PARTICLES – THE BRIDGE BETWEEN GEOLOGY AND METALLURGY
Pertti Lamberg
Luleå University of Technology, Mineral Processing, SE-971 87 LULEÅ, SWEDEN
ABSTRACT
Geometallurgy combines geological and metallurgical information to create spatiallybased predictive model for mineral processing plants. A review of how geometallurgy is
currently applied in mining industry shows that the linkage of geological information and
metallurgical response relies on small number of samples tested in laboratory. Therefore a
holistic particle-based approach is proposed. The particle-based approach uses minerals
and particles as a common parameters going through the geometallurgical program from
the collecting of the geological data to the process simulations. The approach consists of
three quantitative models: 1) geological model, 2) particle breakage model and 3) unit
process models. The geological model describes quantitatively and spatially modal
composition and texture of the ore. The particle breakage model that describes
quantitatively what kind of particles will be produced as the rocks given by the
geological model are broken. The unit process models quantify how particles behave in
different unit operations. For developing and managing the models some practical
techniques are described and proposed. Finally the models are combined in a simulator
which is used to run the process simulation and derive process performance parameters
for each ore block individually. The process performance parameters include figures like
throughput; energy consumption; concentrate recovery and grade; and tailing properties.
Finally a practical example from Kemi chromite mine is given.
1
Introduction
Demands for more effective utilisation of orebodies and proper risk management
in mining industry have emerged a new branch called geometallurgy.
Geometallurgy combines geological and metallurgical information to create
spatially-based predictive model for mineral processing plants. It is not really a
discipline itself because it is related to certain ore deposit and certain processing
flow sheet and therefore it can be rather regarded as a practical amalgamation of
ore geology and minerals processing.
This paper makes a short review of how geometallurgy is currently applied in
mining industry. The review brings out the fact that the linkage of geological
information and metallurgical response relies on small number of samples tested
in laboratory. To strengthen the link this paper introduces a concept where
particles are used as a bridge between geology and metallurgy.
2
Geometallurgical program – a review
Geometallurgical program is an organized attempt to create reliable, practical
and useful model of an ore deposit and mineral processing plant that is used to
exploit the resource. Geometallurgical program goes through following steps
(modified after Bulled & McInnes 2005, David 2007 and Dobby et al. 2004,
Figure 1):
1. Collection of geological data through drilling, drill core logging,
measurements, chemical analyses, and other analyses.
2. An ore sampling program for metallurgical testing where geological data
is used in the identification of preferred locations for the samples.
3. Laboratory testing of these samples in order to extract process model
parameters (sometimes called ore variability testing).
4. Checking the metallurgical validity of the geological ore-type definitions
and, where necessary, developing new ore-type definitions called
geometallurgical domains.
5. Developing mathematical relationships for the estimation of important
metallurgical parameters across the geological database.
6. Developing a metallurgical model of the process. The model consists of
unit operations which use the metallurgical parameters defined above.
7. Plant simulation using the metallurgical process model and the distributed
metallurgical parameters as the data set.
8. Calibration of the models via benchmarking for existing operations.
1.
Collection of geological data
2.
Ore sampling for metallurgical testing
3.
Metallurgical laboratory testing
4.
Establishing geometallurgical domains
5.
Model to derive metallurgical parameters
6.
Establishing process model for simulation
7.
Plant simulations
8.
Model calibration
Figure 1. Steps of a geometallurgical program.
2.2
Benefits of the geometallurgical program
Justification for the geometallurgical program comes from the potential to bring
some of the following benefits compared to traditional approach:
 Better utilisation of the ore resources because ore boundaries are defined
also on the basis (forecasted) metallurgical performance.
 Better metallurgical performance because it is possible to tune the
process according to the information of the plant feed beforehand.
 Better controlled mining due to more comprehensive knowledge of the
ore body.
 Better changes in plant optimisation because the variation in the plant
feed is low or, at least, is better controlled.
 Better changes for new technological solutions because ore-derived
problems are identified well ahead and research programs can focus on
solving these.
 Lowering risks in the operation though better knowledge of the ore body
and the process and through more controlled process chain.
 Better possibilities for economical optimising of the full operation
considering metal prices, alternative products and costs of commodities.
2.3
Problem of representativeness
In the geometallurgical programs the weakest points are normally in inadequate
information collected from the drill cores and small number of samples sent for
metallurgical testing.
