.
All of the probes should be checked to ensure they are clean
and in good working order, and it is imperative that they are
calibrated before use. The pH probe should be calibrated using
pH buffer solutions seven and ten, if this is the range expected in
the plant. Alternative buffer solutions should be used if the pulp
pH is outside this range. The Eh probe should be calibrated, for
example, using Zobell solution (1:1 solution of Part A and B =
231 mV at 24°C), the dissolved oxygen probe calibrated in a
0.2 g/L solution of sodium sulfite for the zero calibration, and air.
The temperature probe can be calibrated with the aid of a
thermometer, using iced and heated water to give a two point
calibration.
To complete the data set, EDTA extractions should be
completed on the same process streams as the pulp chemistry. For
the Eureka survey, each stream was ‘cut’ and the sample poured
into a small wide mouth, screw top sample bottle. The samples
were taken back to the laboratory, and the wet weight recorded.
The EDTA extraction procedure used was:
In the context of a one off survey some of these pieces of
information may not be of great value. However, when the analysis
is extended to include other surveys on other ore blends, circuit
configurations and reagent suites, these data provide a vital link in
the comparison.
It can be beneficial to collect process water samples routinely
to determine the free ions in solution (ie base metal ions (copper,
lead, zinc, iron), calcium, magnesium, sulfate and chloride).
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
How do I analyse the data?
examining the pulp chemistry of a regrind/cleaner circuit, it is
normal to collect a sample of the fresh concentrate that feeds the
regrinding section. It is also good policy when completing surface
analysis comparing pairs of streams (ie rougher concentrate and
rougher tailing), to collect the pulp chemistry of the two process
streams of interest.
With the exception of the Eh data the other pulp chemical
measurements can be read directly from the measured data. The
Eh should be adjusted so that it is referenced against the standard
hydrogen electrode (SHE). Chapter 6: Chemical Measurements
During Plant Surveys and Their Interpretation by Stephen Grano
provides an explanation of how to manipulate and analyse the
data you have collected.
One method of analysing the EDTA data is given by Rumball
and Richmond (1996), who developed a simple relationship for
calculating the percentage of oxidised mineral present in a pulp
from EDTA extraction data (Equation 1):
% EDTA extractable M =
What does it mean?
The pH, Eh, dissolved oxygen, and temperature data should be
plotted in such a way to represent the pulp flow profile through
the circuit to assist with the analysis and interpretation of the
data. The profiles through the Eureka circuit are displayed in
Figures 11 to 14, respectively.
The pH profile (Figure 11) indicated that grinding occurs at
natural pH (in the range 8.0 to 8.5). The slight reduction in pH in
the ball mill discharge maybe attributed to pyrite oxidation.
Sodium metabisulfite (SMBS) was added to the copper rougher
feed, to depress galena during copper flotation. The addition of
SMBS resulted in the pH being reduced to approximately 6.0.
The pH was then increased to 8.0 for galena flotation and 11.5
sphalerite flotation thought the addition of lime.
Figure 12 contains the Eh profile through the circuit. The Eh
shifted to slightly less oxidising pulp potentials as the pulp
flowed from the SAG mill through the ball mill, and became
more oxidising during flotation where air is used as the flotation
gas.
The dissolved oxygen profile (Figure 6) through the circuit
indicated that the oxygen content of the pulp was negligible
during both grinding and flotation, registering zero on the meter.
It was not until the zinc flotation stage that the dissolved oxygen
content of the pulp was increased to 4.5 ppm in the final tailing.
These data suggest that the ore is very reactive and continues to
consume oxygen throughout the process.
The pulp temperature ranged 33 to 35°C across most of the
circuit (Figure 14). The spike in the pulp temperature in the ball
mill discharge can be attributed to part of the addition grinding
energy being dissipated as heat.
The Eh-pH data for the grinding and flotation circuits of the
Eureka Concentrator have been plotted in Figure 15 to determine
where the reactions are occurring. From the Nernst Equation 2
there is a dependence of redox potential on pH:
Mass of M in EDTA solution
× 100 (1)
Mass of M in solids
where:
M
is the metal ion under investigation
The data is generally presented graphically with the pulp
chemical parameter on the y-axis and the circuit position on the
x-axis. The process streams are set to mimic the normal flow of
the 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 often
the urge is to sample every stream in the concentrator. While this
may be a good idea initially to get an idea of the variations
between process streams, it soon becomes apparent that some of
this data is redundant. For example, concentrate streams usually
have high dissolved oxygen contents and very oxidising pulp
potentials because the froth in the concentrate contains significant
amounts of air. Therefore, when conducting a pulp chemistry
survey of the primary grinding/rougher flotation circuit
measuring the pulp chemistry of the rougher concentrate can
produce numbers that skew the analysis. However, if you are
E = E °+
⎛ α Reactants ⎞
0.059
log 10 ⎜
⎟
n
⎝ α Products ⎠
(2)
12.0
10.0
pH
8.0
6.0
4.0
2.0
ee
d
ro
ug
he
rf
ee
d
Zn
ro
ug
he
rf
ee
Zn
d
ro
ug
he
rt
ai
lin
g
Pb
ro
ug
he
rf
ov
er
f lo
w
Cu
Cy
clo
ne
di
sc
ha
r
ge
flo
w
m
ill
un
de
r
Ba
ll
di
s
m
ill
SA
G
Cy
clo
ne
ch
ar
ge
0.0
Circuit position
FIG 11 - The pH profile through the grinding and flotation circuits of the Eureka Concentrator.
Flotation Plant Optimisation
Spectrum Series 16
15
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
200.0
Eh, mV (SHE)
160.0
120.0
80.0
40.0
ee
d
ro
ug
he
rf
ee
d
Zn
ro
ug
he
rf
ee
Zn
d
ro
ug
he
rt
ai
lin
g
Pb
Cu
ro
ug
he
rf
ov
er
f lo
w
Cy
clo
ne
di
sc
ha
r
ge
flo
w
m
ill
un
de
r
Ba
ll
SA
G
m
ill
Cy
clo
ne
di
s
ch
ar
ge
0.0
Circuit position
FIG 12 - The Eh profile through the grinding and flotation circuits of the Eureka Concentrator.
6.0
5.0
DO, ppm
4.0
3.0
2.0
1.0
ee
d
ro
ug
he
rf
ee
d
Zn
ro
ug
he
rf
ee
Zn
d
ro
ug
he
rt
ai
lin
g
Pb
ro
ug
he
rf
ov
er
f lo
w
Cu
m
ill
Cy
clo
ne
di
sc
ha
r
ge
flo
w
un
de
r
Ba
ll
SA
G
m
ill
Cy
clo
ne
di
s
ch
ar
ge
0.0
Circuit position
FIG 13 - Dissolved oxygen profile through the grinding and flotation circuits of the Eureka Concentrator.
45.0
40.0
Temperature, o C
35.0
30.0
25.0
20.0
15.0
10.0
5.0
ee
d
ro
ug
he
rf
ee
d
Zn
ro
ug
he
rf
ee
Zn
d
ro
ug
he
rt
ai
lin
g
Pb
ro
ug
he
rf
ov
er
f lo
w
Cu
m
ill
Cy
clo
ne
di
sc
ha
r
ge
flo
w
un
de
r
Ba
ll
Cy
clo
ne
SA
G
m
ill
di
s
ch
ar
ge
0.0
Circuit position
FIG 14 - Temperature profile through the grinding and flotation circuits of the Eureka Concentrator.
