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Application of process mineralogy as a tool in sustainable processing
Minerals Engineering 24 (2011) 1242–1248
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Application of process mineralogy as a tool in sustainable processing C.L. Evans a,⇑, E.M. Wightman a, E.V. Manlapig a, B.L. Coulter b a b
The University of Queensland, Sustainable Minerals Institute, Julius Kruttschnitt Mineral Research Centre, Indooroopilly, QLD 4068, Australia Xstrata Technology, Level 4, 307 Queen St., Brisbane, QLD 4000, Australia
a r t i c l e
i n f o
Article history: Available online 9 April 2011 Keywords: Ore mineralogy Comminution Froth flotation Liberation
a b s t r a c t The observed behaviours of mineral particles in mineral processing operations have been exploited in the past to model comminution and concentration processes. In this work this concept has been taken a step further, exploiting the mineralogical characteristics of particles to link comminution, concentration and smelting. This approach is demonstrated using a laboratory-based case study of a Ni–Cu sulphide ore. The case study focused on the effect of shifting energy between the comminution and smelting stages on the overall energy consumption for the metal production process. To model this effect the mineral composition of the particles was linked to the behaviour of the ore particles in the primary grinding, regrinding and flotation stages. This application of process mineralogy provides a methodology to minimise energy use across mineral concentration and smelting processes, an important aspect of sustainable processing. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The increasing availability of automated mineralogy systems provides an opportunity for metallurgists to model mineral processing operations in terms of particles and their mineral composition. The models published in the literature fall into two categories – those based on fundamental properties of the mineral particles and those based on heuristics developed by observing the processing behaviours of ore particles. The fundamental modelling of mineral liberation in comminution is a research area which has exercised the minds of many researchers, starting with Gaudin in 1939 and continuing to the present day (Wiegel, 1976; Barbery and Leroux, 1988; King, 1979; Gay, 1994). Similarly the prediction of flotation behaviour from fundamentals is a continuing area of research with many branches of science including surface chemistry and electrochemistry contributing to the understanding of this complex process. While the fundamental research continues, the need for models which can be applied to simulate the effect of changes to liberation and the subsequent impact on flotation processes has led other researchers to develop models of these processes based on heuristics. Such models have been used in the past to optimise the process chain in terms of a variety of parameters for example mineral recovery (Bazin et al., 1994) and economic value (McIvor and Finch, 1991). With the recent focus on sustainability in mining and the drive for resources companies to minimise the energy use in energy⇑ Corresponding author. Address: JKMRC, 40 Isles Road, Indooroopilly, QLD 4068, Australia. Tel.: +61 7 3365 5888; fax: +61 7 3365 5999. E-mail address: [email protected] (C.L. Evans). 0892-6875/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.mineng.2011.03.017
intensive processes such as comminution and smelting, there is a need for tools which allow companies to optimise their process energy use. This research focuses on using quantitative mineralogy and heuristic models to identify where in the comminution–flotation–smelting process chain energy is most efficiently applied; in particular, this work can assist engineers in developing strategies to reduce overall energy consumption across the concentrator and smelter process chain by adding more regrinding energy to remove from the final concentrate minerals which are deleterious to the smelting process. Metallurgists recognise that there is scope to tune the operation of a concentrator to change the product quality and also scope to tune the smelter operation to smelt a different grade of concentrate. However, there is no integrated tool currently available which allows companies to model the combined effect of these changes on overall energy consumption in the mill and smelter. The methodology developed in this research, termed ‘‘Mill to Melt’’ provides such an integrated approach in which the linkage between the various processes is information on particle mineralogical composition. 2. Modelling liberation and flotation The methodology developed in this research exploits some heuristics which are based on the observed behaviours of ores in comminution and flotation. A range of authors, starting with Bérubé and Marchand in 1984, have observed that the amount of liberated mineral in a given size fraction is the same, regardless of where in the comminution circuit the sample is taken. The heuristics have been reported to be applicable to a range of ores including iron ore (Bérubé and Marchand, 1984), Mt Isa lead–zinc ore (Manlapig et al., 1985), a range of Australian copper ores and a lead–zinc ore
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(Wightman et al., 2008). More recently Evans and Wightman (2009) showed that the heuristic applied to both the sulphide and silicate gangue minerals in copper and lead-zinc ores which exhibited a range of ore textures, including massive, net and disseminated sulphides. Vizcarra et al. (2010) have also shown that the products of impact, and compression breakage show similar particle composition distributions at a given particle size, indicating that the heuristic applies regardless of the breakage mode used. Based on the observations in the literature the heuristic appears to be widely applicable and robust and therefore this approach was assessed as a potential means of modelling liberation at a range of grinding product sizes. For the flotation modelling a heuristic based on the relationship between flotation rate and/or mineral recovery and particle mineral composition was assessed. Earlier researchers starting with Imaizumi and Inoue in 1963 have observed that for a given particle size as the proportion of the target mineral in a particle increases the flotation rate of the particle increases. This behaviour has been reported for a range of ores including copper ores (Imaizumi and Inoue, 1963; Sutherland, 1989), lead-zinc ores (Steiner, 1973; Frew and Davey, 1993; Vianna, 2004) and iron ore (Bartlett and Mular, 1974). The form of the relationship between the flotation rate and the mineral composition of the particle at a given particle size varies from one ore to the next; for example in the flotation of haematite Bartlett and Mular (1974) observed an S-shaped relationship while in the flotation of galena Vianna (2004) observed a gradual increase in flotation rate with galena content of the particle with a much sharper increase in flotation rate when particle composition exceeded 70% galena. In the present work, if the flotation rate of a given size and composition class is constant under a fixed set of flotation conditions, this property can be exploited to model flotation recoveries for ore ground to different flotation feed size distributions both in rougher flotation and cleaner flotation. 3. Methodology 3.1. Ore characterisation The ore used for the case study was a sample of Ni–Cu sulphide ore from the Timmins region in Canada. The head assay of the ore sample is shown in Table 1 and the modal mineralogy of the ore measured using the FEI MLA system at JKMRC is presented in Fig. 1. The copper in the ore is present as chalcopyrite and the nickel is present both as pentlandite and in solid solution in pyrrhotite. Electron microprobe analysis of the pyrrhotite indicated that this mineral contained 0.35% Ni in solid solution; the resulting distribution of Ni across the Ni-bearing minerals in the ore was 94% of the total Ni present as pentlandite and 6% present in pyrrhotite. As well as providing information about the distribution of Ni across the two Ni-bearing minerals the mineralogical data also provide information about the distribution of Mg across a range of MgO-bearing minerals. This information is an important input to the smelter modelling since the amount of MgO in the smelter feed can affect the viscosity of the slag and the liquidus temperature. As discussed by Lotter et al. (2008), MgO-bearing minerals such as orthopyroxene can be recovered to the nickel concentrate by a range of mechanisms, namely by entrainment of fine liberated particles, by locking in composite particles, by inadvertent activation from ions in the slurry and by natural flotation. The presence
Others
Aluminosilicates
Pentlandite
Oxides
Pyrrhotite
Pyrite
Chalcopyrite Carbonates Fig. 1. Modal mineralogy of the ore.