In the metallurgical testing quite small samples, in terms of size and number,
should represent large tonnages of the ore. Commonly some tens of carefully
selected and prepared samples are tested (Schowstra et al. 2010, Morrell, 2009,
Philander & Rozendaal, 2008 & 2010), but there are cases where the whole
program is based on less than ten samples (David 2007, Suazo et al. 2010). This
sets high requirements for the sampling and sample preparation to avoid the
sampling error to rise so high that it limits the usefulness of collected data (Gy
1982, Pitard 1989a and b). There lies also a dilemma in selecting and preparing
metallurgical samples based on geological information: tested samples should
represent the full variability of the ore in terms of metallurgical response and
this can be known only after the tests have been done.
To make the sampling (step 2) and definition of geometallurgical domains (step
3) more reliable new rock measurement and analysis techniques are needed (for
the step 1). This has been under strong development in recent years (Walters
2008). The challenge is that measurements and analyses have to be done for a
very big number of samples, from thousands to hundreds of thousands.
Techniques must be fast, inexpensive and preferentially fully automated.
Detailed review of the techniques is out of the scope of this paper, but broadly
they can be divided in three groups: a) techniques measuring rock properties, b)
techniques of quantitative mineralogy and c) geometallurgical tests.
Logging of petrophysical parameters directly from the drill core is an example of
(a) geometallurgical technique which measures the properties of rock
(Vantandoost et al. 2008). Quantitative mineralogical techniques (b) measure the
mineralogical properties like modal composition, mineral grain size, mineral
association and presence of some minerals. Techniques used are optical
microscopy image analysis (Leinonen 1998; Liipo et al. 2004; Hunt et al 2008;
Lane et al. 2008; Donskoi et al. 2007, 2008), scanning-electron (SEM) based
image analysis (Gu 2003, Fandrich et al. 2007; Oghazi et al 2009, 2010, Lund &
Martinsson 2008, Lund et al. 2010, Hallewell 2009, Hoal et al. 2009), X-ray
diffraction (Helle 2005, Knorr 2010) and reflectance spectroscopy
(hyperspectral methods; CSIRO 2011, Haavisto & Kaartinen 2009, Da Costa et
al. 2009, Pirard et al. 2008).
Methods measuring the metallurgical response directly (c) are called
geometallurgical tests. This is an area of rapid development but scientific
publications are currently very few. In comminution commercial laboratories
report on the existence of following geometallurgical tests (JKTech 2010): GeM
Comminution Index (GeMCi), GeM RBT Lite (GeM Rotary Breakage Tester,
Lite) and EQUOtip (Portable hardness tester). In downstream processes
Bradshaw (2010) has mentioned a small-scale flotation test called JKMSI (JK
Mineral Separability Indicator). Lund et al. (2010) used laboratory-scale dry
magnetic separator (Mörtsell) to measure the metallurgical response of iron ore
to magnetic separation.
Metallurgical or geometallurgical parameters determined by testing don’t
forecast directly the behaviour of a single ore block in a full process. For this a
mineral processing model needs to be built (step 6). The model includes the full
closed processing circuit and composes of unit processes. The unit processes
include static process related parameters (like number of units and size of the
units), and ore related parameters which match with the geometallurgical
parameters determined earlier.
The simulation environment must be capable to handle both size reduction and
concentration. Traditionally comminution simulations and concentration
simulations have been separate. The former gives throughput, energy
consumption and size distribution of the comminution circuit (Morrell, 2009)
but doesn’t deal with chemical elements and minerals. Thus, it does not give the
size distribution of the comminution circuit product (e.g. cyclone overflow) by
mineral. Concentration models, on the other hand, are mostly ignoring the
particle size distribution (e.g. Runge et al. 1997).
Once the models are combined in a simulation platform it is possible, by running
a steady-state simulation, to get metallurgical performance figures like
throughput, energy consumption, concentrate recovery and grade, and tailing
properties for each ore block or feed blend. The simulations can be done
basically with any steady-state simulator capable to handle both comminution
and concentration processes and at least JKTech (JKSimMet: Morrison et al.
2002; JKSimFloat: Collins et al. 2009), SGS/Minnovex (CEET: Kosick et al
2001, FLEET: Dobby et al. 2002) and Outotec (HSC Chemistry: Lamberg et al.