Applying the Nernst equation to water results in a Pourbaix
diagram that describes three domains, separated by lines of
equilibria. The upper most of these is the water-oxygen line
(Equation 3), above which water decomposes and oxygen is
evolved, and below which water is stable:
16
E 0 2 = +1.23 + 0.015 log 10 po2 − 0.059 pH
(3)
This can be simplified further (Johnson, 1988; Natarajan and
Iwasaki, 1973) for an oxygenated aqueous solution with no well
defined redox couples to (Equation 4):
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
E 0 2 = +0.9 − 0.059 pH
remained negligible throughout the grinding circuit. The per cent
EDTA extractable copper increased during the copper flotation
step to approximately 2.5 per cent and this increases to 17 per
cent in the zinc rougher feed, which can be attributed to the
addition of copper sulfate for sphalerite activation.
The first observation to be made is that the percentage of EDTA
extractable lead is at least an order of magnitude greater than the
values reported for zinc and iron. The percentage of EDTA
extractable lead is higher as galena is a reactive mineral when in
contact with other sulfide minerals, particularly pyrite. The EDTA
extractable lead profile shows that the percentage of oxidised
galena remained approximately constant through the grinding
circuit, with values of around 1.0 per cent. The per cent EDTA
extractable lead increased in the cyclone overflow, and remains
reasonably constant through the copper circuit. After the lead
flotation stage the lead scavenger tailing per cent EDTA extractable
lead was 17 per cent, and then increased to almost 40 per cent after
zinc flotation. These data suggest that the lead species that remain
in the lead scavenger tailing are more heavily oxidised.
The EDTA extractable zinc profile exhibits a very similar trend
to that observed for lead. That is, through the grinding circuit the
percentage EDTA extractable zinc remained approximately
constant, with values of around 0.1 per cent. This indicated that
sphalerite oxidation was largely unchanged through this circuit.
(4)
In broad terms, if the changes in Eh and pH result in a line
parallel to the water-oxygen line this means that water equilibria
is being maintained. That is, any change in Eh is directly
proportional to a change in pH with a similar relationship to that
expressed in Equation 4. If the changes in Eh and pH result in a
line that is perpendicular to the water-oxygen line then the
evidence suggests that oxidative reactions are occurring.
Figure 15 shows that the Eh-pH lines between points 1 to 4 are
perpendicular to the water-oxygen line. This indicates that
oxidative reactions are occurring in the grinding circuit. It is
likely that these oxidative reactions are corrosion of the grinding
media and oxidation of the sulfide minerals. As the pH is
decreased across the ball mill it is suggested that pyrite oxidation
(an acidic reaction) may be one of the dominant reactions. The
changes in the Eh-pH curve between Points 4 and 5, 5 and 6, and
6 and 7 can be attributed to the addition of reagents that alter the
pH of the system. During copper flotation it is the addition of
SMBS, while in the lead and zinc circuits it is the addition of
lime.
The EDTA extractable copper, lead, zinc and iron data are
presented in Figure 16. The EDTA extractable copper profile
(Figure 9) suggested that the percentage of oxidised chalcopyrite
250
Eh, mV (SHE)
200
6
150
8
4
5
1
100
1.
2.
3.
4.
5.
6.
7.
8.
50
0
SAG mill discharge;
Cyclone underflow;
Ball mill discharge;
Cyclone overflow;
Cu rougher feed;
3
Pb rougher feed;
Zn rougher feed; and
Final tailing
2
7
-50
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
pH
21.0
0.6
14.0
0.4
7.0
0.2
0.0
0.0
Pb
Zn
Fi
he
r
ro
ug
d
ee
ro
ug
er
f
flo
C
u
ro
ug
h
ov
er
ar
ge
ne
C
yc
lo
m
il l
di
sc
h
un
de
r
Ba
ll
ne
C
yc
lo
ill
m
G
SA
EDTA extractable Zn and Fe, %
0.8
na
lt
ai
lin
g
28.0
fe
ed
1.0
he
rf
ee
d
35.0
w
1.2
flo
w
42.0
di
sc
ha
rg
e
EDTA extractable Cu and Pb, %
FIG 15 - The Eh-pH curve for the grinding and flotation circuits of the Eureka Concentrator.
Circuit position
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.
Flotation Plant Optimisation
Spectrum Series 16
17
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
There was an increase in the per cent EDTA extractable zinc
during the copper and lead flotation circuits (ie from 0.13 to
0.37 per cent) and then a significant increase after the zinc
flotation circuit (to 1.0 per cent). This increase indicated that the
zinc species that remained in the zinc scavenger tailing were
more heavily oxidised.
The EDTA extractable iron profile gives the best indication of
the impact of grinding media corrosion on the system. The EDTA
extractable iron through primary grinding ranged from 0.05 to
0.16 per cent, and then increased to 0.30 per cent in the cyclone
overflow. That is, the pulp entering the flotation circuit contains
elevated levels of EDTA extractable iron, presumably after
contact with the forged grinding media. Interestingly, the levels of
EDTA extractable iron decrease through the flotation circuit. This
is probably due to the continuing oxidation of the iron species to
higher level iron oxides, which are not soluble in EDTA.
You need to gather solid data that tells you which size fractions
are being rejected from the circuit, and what their mineralogical
characteristic is. Therefore, the first step in this analysis is to
examine the recovery-by-size around the lead rougher feed.
What do I have to do?
When you conducted the plant survey you may have had every
intention to have the samples sized so that you can complete a
recovery-by-size analysis. However, before having your
metallurgical technicians launch into a large body of work sizing
every sample collected during the survey it is best to mass
balance the plant survey. Sizing samples prior to completing the
mass balance may waste time, resources and money if the survey
does not mass balance easily (ie poor sampling). Once you have
established that the mass balance is good, you need to decide if
completing a recovery-by-size analysis is warranted, and if it is,
what samples you would like to have sized. It is not always
necessary to size every sample in the survey. The sample
selection will depend on what the survey reveals about plant
performance. For example, sizing the concentrate and tailing
from a section will show which size fractions of the valuable
species are misbehaving (ie not being recovered efficiently), and
which size fractions of the gangue species are being recovered.
Sometimes completing recovery-by-size analysis on a downthe-bank survey can add value because it allows you to determine
where size fractions are recovered in the circuit, and can be used
to determine the kinetics of various species on a size basis.
With these decisions made you need to retrieve the samples
from storage, ensure that you have sufficient sample to complete
a sizing (generally 200 grams is sufficient), and determine what
sizes you require. If you need to obtain size fractions below 38
microns you will need to use a cyclosizer, which will give you six
more size fractions. However, if cyclosizer is used in concert with
a precyclone and a centrifuge the number of size fractions
obtained can be extended to seven.
Summary of data acquisition
The metallurgical survey suggests that there are a number of
opportunities for improving the metallurgical performance of the
Eureka Concentrator. It is suspected that these may be liberation
related, and further data is required to identify exactly what the
liberation characteristics are. The data strongly suggests that the
first 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 to
improve this aspect of plant performance. However, this should
not be rectified until the liberation issues have been resolved.
PROBLEM DEFINITION
From the metallurgical survey the circulating load observed in the
lead rougher/lead first cleaner circuit were observed to be
extremely high (Table 10), for both lead and zinc. In fact, the
copper scavenger tailing contributed only 15.4 and 28.6 per cent
of the lead and zinc units, respectively, to the lead rougher feed.
The recycle from the lead first cleaner tailing contributed by far
the most lead and zinc to the lead rougher feed (81.9 and 64.7 per
cent, respectively).
What data do I need?
With the sizing completed you will require the mass collected in
each size fraction. If the sizing has been restricted to a simple
sieve analysis, then you will need to determine how much mass
you have in each size fraction to ensure that you have sufficient
for assay (usually 5 grams is more than enough). If you do not
have enough sample in each size fraction for an assay you need to
either complete another sizing on this sample, or you combine
size fractions to yield enough mass. However, if you do combine
size fractions you must apply this across all of the samples within
this data set. Not to do so will make it impossible to complete the
analysis. That is, if you were required to combine the +300, +212
and +150 micron fractions in the feed sample, then you will need
to do the same for all other samples in the suite of samples for
this survey.