of talcaceous minerals which show natural flotation behaviours can also further increase MgO recovery by entrainment, as these have a strong effect in increasing froth stability (Bradshaw et al., 2005; Kuan and Finch, 2010) which in turn increases water recovery to the froth and hence the amount of entrained material (Johnson, 1972). In this ore the distribution of MgO across the range of minerals present is as shown in Fig. 2, with chlorite, talc and amphibole being the main sources of MgO. Two of the MgO-bearing minerals – talc and biotite – may be recovered to the concentrate by flotation since earlier research has shown that these minerals exhibit natural flotation properties (Chander et al., 1975; Fuerstenau et al., 1988; Rath and Subramanian, 1998). As a result, talc and biotite are potential sources of elevated levels of MgO in the bulk Ni– Cu concentrate. Detailed knowledge of the ore mineralogy and its impact on the various processing stages, particularly in this case the Ni smelter, informs the decisions on which minerals must be tracked in this
Other Biotite Diopside
Amphibole
Table 1 Head assay of Ni–Cu sulphide ore. Element
Ni
Cu
Fe
SiO2
MgO
Al2O3
CaO
S
Assay (%)
1.18
0.67
18.9
42.6
4.17
12.3
6.1
10.6
Hornblende Fig. 2. Distribution of MgO across MgO-bearing minerals in the ore.
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methodology. For example, for this ore all minerals which contain Mg need to be tracked since, as Lotter et al. (2008) observe, ‘‘the total amount of MgO present in a concentrate is what drives the smelting problem, regardless of which mineral source’’. In general the key minerals which require tracking may vary depending on the ore and its chosen process route and would need to be identified on a case by case basis. 3.2. Laboratory scale grinding and flotation The laboratory grinding and flotation procedure for this ore, as shown in Fig. 3, consisted of primary grinding followed by a rougher flotation stage; the rougher concentrate was then subjected to either no regrinding, low level regrinding or medium level regrinding before a stage of cleaner flotation to generate a bulk Ni–Cu concentrate. In the operating plant from which the ore sample originated the ore was further separated into a Cu concentrate and Ni concentrate but in the laboratory-scale work discussed here the small masses of bulk Ni–Cu concentrate generated made this final separation stage impractical. In order to create nickel and copper concentrates for use in the smelter modelling, a theoretical separation was applied to the experimentally-generated bulk concentrate; the recoveries and grades applied in this theoretical separation were based on typical plant performance data for this ore. The primary grinding circuit used in the experimental work consisted of a laboratory stainless steel rod mill operated at 67% solids, with a power meter installed to measure energy requirements during grinding. The subsequent rougher flotation was performed in a bottom-driven 4.7 L JK batch cell with timed concentrates being collected at 0.5, 1, 2, 4, 8 and 16 minutes to provide kinetic information. In order to identify whether this ore follows any existing heuristics in its breakage/liberation and flotation behaviours, three primary grinding/rougher flotation tests were carried out at three different primary grinding product size distributions. The grinding and flotation products were analysed using the MLA systems at JKMRC to measure the particle composition distribution in each size fraction for comparison. In the second stage of the research three alternative process routes for upgrading the rougher concentrate from the intermediate primary grind test were investigated – one with no regrinding of the rougher concentrate prior to a cleaner flotation stage and a second and third process route in which the rougher concentrate was subjected to low and medium levels (respectively) of regrinding in a stirred mill prior to cleaner flotation. The laboratory scale,
Primary grind (Lab. rod mill) 1kg ore (100% -4mm)
stainless steel stirred mill used for this work was charged with 6 mm diameter glass grinding media and had a power meter installed to measure energy requirements during regrinding; nonreactive materials were used in the regrinding section to minimise the effect of regrinding on the pulp chemistry, allowing the work to focus on the physical attributes of the particles being processed. In the final stage of the experimental procedure the mineral composition of the final Ni and Cu concentrates was calculated by mathematically splitting the bulk Ni–Cu concentrate using grade and recovery values based on typical plant performance of this ore. The elemental composition of the Ni and Cu concentrates was calculated from the mineral data and the two sets of compositional data were input to the appropriate smelter models. The mineralogical composition of the final concentrate particles provides this information and is the link which integrates the concentrator and smelter modelling. The latter used models of the various processes within the smelter operation (including thermodynamic models of smelting) to calculate the energy requirements to generate blister copper from the Cu concentrate and nickel matte from the Ni concentrate. While details of the smelter model outputs are reported to confirm that the smelter models will link to the grinding and flotation models, no details of the proprietary smelter models are able to be provided here. 4. Results In the initial stage of the work of the ore was ground to three different product size distributions which are shown in Fig. 4. As can be seen, the 80% passing sizes (P80) of the three primary grinding products are 52, 81 and 105 lm. When the degree of liberation for pentlandite was analysed in the size fractions from the rod mill feed and the three rod mill grinding products, as shown in Fig. 5, it was clear that the ore follows the heuristic previously described in the literature. For size fractions below 75 lm, for this ore, the pentlandite liberation in a given size fraction does not vary significantly, regardless of whether the material comes from the rod mill feed or a finer or coarser primary grinding product. A similar pattern is observed for the pyrrhotite particle composition distribution across the full range of composition classes. This consistent behaviour indicates that the particle composition data from the rod mill feed could be used to predict the particle composition distribution for the grinding products. The ability to use the heuristic based on particle composition distribution
Rougher flotation (4.7L batch cell) Rougher tail
Rougher feed Bulk rougher conc. Regrind
Bulk cleaner Cleaner tail
(Laboratory stirred mill)
Bulk Ni/Cu conc.
