Minerals Engineering 22 (2009) 821–825

Contents lists available at ScienceDirect

Minerals Engineering

journal homepage: www.elsevier .com/locate /mineng

The measurement of chalcopyrite content in rocks and slurriesusing magnetic resonance

Daniel Bennett a,*, David Miljak a, Joe Khachan b

a Commonwealth Science and Industrial Research Organisation (CSIRO), Division of Minerals, PMB 5, Menai, New South Wales 2234, Australiab Department of Physics, University of Sydney, New South Wales 2006, Australia

a r t i c l e i n f o

Article history:Received 19 September 2008Accepted 13 February 2009

Keywords:Sulphide oresOre mineralogySorting methodsProcess optimisationOnline analysis

0892-6875/$ - see front matter Crown Copyright � 2doi:10.1016/j.mineng.2009.02.008

* Corresponding author.E-mail address: Daniel.Bennett@csiro.au (D. Benne

a b s t r a c t

A prototype mineral analyser using magnetic resonance has been configured with two sensors for mea-surement of the copper sulphide mineral chalcopyrite (CuFeS2) in ores and slurries. In the first configu-ration, a sensor was optimised to quantify chalcopyrite content in rock fragments. The detection limit forthese measurements was estimated at 15 mg of contained chalcopyrite in rock fragments of mass rangeof 1.4–3.7 g. The magnetic resonance results correlated with separate quantitative X-ray diffraction mea-surements of chalcopyrite content. The second configuration was used to quantify chalcopyrite content inmineral slurries. Tests on prepared slurry samples with known chalcopyrite concentration were per-formed, yielding a standard error of 0.1 wt% across the sample range of 0.5–4.0 wt%. Measurements ona commercial mineral slurry were also performed. The design of an online industrial analyser is discussedwith reference to processing slurries with high solid loadings, containing phases such as chalcopyrite andother minerals.

Crown Copyright � 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction

The current shift towards automation in mining and processingis driven by a number of factors, such as general decline in oregrade, inhomogeneity of deposits, shortage of experienced plantoperators and limitations in representative assay throughput. Inresponse to these challenges it is likely that mining and mineralprocessing will become increasingly reliant on advanced sensingtechnologies for improved characterisation of ores and down-stream processing. Systematic measurements, integrated into min-ing and plant operations, could yield significant improvements inefficiency.

The mineralogy of a deposit influences the blending, stockpilingand operation of downstream processing. Using online measure-ments, a mine, flotation circuit and smelter may be adjusted fordifferences in feed mineralogy, mitigating the undesirable effectsassociated with the natural variability of the ore body. For exam-ple, in copper processing, the online monitoring of parameterssuch as oxide–sulphide ratio or the presence of excessive contam-inant phases such as arsenides would enable control strategies forincreased overall copper recovery or improved sustainability. Inaddition, improved recovery could be attained by early rejectionof problematic or subgrade ores, controlled through the onlineanalysis of mineralogy. Alternatively, a highly sensitive measure-ment of tailings material may provide a useful process monitoring

009 Published by Elsevier Ltd. All r

tt).

alarm. The potential efficiency improvements and process stabilityenabled through online mineral monitoring is of significant inter-est within the minerals industry.

There already exist well developed methods used by the miner-als industry for quantification of mineralogy. X-ray diffraction(XRD) and microprobe analysis have found widespread deploy-ment in mine planning, optimisation and longer-term process con-trol. These are techniques that provide a range of mineralogical,elemental and grain size parameters. However, owing to the com-paratively small sample sizes, these methods must also rely on rep-resentative sampling of ore streams and deposits. In many cases,the turn-around time for sample analysis may not be sufficientto enable process control. On the other hand, surface measure-ments involving optical sensors are extremely rapid, but may suf-fer from surface bias effects. Ideally an online instrument wouldmeasure the whole process stream, have a short measurementtime, achieve required resolution and detection criteria and be reli-able and easy to calibrate.

The magnetic resonance method described here has several po-tential advantages that may meet many of these industrial require-ments. The method quantifies mineral phases via excitation ofspecific radio frequency (RF) resonances using non-contact sen-sors. It can be scaled to volumes of millilitres to litres, is insensitiveto surface conditions and is suited to online application as rela-tively low concentration of specific minerals can be determinedwithin several minutes. It can be applied to ores or slurries withoutthe need for any sample preparation. However, only selected min-erals can be measured by the technique. The deployment of an

ights reserved.