When it comes to sizing the subsieve fractions using a
precyclone/cyclosizer/centrifuge combination, you will need to
know the water temperature, the elutriation time, the water flow
Recovery by size (Chapter 2)
The mass balanced data gives you information about the grades
and recoveries in the circuit. The data also provides information
about the circulating loads. However, to gain a greater
appreciation for where problems occur and their magnitude, it is
advisable that samples from selected process streams be sized,
and each size fraction assayed. By mass balancing the size
fractions, the performance of each size fraction can be assessed.
An examination of Table 9 and Figure 9 indicates that the lead
rougher/first cleaner section of the plant has a large circulating
load of galena and sphalerite. It is likely that this large circulating
load produces instability within the circuit, and is probably due to
composite particles. However, at this stage you are only guessing.
TABLE 10
Mass 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.
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
18
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
rate and the specific gravity of the mineral being separated. With
these variables known using the cyclosizer manual (Warman
International Limited, 1991) the ‘cut’ size for each cyclone can be
calculated. That is, for quartz the cyclone ‘cut’ sizes for the
cyclosizer are:
•
•
•
•
•
C1 = 44 microns,
C2 = 33 microns,
If you are considering completing a mineralogical analysis it is
at this point that you would ‘split’ out a small representative
portion from the samples of interest prior to pulverising the
samples for assay. Once you have reviewed the weights in each
size fraction for each of the samples you have had sized, and
decided which size fractions to combine, and ensured that there is
enough sample for assay in each size fraction, the samples can be
prepared and submitted of assay.
C3 = 23 microns,
C4 = 15 microns, and
How do I analyse the data collected?
C5 = 11 microns.
When the assays return it is important to check that the assays are
in good order. This can be done by calculating the head assay
from the size fraction assays and comparing it with the actual
head assay. If they are in good agreement, then you can assume
that both the sizing and the assays were completed properly.
To construct a graph similar to that presented in Figure 17 the
size and assay data for the copper rougher tailing, the lead first
cleaner tailing, the lead scavenger concentrate and the lead
rougher feed are required. To simplify the analysis only the lead
assay data will be considered in this example. The starting point
for determining the recovery-by-size behaviour in this part of the
circuit is the mass balanced data from the plant survey given in
Table 9, and for this section of the plant Table 10. Using the mass
balanced tonnages for each of the process streams given in
Table 10 the size distribution data determined from the sizing
process (ie the weight per cent data) for each of these sample
points were applied to yield the tonnes of ore in each size fraction
(Table 12). The weight per cent and the assay for each size
fraction are used to calculate the head grade:
The precyclone generally ‘cuts’ at about 7 microns. To convert
these ‘cut’ sizes to values that are more representative of minerals
such 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 mineral
in the system as each mineral has a different specific gravity. The
cyclone ‘cut’ sizes for each of the cyclosizer cones are given in
Table 11.
Further, it must be realised that sizing the -38 micron material
using the precyclone, cyclosizer and/or the centrifuge represents a
change in the way the particles are sized. When using sieves the
sizing is achieved by passing the particles over a nest of sieves.
Particles that are larger than the screen aperture are retained on
the screen and those that are smaller fall through. However, when
sizing using the precyclone, cyclosizer and/or centrifuge sizing is
completed hydraulically based on size and mineral specific
gravity. When the two sizing methods are used together, the
transition point from one technique to the other can lead to some
unusual shaped curves which are purely due to the change in the
sizing method. To overcome this it is usual to combine the -53
micron to +C2 size fractions. This smooths the curves and
effectively removed the transition between sizing methods.
( mi × ai )
i=1
∑ mi
n
Head grade = ∑
(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 of
the head sample to determine how well the sizing and assay
procedures have been completed. Unfortunately, in this instance
the mass and assay of the -5.6 micron fraction was calculated by
difference. That is:
TABLE 11
Cyclosizer cyclone ‘cut’ sizes for quartz, chalcopyrite, galena, sphalerite and pyrite.
Correction factor
Mineral
Chalcopyrite
Galena
Sphalerite
Pyrite
fT
Quartz
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
Flotation Plant Optimisation
Spectrum Series 16
19
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
100.0
90.0
Pb distribution, %
80.0
70.0
60.0
50.0
40.0
30.0
20.0
Pb rougher feed
Pb 1st cleaner tail
10.0
0.0
Pb scav con
Total
-5.6
+5.6
+7.6
Particle size, microns
Cu scav tail
+11.6
+16.7
+22.2
FIG 17 - Lead distribution by size in the process streams contributing to the lead rougher feed.
TABLE 12
The 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.
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
+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
100.00
127.95
19.41
100.00
81.65
+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
100.00
10.14
8.17
100.00
2.72
+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
Pb First cleaner tailing
Head
Pb scavenger con
Head
Pb rougher feed
-5.6 μm
15.16
38.95
5.22
6.68
6.68
Head
100.00
256.88
11.84
100.00
100.00
20
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
n−1
m−5 .6 μm = mH − ∑ m1
(7)
i−1
What does it mean?
and
a−5 .6 μm
⎛
⎞⎞
⎛n−1
⎜ mH × aH − ⎜ ∑ m1 × ai ⎟ ⎟
⎠⎠
⎝ i−1
⎝
=
m−5 .6 μm
(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 size
fraction all the errors associated with the sizing, assaying and
sampling are accumulated to this size fraction. In an ideal world
as sample of this material would be collected.
Having worked out the tonnes of ore and the assay for each
size fraction the distribution-by-size within each process stream,
and the recovery-by-size against a particular process stream can
be calculated. The distribution within a process stream is given
by:
⎛ ( m × ai ) ⎞
Distributiona = ⎜ i
⎟ × 100
⎝ ( mH × aH )⎠
(9)
The distribution of lead within each process stream is provided
in Table 12. In this case the recovery-by-size is calculated for the
lead rougher feed because the copper scavenger tailing, the lead
first cleaner tailing and the lead scavenger concentrate all
combine to form the lead rougher feed. So, the recovery for each
size fraction, i, is given by:
Recoveryi , a
⎛ n
⎞
⎜ ∑ ( m p × a p )⎟
⎟ × 100
= ⎜ P= 1
⎜ ( mH × aH ) ⎟
⎜
⎟
⎝
⎠
(10)
Figure 17 contains the distribution by size, for galena, for the
three process streams contributing to the lead rougher feed. The
vast majority of the galena in the lead rougher feed is in the
coarse (+20 micron) lead recycled back from the lead first cleaner
tailing (80 per cent of the total lead in the lead rougher feed).
Thus, the flotation characteristics of the lead rougher/scavenger
unit are dominated by the behaviour of this material.
The recoveries versus size data for the lead final concentrate
are provided in Figure 18. These data show clearly that the lead
final concentrate consists predominantly of fine (-20 micron)
galena, with greater than 90 per cent lead recovery for the
intermediate size fractions (+5/-20 microns). Either side of this
size range the recoveries decrease. The recoveries of gangue
minerals (sphalerite, iron sulfides and non-sulfide gangue) are
low 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 the
losses of galena from the lead circuit are bimodal, with significant
losses in the fine (-5 microns) fraction (5.6 per cent) and coarse
(+20 microns) fractions (19.9 per cent). Of greatest concern
however are the losses in the coarse fractions. It is presumed that
these coarse particles occur as galena deficient/sphalerite rich
composites.
Figure 20 shows the recovery by size data for the zinc final
concentrate. These data show the peculiar behaviour of the coarse
(+20 microns) galena, which exhibits recoveries as high as 60 per
cent. It is presumed that this behaviour is related to the
composition of these galena particles (ie galena deficient/
sphalerite rich composites).