Fig. 3. Experimental flow sheet.
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90%
90%
Cumulative weight % finer than size
100%
Cumulative weight % of sample finer than size
100%
80% 70% 60% 50% 40% 30% 20% Grind time = 25 min Grind time = 32 min Grind time = 42 min
10%
80% 70% 60% 50% 40% 30% 20% Regrind Feed Product 5 min regrind Product 10 min regrind
10% 0% 1
10
100
1000
Size ( m)
0% 1
10
100
1000
Size ( m)
Fig. 6. Particle size distributions of regrind mill feed and products.
Proportion of pentlaandite in liberated (90-100%) class (%)
Fig. 4. Particle size distributions of primary grinding products.
100 90 80 70 60 50 40 30 20 10 0 1
10
100
1000
Particle size (m) Rod mill feed
25 min grind product
32 min grind product
42 min grind product
Fig. 5. Comparison of pentlandite particle composition distribution data for rod mill feed and three primary grinding products.
Proportion of pyrrhotite in size and composition class (%)
being constant at a given particle size to model liberation would considerably reduce the amount of physical testing required to optimise the primary grind size in terms of overall energy consumption across the process chain. The heuristic also correctly describes the breakage/liberation behaviour in the regrind circuit. When the rougher concentrate is
subjected to regrinding at one of three levels – none, low or high – the minerals in the resulting cleaner feed stream obey the ‘‘rule of thumb’’. Over the range of cleaner feed size distributions studied, in which the P80 values were 57, 44 and 30 lm as shown in Fig. 6, the proportion of each particle composition class in each size fraction is essentially the same irrespective of the amount of breakage applied. The results of the mineralogical analysis for pentlandite, chalcopyrite and pyrrhotite in the regrind feed and products indicated that the particle composition distribution of these minerals in regrinding products could be adequately described from mineralogical analysis of the regrind feed and size by assay information for the regrind products. The distribution of pyrrhotite across the size and particle composition classes in the regrind feed and products is provided in Fig. 7 as an example of the behaviours observed follow the breakage/liberation heuristic. The flotation response of the ore was assessed for both the rougher and cleaner flotation stages to determine whether the flotation response followed the patterns identified by earlier researchers in which the flotation rate is a function of particle composition as well as particle size; an example of the flotation responses observed for the major sulphide minerals, in this case for liberated chalcopyrite, are shown in Figs. 8 and 9. Similar results were observed for pentlandite and pyrrhotite and for each of the composition classes (30–60% and 60–90% chalcopyrite). This characteristic behaviour allows the flotation rate values measured for a given composition and size class to be ap-
100
Regrind feed 90-100% PO Regrind feed 60-90% PO Regrind feed 0-30% PO 5 min regrind 90-100% PO 5 min regrind 60-90% PO 5 min regrind 0-30% PO 10 min regrind 90-100% PO 10 min regrind 60-90% PO 10 min regrind 0-30% PO
80 60 40 20 0
1
10
100
Particle size ( m) Fig. 7. Comparison of pentlandite particle composition distribution data for regrind mill feed and products.
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Flotation rate K (min-1)
10
1
0.1
0.01 1
10
1000
100
Particle size ( m) 25 min primary grinding product 32 min primary grinding product 42 min primary grinding product Fig. 8. Plot showing variation in flotation rate for liberated chalcopyrite (90–100% chalcopyrite class) particles in rougher flotation.
Flotation rate K (min-1)
10
1
0.1
0.01 1
10
100
1000
Particle size ( m) No regrind 5 min regrind product
5. Discussion
10 min regrind product Fig. 9. Plot showing variation in flotation rate for liberated chalcopyrite (90–100% chalcopyrite class) particles in cleaner flotation.