2009, Lamberg 2010) provide such tools. SGS has developed a specific
geometallurgical simulator, IGS, Integrated Geometallurgical Simulator, which
can solve steady-state problems faster than traditional simulators mentioned
above (Hatfield et al. 2010).
The main problem in the process model is that the original model parameters
have to be derived from limited number of laboratory tests and still they should
give a reliable forecast for the full deposit at any time of the history of the mine
(Figure 2). The model should respond to changes in the process, for example
changes in head grade and throughput.
3
Particle-based approach
Particle-based approach proposed here uses minerals and particles to link
geology and metallurgy. As such this approach has not been used but all the
components required have been applied separately in some of the referred
studies.
The particle-based approach can’t avoid the fact that the samples selected for the
metallurgical testing form the crucial base for the information and if the samples
are not representative the final geometallurgical model is erroneous. However,
particle-based approach uses minerals and particles from the very beginning to
the end (Figure 2). Therefore there are common parameters which go through
the different steps of the geometallurgical program. This gives better changes to
succeed in the critical steps of sampling (step 2, Figure 1) and building a model
between geology and metallurgy (steps 4-5, Figure 1).
Particle-based geometallurgical approach consists of three models (Figure 2):
1) Geological model which gives the mineralogy by ore block.
2) Particle breakage model which forecasts which type of particles will be
generated when different ore blocks and rocks break down.
3) Unit process models forecast how different particles behave in a unit
process.
3.2
Geological model
In the particle-based approach it is required that the geological model describes
the mineralogy of the ore block in a quantitative and adequate way. This is a
challenging, and potentially expensive, requirement because much of the
geological data gathered traditionally is qualitative or semi-quantitative. The
model should give, preferentially, modal composition (mineral composition by
weight percentage) and textural information throughout the ore deposit (block
model). The modal composition can be solved from chemial assays with
element-to-mineral conversion (Lamberg 1997, Whiten 2008, Lamberg and
Vianna 2009). This, however, requires that mineralogy is not too complex and
chemical assays are designed properly. Methods like X-ray diffraction and
reflectance spectroscopy can be used as discussed above (chapter 2).
Process model
parameters
• Geological data
• Metallurgical
testing
Samples for
metallurgical
testing
• Model to derive
metallurgical
parameters from
geological data
Mineralogy
• Plant model and
simulations
Metallurgical
parameters into
block model
Particles
• Geological
model
Production
forecast
• Particle
breakage
model
• Unit
process
model
Production
forecast
• Simulations
Particles
behaviour
Figure 2. Comparison of currently used geometallurgical approach (above) and
particle-based approach proposed here (below).
Textural information is much more difficult to describe quantitatively and to
model than modal composition. Furthermore, unlike mineral grades, textural
variables are not necessarily linear or additive and therefore require very careful
geostatistical consideration when applied in the block model (Dunham & Vann
2007).
The AMIRA P843 and P843A projects have done a lot of research and
development in quantitative characterisation of textures (Walters 2008, AMIRA
2009) and part of that work has been published (Berry et al. 2008; Bonnici et al.
2008 and 2009; Hunt et al. 2008 and 2010). In the project a relatively
inexpensive method for collecting mineralogical data has been developed. Drill
core samples are crushed and a polished thin section are prepared from one
coarse size fraction (0.6-1.18 mm). By optical micrsocopy and advance image
analysis particles and minerals are identified automatically. Textural information
is stored in mineral maps (Figure 3) for further processing by image analysis
techniques (see chapter 3.3). Besides textural information the technique gives
also a modal composition of (one, representative size fraction of) the sample.
Figure 3. Mineral maps produced by the MLA:
‘Frame view’ (left) and a close up of one
particle in a ‘particle view’ (above) of a nickel
Flash Concentrate, size fraction 75-150
microns.
3.3
Particle breakage model
The particle breakage model of the particle-based geometallurgical approach is a
general rule or collection of rules which quantitatively tells what kind of
particles will come out as the rocks, given by the geological model, are broken.
A name liberation model has also been used but here, a more generic name was
chosen to emphasize that current model does not gives liberation distribution
described by statistics but actual particles.