Mineralogy (Chapter 4)
Once the recovery-by-size data has been analysed and a theory
formed, select streams should be examined mineralogically. As
this is a relatively expensive process, it is wise to complete the
work already discussed, prior to obtaining mineralogical analysis.
The recovery-by-size analysis indicates that a significant amount
where:
P
A similar approach is adopted for the more traditional
recovery-by-size curves presented in Figure 18 and 20.
are the process streams of interest
90.0
80.0
Recovery, %
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1.0
10.0
100.0
1000.0
Geometric mean particle size, microns
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.
Flotation Plant Optimisation
Spectrum Series 16
21
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
5.0
4.0
3.0
2.0
1.0
178.3
Geometric mean particle size, microns
126.1
89.2
63.0
48.8
41.0
28.0
17.6
11.7
7.9
6.0
0.0
2.3
Distribution (with respect
to flotation feed), %
6.0
FIG 19 - The galena distribution by size, with respect to flotation feed, in the lead scavenger tailing for the Eureka Concentrator.
100.0
90.0
Recovery, %
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1.0
10.0
100.0
1000.0
Geometric mean particle size, microns
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.
of material is recycled back to the lead rougher feed from the lead
first cleaner tailing, and that the vast majority of this material
occurs in the +20 micron size fraction. It is highly likely that
these particles are galena/sphalerite composites. However, in
order to prove this it is necessary to complete a mineralogical
examination.
What do I have to do?
To complete a mineralogical analysis of samples from you plant
you need to provide the company completing the examination for
you with a representative sample of the process stream that has
not been pulverised. For liberation analysis, the sample provided
will be of a number of closely sized size fractions. If you have
completed a recovery-by-size analysis you should have prepared
the samples for mineralogical examination at the same time.
With the samples available for analysis it is then necessary to
choose the method by which the mineralogical data will be
collected. These methods range from optical microscopy through
to automated methods such as QEMSCAN and MLA. Chapter 4:
A Practical Guide to Some Aspects of Mineralogy that Effect
Flotation, by Alan Butcher, describes the methods available.
22
What data do I need?
When shipping samples off to a laboratory for mineralogical
analysis it is wise to provide the mineralogist with a scope of
work, and assays of the samples that will be examined. The scope
of work will inform the mineralogist of the questions you would
like answered. For example, at Eureka you are interested in the
locking and liberation characteristics of galena and sphalerite
around the lead rougher/first cleaner circuit. It is then clear to the
mineralogist that the examination should provide information
about the degree of liberation of these minerals, and how they are
locked with other minerals. Supplying the assays for the samples
you have supplied for examination allows the mineralogist to
reconcile his calculated head grade based on the minerals present
back to the assays. It is a check that the mineralogist uses to
ensure the quality of the data that is provided.
How do I analyse the data collected?
The mineralogical report provided will depend on the technique
employed by the mineralogist to examine your samples.
Essentially, the data provided should provide nominally the same
information. That is, the percentage of liberated mineral within
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
recovery-by-size and liberation analysis has clearly indicated that
the recovery of +20 micron galena within the lead circuit is
limited. The liberation analysis clearly shows that the galena in
the +20 micron fraction is locked with sphalerite.
Thus, the problem is: poor liberation of galena in the +20 micron
size fractions.
the sample, the percentage of binary particles present and what
the two minerals are, and the percentage of more complex
composite particles measured. Table 13 provides a simplified list
of the liberation data for galena in the lead rougher feed of the
Eureka Concentrator.
What this data means is that of the lead in the +28 micron size
fraction 66 per cent occurs as liberated particles of galena and
28 per cent are present as binary composites with sphalerite.
Minor amounts of galena are associated with pyrite and occur in
ternary particles.
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 in
the lead circuit is to complete a series of laboratory tests examining
the impact of regrinding the main feed source to the lead rougher,
the lead first cleaner tailing.
What does it mean?
Mineralogical examination of strategic process streams feeding
into the lead rougher feed showed that about one third of the
galena in the lead rougher feed occurs as coarse (+20 micron)
galena/sphalerite composite particles (Figure 21), and the
majority of this material emanates from the lead first cleaner
tailing. Thus, two thirds of the galena in the lead rougher feed is
liberated, which infers that the maximum lead recovery to the
final lead concentrate, at grade, would be approximately 70 per
cent. Therefore, galena liberation was considered to be the
limiting factor impinging of improved metallurgical performance.
Laboratory investigation (Chapter 9)
What do I have to do?
As the lead first cleaner tailing contributed the highest flow of
material to the lead rougher feed, and this process stream contains
a significant percentage of galena/sphalerite composite particles
greater than 20 microns, it was decided that this process stream
would be the best one to complete laboratory tests examining the
impact of regrinding on lead rougher flotation. It is your job to
design a series of laboratory flotation tests that would identify
what changes would occur as the lead first cleaner tailing was
reground to progressively finer sizes.
Problem definition summary
The metallurgical survey suggested that the lead circuit was
potentially unstable with large circulating loads of galena and
sphalerite around the lead rougher/first cleaner circuit. The use of
TABLE 13
Galena liberation by size and mineral class for the lead rougher feed of the Eureka Concentrator.
Size (μm)
Liberation class (%)
Liberated
Total (%)
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
100
90
Distribution
80
70
60
50
40
Liberated
30
Ga-Sp
20
Ga-Py
10
Ga-Gn
0
Head
2.3
Liberation class
Ternary
6.0
Size, microns
10.1
28.2
89.2
FIG 21 - Size by liberation class data for the lead rougher feed. Ga = galena; Sp = sphalerite; Py = pyrite; and Gn = gangue.
Flotation Plant Optimisation
Spectrum Series 16
23
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
With the laboratory test program designed, sufficient pulp was
collected from the lead first cleaner tailing. In addition to the lead
first cleaner tailing pulp, a bulk sample of process water was also
collected. The pulp was filtered and ‘cut’ into 1000 gram lots.
The test program called for one test to be completed without
regrinding, and then tests completed on ore ground for five, ten,
15 and 20 minutes. The tests were completed in a 2.5 litre
flotation 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 of
12 minutes. The seven concentrate and tailing samples generated
for each test were prepped and submitted for assay.
What data do I need?
The weights for each of the concentrates and the tailing samples
as well as the assays are required to complete a mass balance for
each of the tests. Additional information such as the pH, reagent
additions and any observations made during the test are also
helpful.
How do I analyse the data collected?
The weights and assays are to be put into a spreadsheet that
allows you to calculate the cumulative grades and recoveries for
each of the tests. These data can then be used to construct lead
grade/recovery curves (Figure 22). From the mass balanced data
it is also possible to determine the kinetics (ie flotation rate
constant and maximum recovery) using Equation 11, and the
selectivity for galena against the gangue minerals.
R = Rmax × (1 − e − kt )
(11)
To assist with interpretation the kinetic data and the selectivity
data can be tabulated. For example, Table 14 shows the lead
concentrate 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 strategic
process streams suggested that the introduction of regrinding
significantly improved the position of the lead grade/recovery
curve. Figure 22 shows that as the degree of regrinding increased
the position of the lead grade/recovery curve improved. For
example, at 80 per cent lead recovery the lead grade was
increased from 30.8 per cent for the test conducted without
regrinding, to 42.2 per cent after 20 minutes regrinding. The
improvement in lead grade was due to better selectivity for galena
against sphalerite and iron sulfides (Table 14).
In the laboratory, regrinding improved lead metallurgy, and
with the improved selectivity for galena against sphalerite it is
expected that zinc metallurgy would also improve.