Others Aluminosilicates MgO minerals Feldspars
Pentlandite Pyrrhotite Pyrite
Quartz
Chalcopyrite 77.2% by weight
No regrind
plied to predict the flotation response of similar particles which derive from feed streams with a different overall size distributions. The cleaner flotation stage generated the bulk Ni–Cu concentrate which was separated mathematically into a final Cu concentrate and a final Ni concentrate. The mineral compositions of these final products for two of the regrind conditions investigated are compared in Figs. 10 and 11. As would be expected, the effect of regrinding was to allow a higher grade of concentrate to be produced at similar or higher recovery. In the case of the copper concentrate the chalcopyrite content increased from 77.2% to 83.0% mainly due to the rejection of pyrrhotite in the cleaner flotation. Comparing the nickel concentrates, the results showed that the pentlandite grade of the concentrate increased from 34.1% to 42.6% after regrinding, mainly due to the improved rejection of pyrrhotite and non-sulphide gangue minerals. The reduction of the MgO content of the nickel concentrate from 3.2% to 3.0% as a result of regrinding was not as great as anticipated. Inspection of the MLA data for the cleaner concentrate indicated that a large proportion of the talc (the major source of MgO in the concentrate) was highly liberated and could be rejected by identifying the main mechanism(s) of talc recovery (i.e. entrainment, activation by ions in solution or natural flotation (Lotter et al., 2008)) and developing an appropriate treatment route. Detailed optimisation studies aimed at reducing talc recovery were not part of the proof of concept investigations presented here but would form part of any detailed work in future. In the final stage of the methodology the mineral compositions of final copper and nickel concentrates and the elemental assays calculated from the mineral compositions, were used as inputs to the smelter models to calculate the energy requirements to produce blister copper and nickel matte from the respective concentrates. The overall results of the Mill to Melt methodology including the energy requirements estimated by the smelter modelling are summarised in Fig. 12 for two of the regrind conditions investigated (see Evans et al. (2009) for more details). The results of applying the methodology show that for this ore using a small amount of energy to regrind the rougher concentrate reduces the total energy requirements across the concentrator– smelter process chain.
Overall the results of this investigation have shown that by integrating the use of already existing tools, namely laboratory scale
Aluminosilicates MgO minerals Feldspars
Others Pentlandite Pyrrhotite Pyrite
Quartz
Chalcopyrite 83.0% by weight
Regrind 5 minutes
Fig. 10. Comparison of mineral composition of copper concentrates with and without regrinding of rougher concentrate.
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Aluminosilicates MgO minerals Feldspars
Others
Aluminosilicates
Others
MgO minerals Feldspars
Quartz
Quartz Ch
alc
Pentlandite 34.1% by weight
Chalc
opyrite
Pyrite
op
yrit
e
Pentlandite 42.6% by weight Pyrite
Pyrrhotite Pyrrhotite
No regrind
Regrind 5 minutes
Energy consumed in process stage per 100t of ore in concentrator feed (kWh)
Fig. 11. Comparison of mineral composition of nickel concentrates with and without regrinding of rougher concentrate.
6,000
5,000
4,000
3,000
2,000
1,000
0 Case 1
Case 2
No regrinding (P80 = 53 m)
Regrinding 5 minutes (P80 = 44 m)
Primary grinding Regrinding Copper smelting Nickel smelting Fig. 12. Plot showing the effect of using energy to regrind rougher concentrates on overall energy requirements across the concentrator–smelter process chain.
grinding and flotation tests, mineralogical analysis and thermodynamic models of smelting, a practical methodology can be developed for the optimisation of processing across both concentrator and smelter. This proof of concept work has shown that the Mill to Melt methodology can generate the quantitative information (in terms of liberation and flotation response of an ore) required to model energy usage across the concentrator–smelter process chain. Although the procedure investigated here has focussed on energy consumption the methodology could be readily adapted to consider other variables of interest including production costs, CO2 generation, penalty element pathways. Also, since the methodology carries information about the flow of particles in a range of size and composition classes it could be further adapted to link with other downstream process routes such as leaching, provided that appropriate models exist for these downstream processes. The evidence that the Ni–Cu sulphide ore used in this work obeys the heuristics both for particle composition being constant
at a given particle size in comminution and for flotation rate being constant for a given particle composition, indicates that there is scope to populate a model describing the relationship between recovery, grade (or enrichment ratio) and process energy requirements with fewer experiments than would otherwise be necessary. In this case the results from a small number of physical experiments and associated mineralogical analyses would calibrate the liberation and flotation responses of the ore and provide quantitative data for use in applying the heuristics. However, further work would be required to identify any limits beyond which the ore no longer obeys the heuristics; in these regions experimental data would be required to populate the model. Similarly, further work with a range of ores is required to determine whether the methodology is generally applicable to base metal sulphide ores and to identify any limitations of this approach. Quantitative mineralogical data for the ore and its grinding and flotation products are key information in the Mill to Melt methodology; the mineral composition data for the particles in each size fraction provide the required link between the process stages of comminution, flotation and smelting. The availability of the required particle composition data from automated mineral analysis systems such as the FEI MLA and QEMSCAN systems makes the Mill to Melt approach a practical methodology which can be applied to optimise existing and greenfield operations. 6. Conclusions The results of this proof of concept research have shown that the Mill to Melt methodology is a practical approach to modelling and optimising the utilisation of energy across the concentrator– smelter process chain. This approach combines laboratory comminution and flotation tests with mineralogical analyses of their products to link with models of downstream processes, in this case smelting. The use of particle composition information to link the various processes makes the Mill to Melt approach adaptable; for example, with further work the methodology could be adapted to optimise the process chain in terms of other variables of interest in sustainable processing such as arsenic rejection. Acknowledgements The authors would like to thank Xstrata Technology for commissioning and supporting this research and Xstrata Process Support for their technical contribution to the smelter modelling work.