822 D. Bennett et al. / Minerals Engineering 22 (2009) 821–825

analyser based on magnetic resonance would therefore be suitedto applications requiring online analysis of only a handful of keymineralogical species most critical to a process.

This paper describes two experiments that were performed toinvestigate the resolution and detection limits of the techniquefor chalcopyrite, in rock fragments and in slurries. Quantitativemeasurements in ores and slurries have been successfully demon-strated. As far as the authors are aware, this is the first report ofsuccessful magnetic resonance measurement of a mineral in acommercial slurry. The work was also performed with an ultimateaim of developing high resolution online instrumentation for min-eral measurement, suitable for online process control.

2. Magnetic resonance technique

Magnetic resonance techniques involve the excitation anddetection of RF spectral lines. There are many classes of magneticresonance. Nuclear magnetic resonance (NMR) is by far the mostwidely employed method across multiple disciplines, finding useas an analytical tool for the study of bonding and lattice dynamics.In conventional NMR, static magnetic fields are applied to testmaterials to set up RF resonances associated with specific targetnuclei. For example, NMR on protons has been used in boreholeoil exploration to infer rock porosity and other textural qualities(Jackson et al., 1980; Lipsicas et al., 1986). There exist several otherclasses of magnetic resonance that are highly discriminating ofmineralogy, including quadrupole resonance (QR) and zero fieldnuclear magnetic resonance (ZFNMR) (Abragam, 1961). These clas-ses do not require the application of an external static magneticfield. The absence of this field simplifies the practical deploymentof an analyser by avoiding the complexity associated with the gen-eration and control of a static magnetic field.

QR occurs for nuclei with nuclear spin I > ½ and where thebonding around the nucleus has non-spherical symmetry (Dasand Hahn, 1958). Both natural isotopes 63Cu and 65Cu have I = 3/2,and in most copper minerals the crystalline structure establishesthe conditions for quadrupole resonance. For example, distinct res-onances widely separated in frequency occur for the oxides cuprite(Cu2O) and tenorite (CuO), and the sulphide covellite (CuS). Insome cases copper need not be measured directly, as other nucleiin the mineral can be targeted. For example, some copper arsenidesand copper antimonides are measurable through arsenic and anti-mony quadrupole resonances.

ZFNMR occurs in certain copper–iron minerals, where intensemagnetic fields are present at iron nuclei due to internal magneticordering. Weaker magnetic fields are imposed on adjacent coppernuclei. Copper NMR transitions may therefore be detected becauseof the local fields, without application of an external field (hencethe description ‘‘Zero Field NMR”). The internal fields and the tran-sition frequencies are sensitive to the mineral structure and mag-netic order. This aspect of the resonances provides the requiredmineral discrimination. Examples of magnetically ordered copperminerals include the antiferromagnets chalcopyrite (CuFeS2) andcubanite (CuFe2S3). Chalcopyrite has a six line spectrum at roomtemperature, the most intense line occurring at 18.46 MHz (Abdul-lin et al., 1987). The work described in this paper focuses onexploiting this resonance for quantitative measurement of chalco-pyrite, with particular emphasis on the measurement of rocks andslurries.

3. CSIRO instrumentation

CSIRO has developed instrumentation for mineralogical investi-gation (Bennett et al., 2007). The analyser consists of a radio fre-quency transmitter that can be modulated to provide RF

excitation pulses, a resonant sample sensor and receiver electron-ics for signal detection. The sequence to acquire magnetic reso-nance signals and sensor configuration is shown in Fig. 1.

The analyser and its electronics can be used with different sen-sor configurations. For the rock measurements, a 1 mL solenoid coilwas fabricated to match the typical rock size, while a differentsolenoid configuration was used in the slurry tests. In each case,the sample was excited by pulsed RF fields inside the volume ofthe sensor, and the resonant response of the sample is subse-quently measured by the same sensor. The Hahn echo excitationsequence was used to excite the resonances. This sequence consistsof two RF pulses at the resonant frequency, the second pulse twicethe duration of the first, with a separation of approximately 200 lsbetween pulses. For each of the experiments, the pulse durationswere optimised for sample size and measurement time.