Plant trial (Chapter 10)
As the existing lead regrind mill was inadequate for this duty, a
larger mill would be required if improvements in metallurgical
performance were to be realised. In light of the very first
comment made at the beginning of this chapter about utilising the
60.0
Pb grade, %
55.0
50.0
45.0
40.0
35.0
30.0
25.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Pb recovery, %
No regrinding
15 minutes regrinding
5 minutes regrinding
20 minutes regrinding
10 minutes regrinding
FIG 22 - Lead grade/recovery curves for lead rougher flotation tests conducted on lead first cleaner tailing, demonstrating the effect
of regrinding.
TABLE 14
Lead 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.
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
10 minutes regrinding
37.6
50.8
30.4
15 minutes regrinding
42.0
42.6
24.3
20 minutes regrinding
42.2
42.2
25.8
24
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
• the zinc grade in the lead concentrate was reduced by 1.8 ±
resources around you, and especially people, this problem was
discussed with the operations team and a solution was quickly
found. As capital was not readily available an experienced
flotation operator suggested swapping the lead and zinc
regrinding mills, as the zinc regrinding mill was larger and more
able to cope with the higher tonnages experienced in the lead
circuit.
Redirecting the lead scavenger concentrate and lead first
cleaner tailing process streams to the zinc regrind mill was
accomplished through a simple change in the pipe work. The
existing pipe work to the old lead regrind mill was left intact so
that a plant trial could be conducted. A randomised block on/off
trial was designed, following the guidelines prescribed by Tim
Napier-Munn in Chapter 10: Designing and Analysing Plant
Trials, that would use the shift mass balanced data to statistically
analyse the metallurgical performance of the Eureka Concentrator
with and without effective lead regrinding.
When the new lead regrind mill was turned on the impact on
lead and zinc metallurgy was immediate and dramatic. Because
of the magnitude of the improvements the on/off trial was
abandoned. However changes of this magnitude are not always
apparent, and strong statistical data analysis is often required as
proof that there is a statistically confident change in the plant
which was due to your implemented solution. In some cases,
trials can run for a number of months but persistence pays off
when you achieve the result you were aiming for.
The lead recovery to final concentrate from June 1998 through
to February 2000 is presented in Figure 23. It is apparent that the
lead recovery has increased on average from 65 per cent up to
81.5 per cent. The most significant improvement to lead recovery
was achieved through the application of an effective regrinding
stage.
Despite not undertaking the randomised block trial as first
thought, it was possible to complete a statistical analysis using
plant data from before and after the change. Using the Student
t-test to analyse the data showed that installing effective lead
regrinding produced the following results:
0.6 per cent, with greater than 99 per cent confidence.
These changes to the lead circuit resulted in a decrease in the
amount of sphalerite reporting to the lead concentrate, and shifted
this material into the zinc circuit. Hence, zinc metallurgy was also
improved, with:
• the zinc recovery increased by 1.5 ± 0.4 per cent, with
greater 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 of
regression 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 on
your laurels, as the Senior Project Metallurgist it is your job to
identify problems and develop cost-effective solutions to
implement in the plant. You’ve only solved part of the problem,
and opportunities for further improvement are available. So, sorry
but the Senior Project Metallurgist’s work is never finished! The
key areas where efforts can be made to improve metallurgy are:
• the optimisation of both the lead and zinc regrinding
circuits, to improve liberation and subsequently concentrate
grades and recoveries;
• the optimisation of chemistry for flotation to improve
selectivity of valuable minerals against gangue minerals, and
consequently increased concentrate grades; and
• to continue minimising variations in the process.
More surveys
To move forward it is necessary to continue monitoring the
process through:
• the lead recovery was improved by 16.5 ± 2.5 per cent, with
• analysis of shift mass balances,
• analysis of monthly composite samples (by size and
greater than 99 per cent confidence;
• the lead concentrate grade improved by 4.0 ± 0.8 per cent,
liberation class),
with greater than 99 per cent confidence; and
90.0
85.0
New Pb Feed distributor
Average % Rec.
80.0
MF4 used as
lead regrind mill
75.0
Complete Pb froth level control
+ new 6" Pb regrind cyclones
+ Dedicated float operators
70.0
65.0
Initial Pb froth level control
60.0
Level on Pb conditioning tank
dropped
55.0
2-Feb-00
12-Jan-00
1-Dec-99
22-Dec-99
20-Oct-99
10-Nov-99
8-Sep-99
29-Sep-99
18-Aug-99
7-Jul-99
28-Jul-99
16-Jun-99
5-May-99
26-May-99
14-Apr-99
3-Mar-99
24-Mar-99
20-Jan-99
10-Feb-99
9-Dec-98
30-Dec-98
28-Oct-98
18-Nov-98
7-Oct-98
16-Sep-98
5-Aug-98
26-Aug-98
15-Jul-98
3-Jun-98
24-Jun-98
13-May-98
1-Apr-98
22-Apr-98
18-Feb-98
11-Mar-98
7-Jan-98
28-Jan-98
50.0
W/E
Average
Average CL
UCL
LCL
FIG 23 - Lead recovery versus time data from June 1998 to February 2000. Some milestones are highlighted.
Flotation Plant Optimisation
Spectrum Series 16
25
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
• 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 better
regrinding perhaps it is time to study the chemistry of the
system.
Pulp chemistry
The pulp chemistry indicated that the lead rougher circuit
operated in oxidising conditions, with Eh values between 150 and
200 mV (SHE). Figure 15 shows the Eh-pH curve for the circuit.
The curve is essentially parallel to the water-oxygen line for the
lead rougher circuit, and suggested that any changes in Eh or pH
were due to the maintenance of water equilibria. These conditions
points to a system operating in oxidising conditions.
Solution chemistry
The EDTA extractable lead increased from approximately five
per cent in the copper rougher tailing up to 17 per cent in the lead
scavenger tailing. The EDTA extractable zinc and iron values
were an order of magnitude lower than those reported for lead.
These data suggest that the galena in the lead circuit feed is
moderately oxidised, while the galena remaining in the lead
scavenger tailing is heavily oxidised. The main point to come
from this analysis is that there would appear to be a significant
quantity of galena oxidation products present within the pulp,
which under the right circumstances could lead to the inadvertent
activation of sphalerite.
Surface chemistry
When collecting the samples for surface analysis it is important to
use the right sample preparation protocols. The surface scientist
will provide you with these. Generally, once the samples have
been collected they need to be purged with nitrogen gas to
remove oxygen from the pulp to prevent further oxidation. The
sample is then sealed in the sample vial, and snap frozen in liquid
nitrogen. The sample pairs are then shipped to the surface
analysis facility frozen in a cryogenic container.
Ideally, when you collect the samples for surface analysis you
should complete a block survey of that part of the circuit of
interest. At Eureka, a block survey of the lead rougher circuit
would be the order of the day. Conducting a survey gives you a
reference point in terms of metallurgical performance that can be
used for comparison purposes. It is also wise to complete a pulp
chemical and EDTA extractable metal ion analysis on the sample
process streams that the surface analysis samples were collected
from. This information should be transmitted to the surface
scientist as it gives them some idea of the chemistry of the system
and helps explain the observations they will make.
How do I analyse the data collected?
The surface scientist will provide you with an analysis of the
findings. You are not expected to know everything! However, it is
wise to read the surface analysis report and ask questions. If you
do not understand the analysis of the surface chemical data ask
the surface scientist to explain. This will allow you to question
the surface scientist to determine how they reached the
conclusions and help you to understand these techniques, and the
valuable information they can add to an investigation. It is very
important to link the surface chemical observations to the pulp
chemistry and metallurgical performance of the plant. After all
the data generated will be used to confirm your theory as to why
something is happening (at Eureka identification of the activating
species on liberated sphalerite), and from this you will propose
solutions for testing in the laboratory to solve the problem.
What does it mean?