As an input the model takes the modal composition and the textural information
from the geological model. In addition the model requires that the size
distribution of the progeny particles is given; this comes from the unit model
(see chapter 3.4). The model gives as an output the progeny particles. Each
particle in the model output has following properties:
 size,
 mineral composition by weight,
 mineral composition by volume,
 mineral composition by surface area,
 flowrate or mass proportion of all the particles (t/h), and
 (texture as a particle map, potentially).
Several different model have been developed in past to forecast the liberation
distribution of comminution products. Models which take an image of an
unbroken ore and calculate the outcome by random breakage assumption (e.g.
King 1979, Stamboliadis, 2008; Gay 2004) were found to fail (Laslett et al.
1990). Introduction of more parameters made the model very difficult and
tedious to calibrate and their portability became questionable (Wei and Gay
1999; Gay 1999, 2004; King & Schneider 1998).
Two quite recent studies serve a simpler approach matching perfectly with the
structure of the particle-based geochemical approach described here. Hunt et al.
(2008, 2010) and Bonnici et al. (2008, 2009) simulated the fragmentation of
particles by applying chessboard segmentation on the particle map of one size
fraction (0.6-1.18 m, see previous chapter for description of the measurement).
Vizcarra et al. (2010), on the other hand, showed that liberation distribution is
conserved in narrow size classes regardless of global size distribution of the
sample or of the breaking mechanism. These studies introduce a new technique
for establishing the particle breakage model: from the rock texture given by the
geological model it is possible to populate particles with mineralogical and
physical properties for any given size distribution.
3.4
Unit process models
The unit process models describe quantitatively how different particles behave
in a single processing stage. In minerals processing the behaviour of particles is
dictated by particle properties therefore the unit models have to include, as
parameters, the particle properties like size, composition and density. The
structure of the simulator binding up the unit models must be based on particles
(Lamberg 2010).
The unit operations models used in minerals processing can be divided in three
types:
 Comminution models where particle size distribution changes.
 Separation models where particles are distributed between two or more
output streams based on their physical properties.
 Leaching and precipitation models where liquid phase is an active
component and minerals dissolve and new phases are formed through
chemical reactions.
In the comminution unit models (grinding mills, crushers) it is possible to use
the particle breakage model described above. Therefore in the model the
forecasting of liberation distribution and total size distribution can be decoupled.
In the latter the traditional population balance breakage models can be used
(Weller et al. 1996, Alruiz et al. 2009, Vogel & Peukert 2003).
Most of the models of separation and leaching, which industry uses, are semiempirical. In minerals processing the fundamental process model based entirely
on physics, chemistry and particle properties are still quite far away from being
practical and accurate enough for everyday use. For example the physical
flotation model which describes sub processes of collision, attachment, bubble
rise, detachment and froth behaviour requires tens of parameters, which are
mostly difficult to determine or estimate (King 2001). Therefore commonly used
approach is so called floatability component model, where each mineral is
divided into three kinetic types: fast floating, slow floating and non-floating
(Runge et al. 1997). Although the division into kinetic types is based on
mathematical model fitting, Polat and Chander (2000), and more recently
Welsby et al. (2010), have found that there exist a clear link between the
floatability type and physical properties of particles (i.e. mineral composition of
particles).
The development of property based models in minerals and metallurgical
processing requires that particle properties can be measured in different parts of
the process. Liberation analysis is state-of-the-art technique (Sutherland et al.
1997, 1998, Gu, 2003, Fandrich et al. 2007) and X-ray tomography is an
emerging method (Miller et al. 2003).
The liberation measurement gives quantitative information on particles in
process stream but for the modelling purposes a particle mass balance is
required. Lamberg and Vianna (2007) have developed a particle tracking, which
is a mass balancing technique of multiphase liberation data. The technique gives
a quantitatively description how different particle types (liberation classes)
behave in single process units and the full process. Vianna (2004) and Lamberg
& Vianna (2007) have shown that in flotation the flotation kinetics of binary
galena-sphalerite and galena-quartz particles differs significantly from each
other (Figure 4).
Figure 4. Recovery of binary galena-sphalerite (above) and galena-quartz
(below) particles of size fraction 10-20 microns into the concentrate in
continuous laboratory flotation test (Vianna 2004, Lamberg & Vianna 2007). Xaxis gives the mass proportion of galena in binary particles; 100% equals to
fully liberated galena particle.