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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng
Application of process mineralogy as a tool in sustainable processing C.L. Evans a,⇑, E.M. Wightman a, E.V. Manlapig a, B.L. Coulter b a b
The University of Queensland, Sustainable Minerals Institute, Julius Kruttschnitt Mineral Research Centre, Indooroopilly, QLD 4068, Australia Xstrata Technology, Level 4, 307 Queen St., Brisbane, QLD 4000, Australia
a r t i c l e
i n f o
Article history: Available online 9 April 2011 Keywords: Ore mineralogy Comminution Froth flotation Liberation
a b s t r a c t The observed behaviours of mineral particles in mineral processing operations have been exploited in the past to model comminution and concentration processes. In this work this concept has been taken a step further, exploiting the mineralogical characteristics of particles to link comminution, concentration and smelting. This approach is demonstrated using a laboratory-based case study of a Ni–Cu sulphide ore. The case study focused on the effect of shifting energy between the comminution and smelting stages on the overall energy consumption for the metal production process. To model this effect the mineral composition of the particles was linked to the behaviour of the ore particles in the primary grinding, regrinding and flotation stages. This application of process mineralogy provides a methodology to minimise energy use across mineral concentration and smelting processes, an important aspect of sustainable processing. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The increasing availability of automated mineralogy systems provides an opportunity for metallurgists to model mineral processing operations in terms of particles and their mineral composition. The models published in the literature fall into two categories – those based on fundamental properties of the mineral particles and those based on heuristics developed by observing the processing behaviours of ore particles. The fundamental modelling of mineral liberation in comminution is a research area which has exercised the minds of many researchers, starting with Gaudin in 1939 and continuing to the present day (Wiegel, 1976; Barbery and Leroux, 1988; King, 1979; Gay, 1994). Similarly the prediction of flotation behaviour from fundamentals is a continuing area of research with many branches of science including surface chemistry and electrochemistry contributing to the understanding of this complex process. While the fundamental research continues, the need for models which can be applied to simulate the effect of changes to liberation and the subsequent impact on flotation processes has led other researchers to develop models of these processes based on heuristics. Such models have been used in the past to optimise the process chain in terms of a variety of parameters for example mineral recovery (Bazin et al., 1994) and economic value (McIvor and Finch, 1991). With the recent focus on sustainability in mining and the drive for resources companies to minimise the energy use in energy⇑ Corresponding author. Address: JKMRC, 40 Isles Road, Indooroopilly, QLD 4068, Australia. Tel.: +61 7 3365 5888; fax: +61 7 3365 5999. E-mail address: [email protected] (C.L. Evans). 0892-6875/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.mineng.2011.03.017
intensive processes such as comminution and smelting, there is a need for tools which allow companies to optimise their process energy use. This research focuses on using quantitative mineralogy and heuristic models to identify where in the comminution–flotation–smelting process chain energy is most efficiently applied; in particular, this work can assist engineers in developing strategies to reduce overall energy consumption across the concentrator and smelter process chain by adding more regrinding energy to remove from the final concentrate minerals which are deleterious to the smelting process. Metallurgists recognise that there is scope to tune the operation of a concentrator to change the product quality and also scope to tune the smelter operation to smelt a different grade of concentrate. However, there is no integrated tool currently available which allows companies to model the combined effect of these changes on overall energy consumption in the mill and smelter. The methodology developed in this research, termed ‘‘Mill to Melt’’ provides such an integrated approach in which the linkage between the various processes is information on particle mineralogical composition. 2. Modelling liberation and flotation The methodology developed in this research exploits some heuristics which are based on the observed behaviours of ores in comminution and flotation. A range of authors, starting with Bérubé and Marchand in 1984, have observed that the amount of liberated mineral in a given size fraction is the same, regardless of where in the comminution circuit the sample is taken. The heuristics have been reported to be applicable to a range of ores including iron ore (Bérubé and Marchand, 1984), Mt Isa lead–zinc ore (Manlapig et al., 1985), a range of Australian copper ores and a lead–zinc ore
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(Wightman et al., 2008). More recently Evans and Wightman (2009) showed that the heuristic applied to both the sulphide and silicate gangue minerals in copper and lead-zinc ores which exhibited a range of ore textures, including massive, net and disseminated sulphides. Vizcarra et al. (2010) have also shown that the products of impact, and compression breakage show similar particle composition distributions at a given particle size, indicating that the heuristic applies regardless of the breakage mode used. Based on the observations in the literature the heuristic appears to be widely applicable and robust and therefore this approach was assessed as a potential means of modelling liberation at a range of grinding product sizes. For the flotation modelling a heuristic based on the relationship between flotation rate and/or mineral recovery and particle mineral composition was assessed. Earlier researchers starting with Imaizumi and Inoue in 1963 have observed that for a given particle size as the proportion of the target mineral in a particle increases the flotation rate of the particle increases. This behaviour has been reported for a range of ores including copper ores (Imaizumi and Inoue, 1963; Sutherland, 1989), lead-zinc ores (Steiner, 1973; Frew and Davey, 1993; Vianna, 2004) and iron ore (Bartlett and Mular, 1974). The form of the relationship between the flotation rate and the mineral composition of the particle at a given particle size varies from one ore to the next; for example in the flotation of haematite Bartlett and Mular (1974) observed an S-shaped relationship while in the flotation of galena Vianna (2004) observed a gradual increase in flotation rate with galena content of the particle with a much sharper increase in flotation rate when particle composition exceeded 70% galena. In the present work, if the flotation rate of a given size and composition class is constant under a fixed set of flotation conditions, this property can be exploited to model flotation recoveries for ore ground to different flotation feed size distributions both in rougher flotation and cleaner flotation. 3. Methodology 3.1. Ore characterisation The ore used for the case study was a sample of Ni–Cu sulphide ore from the Timmins region in Canada. The head assay of the ore sample is shown in Table 1 and the modal mineralogy of the ore measured using the FEI MLA system at JKMRC is presented in Fig. 1. The copper in the ore is present as chalcopyrite and the nickel is present both as pentlandite and in solid solution in pyrrhotite. Electron microprobe analysis of the pyrrhotite indicated that this mineral contained 0.35% Ni in solid solution; the resulting distribution of Ni across the Ni-bearing minerals in the ore was 94% of the total Ni present as pentlandite and 6% present in pyrrhotite. As well as providing information about the distribution of Ni across the two Ni-bearing minerals the mineralogical data also provide information about the distribution of Mg across a range of MgO-bearing minerals. This information is an important input to the smelter modelling since the amount of MgO in the smelter feed can affect the viscosity of the slag and the liquidus temperature. As discussed by Lotter et al. (2008), MgO-bearing minerals such as orthopyroxene can be recovered to the nickel concentrate by a range of mechanisms, namely by entrainment of fine liberated particles, by locking in composite particles, by inadvertent activation from ions in the slurry and by natural flotation. The presence
Others
Aluminosilicates
Pentlandite
Oxides
Pyrrhotite
Pyrite
Chalcopyrite Carbonates Fig. 1. Modal mineralogy of the ore.