To confirm your theory developed from the pulp chemistry
observations it is wise to complete surface analysis on selected
samples. Alan Buckely, in Chapter 8: Surface Chemical
Characterisation for Identifying and Solving Problems Within
Base Metal Sulfide Flotation Plants, provides details of the types
of surface analysis and how they may be used in your quest for
more information.
What do I have to do?
With the decision made to complete surface analysis to validate
your hypothesis you need to provide the institution conducting
the surface analysis with a scope of work. It is imperative that
you provide the surface scientist with a focused question that
defines what you hope to get out of the surface analysis. In your
case, at the Eureka Mine, you are very interested in knowing the
surface chemistry of liberated sphalerite that reports to the lead
rougher concentrate, and how this compares with free sphalerite
in the lead rougher tailing. This essentially means that the
surface scientist will examine liberated sphalerite in lead
rougher concentrate and tailing process streams to identify
differences that may explain why some of the liberated
sphalerite is being recovered into the rougher concentrate. The
surface scientist will suggest the surface analysis technique that
they feel is best for your application. ToF-SIMS is frequently
used because of its high surface sensitivity and its ability to
analyse individual particles.
26
What data do I need?
Surface analysis was completed on samples of lead rougher
concentrate and tailing samples using ToF-SIMS to determine the
dominant species on the surfaces of the liberated sphalerite
particles. The surface analysis investigation indicated that the
liberated sphalerite particles contained within the lead rougher
concentrate had statistically more collector, copper, silver and
lead 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 in
oxidising conditions.
• The level of EDTA extractable lead within the lead circuit
was high, indicating the presence of a large quantity of
galena oxidation species capable of activating sphalerite.
• The surface analysis confirmed the suspicion that the
liberated sphalerite was activated by lead ions. Copper and
silver were also observed on the surfaces of the sphalerite
particles, but in lower concentrations.
These data would suggest that some of the sphalerite is being
recovered into the lead concentrate because it is activated by lead
ions. You should propose that laboratory test work be completed
examining the effect of various reagents to depress activated
sphalerite. Should these tests prove positive, and the solution is
economically sound, then it should be tested in the plant using
statistically rigorous trial methodology.
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
0.3
0.25
0.2
0.15
0.1
0.05
0
Pb
Pb
O
H
Zn
Fe
O
H
C
u
Fe
C
a
Si
Ag
Pb cleaner con
Pb cleaner tail
M
g
Normalised Intensity
(+) SIMS sphalerite
(+) Fragment
Normalised Intensity
(-) SIMS sphalerite
0.5
0.4
0.3
0.2
0.1
0
Pb cleaner con
Pb cleaner tail
C
CH
O
OH
S
SO3
SO4
IEX
(-) Fragment
FIG 24 - Positive and negative mass spectra for sphalerite particles in the lead cleaner concentrate and tailing process streams – confidence
intervals calculated for 95 per cent.
(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.
COMMUNICATION
The technical aspects of concentrator performance are very
important in achieving optimum metallurgy. However, the best
technical expertise is of little consequence if people and
maintenance issues are not adequately addressed.
Process variations are probably the hardest to monitor and
control, and can result in operations personnel treating symptoms
rather than causes (ie ‘fire fighting’). In most instances process
variations, such as ore hardness, mineralogical changes with ore
type, and head grade variation, are very difficult to measure
online, but can be partially overcome with an effective ore
characterisation program (geometallurgy) and good communication between the mine and the mill.
Behavioural variations are often easily identified, but are
difficult to correct because of the human factor. One way of
improving the behavioural variations within a concentrator is to
document and standardise each task. That is, if each operator
Flotation Plant Optimisation
performs the same task the same way, by following as standard
work procedure, then behavioural variations can be minimised.
Unfortunately, this is easier said than done.
Another key element in altering behaviour to maximise
metallurgical performance is the development of a proactive,
interventionist metallurgical team, such that there are definite
metallurgical targets and procedures prescribed to achieve these
goals. A change in the perception of the metallurgist is required
such that he should be viewed as a member of the team with
specialist knowledge that is on-call when problems arise.
CONCLUSIONS
At the Eureka Concentrator the use of classical metallurgical
techniques (recovery- and liberation-by size analysis) has
provided a high degree of certainty in determining the source of
the metallurgical problem. Initially galena/sphalerite liberation
was identified as a significant impediment to achieving better
Spectrum Series 16
27
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
lead and zinc metallurgy. With the application of greater
regrinding capacity the focus shifted from liberation to chemistry.
The use of pulp and solution chemistry provided an indication
that inadvertent activation of sphalerite by lead, copper and silver
ions was probably the reason for the high recovery of liberated
sphalerite into the final lead concentrate. The judicious
application of appropriate surface chemical techniques provided
confirming evidence. The Eureka metallurgists have now
identified the reasons for the losses of sphalerite from the circuit,
and can now develop solutions to this problem for testing in the
laboratory, prior to trialling in the plant.
TABLE A1
The sample list for a down-the-bank survey conducted on the
bank of 12 flotation cells pictured in Figure A1.
Sample
number
Sample name
Sample type
1
T bank feed
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
5
Con 3 – T bank cells 5 to 7
Timed lip sample
REFERENCES
6
Con 4 – T bank cells 8 to 10
Timed lip sample
Johnson, N W, 1988. Application of electrochemical concepts to four
sulphide flotation separations, in Proceedings Electrochemistry in
Mineral and Metal Processing II, pp 131-149.
Natarajan, K A and Iwasaki, I, 1973. Practical implications of Eh
measurements in sulphide flotation circuits, in AIME Transactions,
256:323-328.
Rumball, J A and Richmond, G D, 1996. Measurement of oxidation in a
base 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).
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
APPENDIX 1 – THE DOWN-THE-BANK SURVEY
Introduction
Often it is of value to complete metallurgical surveys were
samples are collected down a bank of flotation cells. This type of
survey is referred to as a down-the-bank survey, and differs from
a block survey, as it provides more detailed information about the
flotation behaviour of the various species contained within that
bank of flotation cells. In conducting a block survey you collect
samples of the feed, concentrate and tailing from the section of
the circuit you are interested in. This information provides you
with data about the overall performance of that block of cells.
The down-the-bank survey provides detail of the internal
workings of that block of flotation cells.
How do I do a down-the-bank survey?
Chapter 2: Existing Methods for Process Analysis, by Bill
Johnson provides a detailed description of the two common
methods employed. An example, using one of those methods is
given below. The first step in completing a down-the-bank survey
is to identify the samples that you wish to collect. Figure A1 is a
schematic of the bank of 12 flotation cells you want to survey.
You have decided to divide the flotation cells into groups such
that you will collect five concentrate samples, in addition to the
combined concentrate sample that the bank produces. The sample
list is given in Table A1.
1. T bank feed
Cell 1
3. Con 1
Cell 2 to 4
4. Con 2
Dip sample
With the sampling points identified you need to communicate
your intentions to various people involved and prepare your
equipment. The same actions discussed above the Section: The
Metallurgical Survey, What do I have to do? apply to conducting
a down-the-bank survey. Communication and organisation are the
key 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 observations
that may be of use later.
Conducting a down-the-bank survey is usually a job for two
people. One person collects the sample, and the other measures
and records the time taken to collect each of the timed lip
samples. Prior to conducting the survey it is wise to inspect the
flotation bank to be samples, clean the cell lips to ensure that the
froth flows freely, make the work area safe and free of tripping
hazards, and establish how the lip sampling is going to occur,
then perform a dry run to practice how the timed samples will be
collected. One method of sampling that works effectively is for
the sampler to yell ‘GO’ and ‘STOP’ as he starts and finishes the
sample collection. On these commands the time keeper starts and
stops the stop watch. It is imperative that the two people
collecting the timed lip samples agree on the methodology, and
work in concert. So, with all the preparations complete, the plant
operating correctly it is time to conduct the survey.