The particle tracking technique gives an opportunity to develop unit models
which are based on particle properties. This is an attractive tool for research and
development but it is possible to apply the technique in a practical way in the
particle-based geometallurgical program. The working plan is as follows:
 In selected metallurgical tests samples are sent for liberation analysis.
 Particle tracking technique is applied to establish a particle balance in
each test. The particle classes are defined in a uniform way.
 Property-based unit models are created based on particle balance.
3.5
Combining the model in a simulation
After completing the steps 1-6 it is time to put all the models together and do
the geometallurgical simulations to derive the outcome of the full program:
metallurgical performance figures like throughput, energy consumption,
concentrate recovery and grade, and tailing properties for each ore block or feed
blend. In practise this goes through following stages:
 The process simulator is established to include all the minerals and
particle classes which were found in the particle tracking to be
significant. This is called global definitions (Figure 5).
 The process flowsheet is drawn and property-based models and model
parameters are defined (unit properties Figure 5).
Global definitions
•
•
•
•
•
•
Minerals
Size Classes
Particles
Other solid component
Liquid components
Gas components
Stream, basic properties
• Particle Flowrates
• Liquid component flowrates
• Gas component flowrates
Stream, calculated properties
• Mineral (modal) composition, chemical composition
• Denstity (specific gravity), %solids, …
• Enthalpy, …
Unit properties
• Static unit parameters (e.g. size)
• Operational parameters
Figure 5. Structure of the property based simulator for minerals processing
(Lamberg 2010).
 The geological model and geostatistical methods are applied through the
whole geological database to have the required mineralogical
information (modal composition, textural description) in all blocks in the
ore block model (Figure 6).
 The master simulation loop is established and run (Figure 6).
o The master loop goes one-by-one through the block model, takes
one block and sends the information to the process simulator.
o Process simulator takes the geological information and updates
the feed characterisation information (called stream basic
properties or mineral setup, Figure 5).
o Steady-state simulation is run.
o The key parameters like throughput, final concentrate tonnages,
grade, and recovery are read from the simulation.
o The loop goes to the next block.
 The process performance parameters are returned into the block model
(Figure 6).
Figure 6. Example of simulation platform where ore block model, mining
schedule and blend calculator has been combined with the particle-based model.
Each block and blend is simulated separately to give forecasted metallurgical
performance.
The strucure enables to run simulations to schedule the production and to study
different blending strategies.
4
Practical example – Kemi chromite mine
The particle-based approach may sound tedious and one can question whether
the potential benefits will realise and pay back the costs of the program. As such
the program is not in use. However, one example of a long ongoing
geometallurgical program utilising mineralogy and particles in mining industry
can be given.
At Kemi chromite mine mineralogical information has been collected and used
systematically in production planning for more than 15 years (Leinonen 1998,
Huovinen 2007, Huhtelin pers. comm. 2011). The grain size of chromite varies
in the deposit a lot and that has a big effect on processing. Therefore chromite
grain size is measured in a systematic way from the drill cores. Polished sections
are prepared in (unbroken) ore samples and the grain size distribution of
chromite is determined with optical microscopy and image analysis (Figure 7).
This information is fed into the ore block model. The size distribution of
chromite is used to evaluate whether ore is suitable for fine concentration and to
forecast the expected recovery. Estimation is based on findings that the grain
size of chromite after grinding follows the grain size of the original chromite in
the ore and that the recovery losses come in the gravity separation in the fine end
(<80 microns).
100
80
11
60
Passing-%
12
12_2*
13
40
14
20
0
1000
550
700
350
450
150
250
µm
50
100
Figure 7. Processed binary
images of Kemi Cr ore
illustrating the variety in the
grain size of chromite in the
deposit. The width of the picture
is about 3 mm. Chromite is
shown in white, other minerals
are black. Graph on left shows
the cumulative size distribution
of chromite. Size distribution
data is generated from the
optical images by image
analysis techniques (Leinonen
1998, Liipo et al. 2004).
5
Acknowledgements
This paper was prepared within the Geometallurgy workpackage of the Center
of Advanced Mining and Metallurgy (CAMM, WP1). I’d like to thank Timo
Huthelin (Outokumpu) and Jussi Liipo (Outotec) for their help and permission
of using Kemi as a practical example. I’m grateful to Sergio Vianna (University
of Queensland, Julius Kruttschnitt Mineral Research Center) for excellent cooperation in the AMIRA P9 project where we developed the particle tracking
technique.
6
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