of talcaceous minerals which show natural flotation behaviours can also further increase MgO recovery by entrainment, as these have a strong effect in increasing froth stability (Bradshaw et al., 2005; Kuan and Finch, 2010) which in turn increases water recovery to the froth and hence the amount of entrained material (Johnson, 1972). In this ore the distribution of MgO across the range of minerals present is as shown in Fig. 2, with chlorite, talc and amphibole being the main sources of MgO. Two of the MgO-bearing minerals – talc and biotite – may be recovered to the concentrate by flotation since earlier research has shown that these minerals exhibit natural flotation properties (Chander et al., 1975; Fuerstenau et al., 1988; Rath and Subramanian, 1998). As a result, talc and biotite are potential sources of elevated levels of MgO in the bulk Ni– Cu concentrate. Detailed knowledge of the ore mineralogy and its impact on the various processing stages, particularly in this case the Ni smelter, informs the decisions on which minerals must be tracked in this
Other Biotite Diopside
Amphibole
Table 1 Head assay of Ni–Cu sulphide ore. Element
Ni
Cu
Fe
SiO2
MgO
Al2O3
CaO
S
Assay (%)
1.18
0.67
18.9
42.6
4.17
12.3
6.1
10.6
Hornblende Fig. 2. Distribution of MgO across MgO-bearing minerals in the ore.
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methodology. For example, for this ore all minerals which contain Mg need to be tracked since, as Lotter et al. (2008) observe, ‘‘the total amount of MgO present in a concentrate is what drives the smelting problem, regardless of which mineral source’’. In general the key minerals which require tracking may vary depending on the ore and its chosen process route and would need to be identified on a case by case basis. 3.2. Laboratory scale grinding and flotation The laboratory grinding and flotation procedure for this ore, as shown in Fig. 3, consisted of primary grinding followed by a rougher flotation stage; the rougher concentrate was then subjected to either no regrinding, low level regrinding or medium level regrinding before a stage of cleaner flotation to generate a bulk Ni–Cu concentrate. In the operating plant from which the ore sample originated the ore was further separated into a Cu concentrate and Ni concentrate but in the laboratory-scale work discussed here the small masses of bulk Ni–Cu concentrate generated made this final separation stage impractical. In order to create nickel and copper concentrates for use in the smelter modelling, a theoretical separation was applied to the experimentally-generated bulk concentrate; the recoveries and grades applied in this theoretical separation were based on typical plant performance data for this ore. The primary grinding circuit used in the experimental work consisted of a laboratory stainless steel rod mill operated at 67% solids, with a power meter installed to measure energy requirements during grinding. The subsequent rougher flotation was performed in a bottom-driven 4.7 L JK batch cell with timed concentrates being collected at 0.5, 1, 2, 4, 8 and 16 minutes to provide kinetic information. In order to identify whether this ore follows any existing heuristics in its breakage/liberation and flotation behaviours, three primary grinding/rougher flotation tests were carried out at three different primary grinding product size distributions. The grinding and flotation products were analysed using the MLA systems at JKMRC to measure the particle composition distribution in each size fraction for comparison. In the second stage of the research three alternative process routes for upgrading the rougher concentrate from the intermediate primary grind test were investigated – one with no regrinding of the rougher concentrate prior to a cleaner flotation stage and a second and third process route in which the rougher concentrate was subjected to low and medium levels (respectively) of regrinding in a stirred mill prior to cleaner flotation. The laboratory scale,
Primary grind (Lab. rod mill) 1kg ore (100% -4mm)
stainless steel stirred mill used for this work was charged with 6 mm diameter glass grinding media and had a power meter installed to measure energy requirements during regrinding; nonreactive materials were used in the regrinding section to minimise the effect of regrinding on the pulp chemistry, allowing the work to focus on the physical attributes of the particles being processed. In the final stage of the experimental procedure the mineral composition of the final Ni and Cu concentrates was calculated by mathematically splitting the bulk Ni–Cu concentrate using grade and recovery values based on typical plant performance of this ore. The elemental composition of the Ni and Cu concentrates was calculated from the mineral data and the two sets of compositional data were input to the appropriate smelter models. The mineralogical composition of the final concentrate particles provides this information and is the link which integrates the concentrator and smelter modelling. The latter used models of the various processes within the smelter operation (including thermodynamic models of smelting) to calculate the energy requirements to generate blister copper from the Cu concentrate and nickel matte from the Ni concentrate. While details of the smelter model outputs are reported to confirm that the smelter models will link to the grinding and flotation models, no details of the proprietary smelter models are able to be provided here. 4. Results In the initial stage of the work of the ore was ground to three different product size distributions which are shown in Fig. 4. As can be seen, the 80% passing sizes (P80) of the three primary grinding products are 52, 81 and 105 lm. When the degree of liberation for pentlandite was analysed in the size fractions from the rod mill feed and the three rod mill grinding products, as shown in Fig. 5, it was clear that the ore follows the heuristic previously described in the literature. For size fractions below 75 lm, for this ore, the pentlandite liberation in a given size fraction does not vary significantly, regardless of whether the material comes from the rod mill feed or a finer or coarser primary grinding product. A similar pattern is observed for the pyrrhotite particle composition distribution across the full range of composition classes. This consistent behaviour indicates that the particle composition data from the rod mill feed could be used to predict the particle composition distribution for the grinding products. The ability to use the heuristic based on particle composition distribution
Rougher flotation (4.7L batch cell) Rougher tail
Rougher feed Bulk rougher conc. Regrind
Bulk cleaner Cleaner tail
(Laboratory stirred mill)
Bulk Ni/Cu conc.