As with other surveys you would have decided before hand
how many ‘cuts’ from each process stream will be taken over a
specified sampling time. The two people detailed to conduct the
down-the-bank survey will then collect the nine samples over the
prescribed sampling period. Once the sampling is finished, the
samples are gathered together and taken to the laboratory.
Cell 5 to 7
Cell 8 to 10
Cell 11 and 12
5. Con 3
6. Con 4
7. Con 5
8. T bank tailing
2. T bank combined concentrate
FIG A1 - A bank of 12 flotation cells, and the cell grouping for a down-the-bank survey.
28
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE A2
Summary of the down-the-bank survey data collected.
No
Sample name
Weights (g)
Gross
% solids
Tare
Bucket
Wet
Dry
1235.3
471.9
38.2
Lip
1
T bank feed
1585.3
350.0
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
What data do I need?
In the laboratory the samples are weighed (to determine the wet
weight), filtered, dried, weighed, prepped and submitted for
assay. A summary of this data is given in Table A2. The wet and
dry weights are used to determine the per cent solids of each
sample, and calculate a water balance. In terms of assays, apart
from those pertaining to the valuable minerals you are separating
from 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 lip
samples, the lip lengths of the flotation cells, and the lip sample
cutter length. With these data it will be possible to calculate the
flow 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 survey
mass 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 of
information may not be of great value. However, when the
analysis is extended to include other surveys on other ore blends,
circuit configurations, reagent suites, these data provide a vital
link in the comparison.
How do I analyse the data collected?
TABLE A3
Lip sample details.
No
Sample name
Sample
time (s)
Cell lip
Length (mm)
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
The assays will be returned to you in a form similar that shown in
Table A4. Before proceeding with mass balancing the survey it is
necessary to check these numbers are in good order. In this
example, the tailing assays are in good agreement, and the silver,
copper and lead assays all trend in the right way (ie from higher
assays in Con 1 to progressively lower numbers by Con 5).
Further, the combined concentrate assay falls within the extremes
of Con 1 and Con 5 assays.
Mass balancing this survey involves a number of steps. The
first step is to calculated the mass flow rate from the timed lip
samples. The mass flow rate is given by:
TABLE A4
Elemental assays for the down-the-bank survey.
No
Sample name
Assay (%)
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
Flotation Plant Optimisation
Ag (ppm)
Cu
Pb
Spectrum Series 16
29
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
Dry weight of sample, g 3600
×
×
Sample time, s
10 6
Cell lip length, mm
number of cell lips ×
Cutter lip length, mm
concentrate produced is 6.96 tonnes per hour, compared to 11.76
determined from the timed lip tonnages. The difference is
reconciled by applying the tonnage distribution from Table A5 to
the mass balanced tonnage given in Table A6. Thus, using the
‘outer’ circuit mass balanced tonnage new values are calculated
for each of the concentrates. These new values are presented in
Table A7, and are used in the second mass balance incorporating
the down-the-bank concentrates.
t/ h =
(A1)
Thus, by substituting the dry weights for each of the timed
concentrate samples from Table A2, and the sample times,
number of cell lips, cell lip length and cutter lip length from
Table A3 in Equation (A1), you can calculate the tonnes per hour
recovered into each concentrate. For example, for Con 1 – cell 1
Equation (A1) becomes:
t/ hCon 1 =
TABLE A7
The ‘new’ tonnes per hour for the five concentrates collected in the
down-the-bank survey calculated from the tonnage distribution
and the ‘outer’ mass balanced tonnage recovered into the
combined concentrate.
630.8, g 3600
1250, mm
×
×2×
31.6, s
10 6
105, mm
No
t/ hCon 1 = 1.71 t/ h
So, from the timed lip sample collected from Cell 1, the tonnes
per hour recovered are 1.71. The same calculation was completed
for each of the other concentrate. The values obtained are given in
Table A5. The tonnage distribution over the five concentrate
samples was calculated, and also appear in Table A5.
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 A5
The down-the-bank mass balance has eight process streams:
The tonnes per hour and distribution of tonnes for the five
concentrates collected in the down-the-bank survey.
No
Sample name
Tonnage data
Tonnes per hour
Distribution
1.
T bank feed,
2.
T bank combined concentrate,
3.
Con 1 – Cell 1,
4.
Con 2 – Cells 2 to 4,
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
5.
Con 3 – Cells 5 to 7,
6
Con 4 – Cells 8 to 10
3.17
26.91
6.
Con 4 – Cells 8 to 10,
7
Con 5 – Cells 11 and 12
0.81
6.88
7.
Con 5 – Cells 11 and 12, and
Total
11.76
100.00
8.
T bank tailing.
There are two nodes:
The next step is to mass balance the ‘outer’ circuit. The process
streams are:
1.
T bank feed = Con 1 + Con 2 + Con 3 + Con 4 + Con 5 + T
bank tailing (ie 1 = 3 + 4 + 5 + 6 + 7 + 8); and
1.
2.
Con 1 + Con 2 + Con 3 + Con 4 + Con 5 = T bank
combined concentrate (ie 3 + 4 + 5 + 6 + 7 = 2).
T bank feed,
2.
T bank combined concentrate, and
3.
T bank tailing.
The mass balance is accomplished by fixing the tonnage values
for the T bank feed, and the five down-the-bank concentrates,
then using a mass balancing software fitting the data. The
down-the-bank mass balance is given in Table A8.
The mass balanced down-the-bank data can now be used to
construct a lead grade/recovery curve for this bank of flotation
cells, as shown in Figure A2. This data can also be used to
generate kinetic data examining the flotation rate constant and
maximum recovery of the various species.
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 feed
tonnage was 100 per cent. The mass balanced data are provided
in Table A6. The mass balance has calculated that the tonnes of
TABLE A6
The mass balanced data for the ‘outer’ balance.
No
Sample time
Wt (%)
Adjusted assay (%)
Ag (ppm)
Cu
Pb
Zn
Fe
SiO2
21.78
1
T bank feed
100.00
98
0.04
2.91
10.67
15.48
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
30
Spectrum Series 16
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
TABLE A8
The down-the-bank mass balance.
No
Sample name
Wt
(%)
Ag
Cu
Weight (%)
Pb
Zn
Fe
SiO2
Ag
Cu
Recovery (%)
Pb
Zn
Fe
SiO2
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
T bank feed
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
Pb grade, %
1
25.0
Mineral conversions
20.0
Each conversion is based on the atomic mass of the elemental
components of the mineral.
15.0
Galena
Galena contains lead (207.2 amu) and sulfur (32.06 amu),
therefore the atomic mass of galena (PbS) is:
10.0
5.0
0.0
0.0
PbS = Pb + S
PbS = 207.2 + 32.06 amu
10.0
20.0
30.0
40.0
50.0
60.0
PbS = 239.26 amu
Pb recovery, %
OUTER balance data
Down-the-bank balanced data
FIG A2 - The lead grade/recovery curve constructed from the
mass balanced down-the-bank survey data.
Thus, ‘pure’ galena contains 86.6 per cent lead and 13.4 per
cent sulfur. The conversion factor (fGa) to convert the lead assay
to galena is given by:
fGa =
fGa =
APPENDIX 2 – ESTIMATED MINERAL ASSAYS
FROM ELEMENTAL DATA
Introduction
As iron occurs in a variety of minerals present in the Eureka
orebodies’ interpretation of iron deportment from elemental assay
data from flotation tests and plant surveys is a complex issue.
Therefore, by making a few simple assumptions regarding the
composition of the dominant sulfide minerals present in the
orebody, it is possible to estimate mineral assays from the
elemental assay data.
239.26
207.2
.
fGa = 1155
So, an assay of ten per cent lead is equivalent to 11.6 per cent
galena.
The sulfur in galena conversion factor (fSGa) is given by:
f SGa =
% sulfur in PbS
% lead in PbS
f SGa =
13.4
86.6
.
f SGa = 0155
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 as
sphalerite; the copper as chalcopyrite; and the iron occurs in
sphalerite, chalcopyrite and pyrite.
The mineral conversions are based on the assumption that each
mineral 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 by
assuming that everything that is not galena, sphalerite,
chalcopyrite, or pyrite is non-sulfide gangue.
Flotation Plant Optimisation
PbS amu
Pb amu
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 per
cent sulfur. Unfortunately, Eureka sphalerite contains an average
of 3.0 per cent iron in solid solution. Further, it is assumed that
Spectrum Series 16
31
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
the iron replaced zinc in the sphalerite lattice. Thus, the
composition of Eureka sphalerite is 64.1 per cent zinc, 3.0 per
cent iron and 32.9 per cent sulfur. Hence, the conversion factor
(fSp) to convert the zinc assay to sphalerite is given by:
f Sp =
100.00
641
.
f Sp = 1.560
fCh = 2.888
fFeCh =
So, an assay of ten per cent zinc is equivalent to 15.6 per cent
sphalerite. To determine the amount of iron present in sphalerite
it is simply a matter of multiplying the zinc assay by the ratio of
the per cent iron in the sphalerite and the per cent zinc in the
sphalerite. So, the iron in the sphalerite conversion factor (fFeSp) is
given by:
fFeSp =
% iron in sphalerite
% zinc in sphalerite
fFeSp =
3.0
641
.
fFeSp = 0.047
Thus, for a sample containing ten per cent zinc, the per cent
iron 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 solid
solution in sphalerite (ie 3.0 per cent), and the per cent iron
associated with sphalerite is 0.47 per cent.
Similarly, the sulfur in the sphalerite conversion factor (fSSp) is
given by:
f SSp =
32.9
641
.
f SSp = 0.513
Thus, for a sample containing ten per cent zinc, the per cent
sulfur 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 with
sphalerite is 5.13 per cent.
fFeCh =
30.43
34.63
fFeCh = 0.879
Thus, for a sample containing ten per cent copper the per cent
iron associated with the chalcopyrite is 8.8 per cent (ie 10 ×
0.879). To check this, convert the copper assay to chalcopyrite (ie
10 × 2.888), and multiply this by the amount of iron in solid
solution in chalcopyrite (ie 30.43 per cent), and the per cent iron
associated with chalcopyrite is 8.8 per cent.
Similarly, the sulfur in chalcopyrite conversion factor (fSCh) is
given by:
f SCh =
% sulfur in chalcopyrite
% copper in chalcopyrite
f SCh =
34.94
34.63
f SCh = 1.009
Pyrite
Assuming that pyrite is the dominant iron sulfide mineral present
(ie no pyrrhotite) it is relatively easy to calculate the per cent
pyrite 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
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 cent
copper, 30.43 per cent iron and 34.94 per cent sulfur. Hence, the
conversion factor (fCh) to convert the copper assay to chalcopyrite
is given by:
fCh =
32
% iron in chalcopyrite
% copper in chalcopyrite
Thus, for a sample containing ten per cent copper the per cent
sulfur associated with the chalcopyrite is 10.1 per cent (ie 10 ×
1.009). To check this, convert the copper assay to chalcopyrite (ie
10 × 2.888), and multiply this by the amount of sulfur in solid
solution in chalcopyrite (ie 34.94 per cent), and the per cent
sulfur associated with chalcopyrite is 10.1 per cent.
% sulfur in sphalerite
% zinc in sphalerite
f SSp =
183.52
63.55
So, an assay of ten per cent copper is equivalent to 28.9 per
cent chalcopyrite. To determine the amount of iron present in
chalcopyrite it is simply a matter of multiplying the copper assay
by the ratio of the per cent iron in the chalcopyrite and the per
cent copper in the chalcopyrite. So, the iron in chalcopyrite
conversion factor (fFeCh) is given by:
100.00
% Zn in sphalerite
f Sp =
fCh =
FeS2 = 119.97 amu
Thus, ‘pure’ pyrite contains 46.6 per cent iron and 53.4 per cent
sulfur. Depending on the assay data the pyrite content of the ore
can be calculated using either the iron or the sulfur assay.
Generally speaking, the estimation of the pyrite concentration is
more accurate using the sulfur assay because it is assumed that
the vast majority of the sulfur in the ore is associated with the
sulfide minerals. Iron, on the other hand, can be present in the
sulfides as well as the non-sulfide gangue, for example as iron
oxides such as haematite or magnetite, and feldspars to name but
a few. Thus, the pyrite conversion factor (fFePy) based on the iron
assay is given by:
CuFeS 2 amu
Cu amu
fFePy =
Spectrum Series 16
FeS 2 amu
Fe amu
Flotation Plant Optimisation
CHAPTER 1 – THE EUREKA MINE – AN EXAMPLE OF HOW TO IDENTIFY AND SOLVE PROBLEMS IN A FLOTATION PLANT
fFePy =
119.97
5585
.
However, if the pyrite conversion factor (fSPy) based on the
sulfur assay then:
f SPy = 1871
.
× (% S − (% sulfur in galena +
fFePy = 2148
.
So, based on the conversion factor developed from the iron
assay, an assay of ten per cent iron is equivalent to 21.5 per cent
pyrite.
Using sulfur, the pyrite conversion factor (fSPy) is given by:
f SPy =
f SPy
FeS 2 amu
S 2 amu
From the discussion above the conversion factor for sulfur in
galena from the lead assay is 0.155; sulfur in sphalerite from the
zinc assay was determined to be 0.513, and a similar calculation
revealed that the conversion factor for sulfur in chalcopyrite is
1.009. So, the per cent pyrite is given by:
.
×
f SPy = 1871
119.97
=
6412
.
(% S − ( 0155
. × % Pb + 0.513 × % Zn + 1.009 × %Cu))
f SPy = 1871
.
So, based on the conversion factor developed from the sulfur
assay, an assay of ten per cent iron is equivalent to 18.7 per cent
pyrite.
Unfortunately, iron is also associated with sphalerite and
chalcopyrite in the ore, so these contributions must be subtracted
from the iron assay before determining the pyrite content. Thus,
the pyrite conversion factor (fFePy) based on the iron assay
becomes:
. ×
fFePy = 2148
Thus, if an ore sample contained ten per cent sulfur, ten per
cent lead, ten per cent zinc, and 0.1 per cent copper, the per cent
pyrite is 6.0 per cent.
Non-sulfide gangue
If it is assumed that galena, sphalerite, chalcopyrite, and pyrite
are the only significant sulfide minerals present in the orebody it
is possible to estimate the per centage of non-sulfide gangue
present. 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)
(% Fe − (% iron in sphalerite + % iron in calcopyrite))
From the discussion above the conversion factor for iron in
sphalerite from the zinc assay was determined to be 0.0.47, and a
similar calculation revealed that the conversion factor for iron in
chalcopyrite is 0.879. So, the per cent pyrite is given by:
fFePy = 2148
. ×
(% Fe − ( 0.047 × % Zn + 0.879 × %Cu))
Thus, if an ore sample contained ten per cent iron, ten per cent
zinc, and 0.1 per cent copper, the per cent pyrite is 20.28 per cent.
Flotation Plant Optimisation
% sulfur in sphalerite + % sulfur in chalcopyrite ))
Thus, if a sample assayed ten per cent lead, zinc and iron, and
0.1 per cent copper, the per cent non-sulfide gangue is 57.8 per
cent.
Final comment
These element to mineral conversions have been completed based
on a lead/zinc ore, however a similar approach can be applied to
other ore types provided the stoichiometry of the minerals in the
system is known, and the assumptions made in the calculations
are clearly explained.
Spectrum Series 16
33