Fig. 3. Experimental flow sheet.
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90%
90%
Cumulative weight % finer than size
100%
Cumulative weight % of sample finer than size
100%
80% 70% 60% 50% 40% 30% 20% Grind time = 25 min Grind time = 32 min Grind time = 42 min
10%
80% 70% 60% 50% 40% 30% 20% Regrind Feed Product 5 min regrind Product 10 min regrind
10% 0% 1
10
100
1000
Size ( m)
0% 1
10
100
1000
Size ( m)
Fig. 6. Particle size distributions of regrind mill feed and products.
Proportion of pentlaandite in liberated (90-100%) class (%)
Fig. 4. Particle size distributions of primary grinding products.
100 90 80 70 60 50 40 30 20 10 0 1
10
100
1000
Particle size (m) Rod mill feed
25 min grind product
32 min grind product
42 min grind product
Fig. 5. Comparison of pentlandite particle composition distribution data for rod mill feed and three primary grinding products.
Proportion of pyrrhotite in size and composition class (%)
being constant at a given particle size to model liberation would considerably reduce the amount of physical testing required to optimise the primary grind size in terms of overall energy consumption across the process chain. The heuristic also correctly describes the breakage/liberation behaviour in the regrind circuit. When the rougher concentrate is
subjected to regrinding at one of three levels – none, low or high – the minerals in the resulting cleaner feed stream obey the ‘‘rule of thumb’’. Over the range of cleaner feed size distributions studied, in which the P80 values were 57, 44 and 30 lm as shown in Fig. 6, the proportion of each particle composition class in each size fraction is essentially the same irrespective of the amount of breakage applied. The results of the mineralogical analysis for pentlandite, chalcopyrite and pyrrhotite in the regrind feed and products indicated that the particle composition distribution of these minerals in regrinding products could be adequately described from mineralogical analysis of the regrind feed and size by assay information for the regrind products. The distribution of pyrrhotite across the size and particle composition classes in the regrind feed and products is provided in Fig. 7 as an example of the behaviours observed follow the breakage/liberation heuristic. The flotation response of the ore was assessed for both the rougher and cleaner flotation stages to determine whether the flotation response followed the patterns identified by earlier researchers in which the flotation rate is a function of particle composition as well as particle size; an example of the flotation responses observed for the major sulphide minerals, in this case for liberated chalcopyrite, are shown in Figs. 8 and 9. Similar results were observed for pentlandite and pyrrhotite and for each of the composition classes (30–60% and 60–90% chalcopyrite). This characteristic behaviour allows the flotation rate values measured for a given composition and size class to be ap-
100
Regrind feed 90-100% PO Regrind feed 60-90% PO Regrind feed 0-30% PO 5 min regrind 90-100% PO 5 min regrind 60-90% PO 5 min regrind 0-30% PO 10 min regrind 90-100% PO 10 min regrind 60-90% PO 10 min regrind 0-30% PO
80 60 40 20 0
1
10
100
Particle size ( m) Fig. 7. Comparison of pentlandite particle composition distribution data for regrind mill feed and products.
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Flotation rate K (min-1)
10
1
0.1
0.01 1
10
1000
100
Particle size ( m) 25 min primary grinding product 32 min primary grinding product 42 min primary grinding product Fig. 8. Plot showing variation in flotation rate for liberated chalcopyrite (90–100% chalcopyrite class) particles in rougher flotation.
Flotation rate K (min-1)
10
1
0.1
0.01 1
10
100
1000
Particle size ( m) No regrind 5 min regrind product
5. Discussion
10 min regrind product Fig. 9. Plot showing variation in flotation rate for liberated chalcopyrite (90–100% chalcopyrite class) particles in cleaner flotation.
Others Aluminosilicates MgO minerals Feldspars
Pentlandite Pyrrhotite Pyrite
Quartz
Chalcopyrite 77.2% by weight
No regrind
plied to predict the flotation response of similar particles which derive from feed streams with a different overall size distributions. The cleaner flotation stage generated the bulk Ni–Cu concentrate which was separated mathematically into a final Cu concentrate and a final Ni concentrate. The mineral compositions of these final products for two of the regrind conditions investigated are compared in Figs. 10 and 11. As would be expected, the effect of regrinding was to allow a higher grade of concentrate to be produced at similar or higher recovery. In the case of the copper concentrate the chalcopyrite content increased from 77.2% to 83.0% mainly due to the rejection of pyrrhotite in the cleaner flotation. Comparing the nickel concentrates, the results showed that the pentlandite grade of the concentrate increased from 34.1% to 42.6% after regrinding, mainly due to the improved rejection of pyrrhotite and non-sulphide gangue minerals. The reduction of the MgO content of the nickel concentrate from 3.2% to 3.0% as a result of regrinding was not as great as anticipated. Inspection of the MLA data for the cleaner concentrate indicated that a large proportion of the talc (the major source of MgO in the concentrate) was highly liberated and could be rejected by identifying the main mechanism(s) of talc recovery (i.e. entrainment, activation by ions in solution or natural flotation (Lotter et al., 2008)) and developing an appropriate treatment route. Detailed optimisation studies aimed at reducing talc recovery were not part of the proof of concept investigations presented here but would form part of any detailed work in future. In the final stage of the methodology the mineral compositions of final copper and nickel concentrates and the elemental assays calculated from the mineral compositions, were used as inputs to the smelter models to calculate the energy requirements to produce blister copper and nickel matte from the respective concentrates. The overall results of the Mill to Melt methodology including the energy requirements estimated by the smelter modelling are summarised in Fig. 12 for two of the regrind conditions investigated (see Evans et al. (2009) for more details). The results of applying the methodology show that for this ore using a small amount of energy to regrind the rougher concentrate reduces the total energy requirements across the concentrator– smelter process chain.
Overall the results of this investigation have shown that by integrating the use of already existing tools, namely laboratory scale
Aluminosilicates MgO minerals Feldspars
Others Pentlandite Pyrrhotite Pyrite
Quartz
Chalcopyrite 83.0% by weight
Regrind 5 minutes
Fig. 10. Comparison of mineral composition of copper concentrates with and without regrinding of rougher concentrate.
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Aluminosilicates MgO minerals Feldspars
Others
Aluminosilicates
Others
MgO minerals Feldspars
Quartz
Quartz Ch
alc
Pentlandite 34.1% by weight
Chalc
opyrite
Pyrite
op
yrit
e
Pentlandite 42.6% by weight Pyrite
Pyrrhotite Pyrrhotite
No regrind
Regrind 5 minutes
Energy consumed in process stage per 100t of ore in concentrator feed (kWh)
Fig. 11. Comparison of mineral composition of nickel concentrates with and without regrinding of rougher concentrate.
6,000
5,000
4,000
3,000
2,000
1,000
0 Case 1
Case 2
No regrinding (P80 = 53 m)
Regrinding 5 minutes (P80 = 44 m)
Primary grinding Regrinding Copper smelting Nickel smelting Fig. 12. Plot showing the effect of using energy to regrind rougher concentrates on overall energy requirements across the concentrator–smelter process chain.
grinding and flotation tests, mineralogical analysis and thermodynamic models of smelting, a practical methodology can be developed for the optimisation of processing across both concentrator and smelter. This proof of concept work has shown that the Mill to Melt methodology can generate the quantitative information (in terms of liberation and flotation response of an ore) required to model energy usage across the concentrator–smelter process chain. Although the procedure investigated here has focussed on energy consumption the methodology could be readily adapted to consider other variables of interest including production costs, CO2 generation, penalty element pathways. Also, since the methodology carries information about the flow of particles in a range of size and composition classes it could be further adapted to link with other downstream process routes such as leaching, provided that appropriate models exist for these downstream processes. The evidence that the Ni–Cu sulphide ore used in this work obeys the heuristics both for particle composition being constant
at a given particle size in comminution and for flotation rate being constant for a given particle composition, indicates that there is scope to populate a model describing the relationship between recovery, grade (or enrichment ratio) and process energy requirements with fewer experiments than would otherwise be necessary. In this case the results from a small number of physical experiments and associated mineralogical analyses would calibrate the liberation and flotation responses of the ore and provide quantitative data for use in applying the heuristics. However, further work would be required to identify any limits beyond which the ore no longer obeys the heuristics; in these regions experimental data would be required to populate the model. Similarly, further work with a range of ores is required to determine whether the methodology is generally applicable to base metal sulphide ores and to identify any limitations of this approach. Quantitative mineralogical data for the ore and its grinding and flotation products are key information in the Mill to Melt methodology; the mineral composition data for the particles in each size fraction provide the required link between the process stages of comminution, flotation and smelting. The availability of the required particle composition data from automated mineral analysis systems such as the FEI MLA and QEMSCAN systems makes the Mill to Melt approach a practical methodology which can be applied to optimise existing and greenfield operations. 6. Conclusions The results of this proof of concept research have shown that the Mill to Melt methodology is a practical approach to modelling and optimising the utilisation of energy across the concentrator– smelter process chain. This approach combines laboratory comminution and flotation tests with mineralogical analyses of their products to link with models of downstream processes, in this case smelting. The use of particle composition information to link the various processes makes the Mill to Melt approach adaptable; for example, with further work the methodology could be adapted to optimise the process chain in terms of other variables of interest in sustainable processing such as arsenic rejection. Acknowledgements The authors would like to thank Xstrata Technology for commissioning and supporting this research and Xstrata Process Support for their technical contribution to the smelter modelling work.
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