For a given pulse sequence and system gain, the magnitude ofthe echo signal is proportional to the number of resonant nucleiwithin the target mineral. Therefore the strength of response at18.46 MHz can be used to quantify the concentration of chalcopy-rite. A typical echo signal corresponding to a sample containingapproximately 79 mg of chalcopyrite is shown in Fig. 2. For quan-titative determination of mineral concentration, the absolute re-sponse of the analyser must be carefully controlled acrossdifferent sample loading or sample presentations (Shultz and Karr,1969). This is in contrast to many analytical NMR measurements,where the absolute value of received signal may not be of particu-lar significance. Several techniques have been applied to obtainspectrometer stability, including the careful selection of sensorbandwidth, tuning method and monitoring of RF amplifier powerand sensor current. These measurements can be used to correctfor any variation due to sample electrical loading. The signal tonoise ratio was improved by averaging multiple Hahn echo se-quences. The resulting average magnitude was corrected for thepresence of thermal sensor noise (Koay and Basser, 2006).

4. Results

4.1. Bulk chalcopyrite estimation in rocks

To demonstrate analyser capability for bulk analysis, the massof chalcopyrite was measured in fourteen rock fragments. Thesamples were supplied by the Julius Kruttschnitt Mineral ResearchCentre (JKMRC) from a copper mine located in New South Wales,Australia. The mineralisation predominantly consists of plagioclaseand orthoclase feldspars with minor amounts of hornblende,quartz and other minerals (Holliday et al., 2002). The predominantsulphide minerals are bornite, chalcopyrite and pyrite. Six of therocks were selected from sub 10 mm ore feeding a semi-autoge-nous grinding mill and the remaining eight rocks were obtainedfrom recycled mill feed. Three of the samples studied are shownin Fig. 3.

Generally, the magnetic resonance technique benefits from lar-ger sample size. If sensor filling factor and RF field strength is keptconstant, the sensitivity increases as the square root of sample vol-ume (Hoult and Richards, 1976). For the spectrometer used, the opti-mum volume is over 200 mL. Therefore, the size of rocks in theavailable sample set is non-optimal, leading to the use of increasedsignal integration times. For these measurements a smaller sensorwas specially fabricated to improve the filling factor and the pulsesequence was optimised to increase the detection limit. Each ofthe fourteen rock fragments were subjected to analyser measure-ment. A characterised standard (76 mg mass, >90% natural chalcopy-rite) was used to calibrate for absolute chalcopyrite concentration.After measurement in the solenoid (10,000 acquisitions over 1000s), the samples were crushed and subjected to quantitative XRD

Fig. 1. (Top) RF pulses were applied to samples to generate resonances in the material. (Bottom) The two sensor configurations for rock fragment and slurry measurements.

Fig. 2. An interference – free Hanh echo observed from a sample containing 79 mgof chalcopyrite. The magnitude of the signal is proportional to the chalcopyriteconcentration.

Fig. 3. Three rock fragments of ore analysed for chalcopyrite content. Each rock wasapproximately 1 mL in volume. Small intrusions of mineralisation were visuallyidentified by their gold lustre on several of the rock surfaces.

Fig. 4. Measured chalcopyrite content of individual rock specimens obtained frommagnetic resonance and quantitative XRD measurements.

D. Bennett et al. / Minerals Engineering 22 (2009) 821–825 823

analysis at the laboratories in the Faculty of Science, QueenslandUniversity of Technology.

An estimate of contained chalcopyrite for each sample is shownin Fig. 4, obtained from magnetic resonance and XRD analysis. Thecorrelation coefficient was 0.97 for the two methods. Despite thesmall rock sizes, it was still possible to detect chalcopyrite concen-trations using the magnetic resonance method. Due to therestricted size of the reference sample in these experiments allchalcopyrite mass and weight percentage values have a commoncalibration scaling uncertainty of approximately 7%.

It is noteworthy that the largest relative discrepancy betweenthe two measurements occurs at low chalcopyrite mass below100 mg, implying that detection limits of each method maybecome important in this range. For the magnetic resonance data,the chalcopyrite mass resolution was estimated at 7 mg, and detec-tion limit at 15 mg. These values were obtained by analysis of sig-nal to noise ratio on a measurement of the 76 mg standard. Asnoted in previous studies (Bennett et al., 2007), the magnetic res-onance resolution and detection limits are dominated by thermalnoise, rather than interference from other phases. Interference-freedetection limits are certainly expected in this study, where otherknown or probable co-crystallised phases are restricted to mineralsthat provide null signal in the analyser at 18.46 MHz. The domi-nance of thermal noise is significant in that it provides the oppor-tunity of enhancing signal to noise ratio through increased signalaveraging. A parallel analysis of XRD resolution and detection lim-its was not performed here, but a future study with greater empha-sis on checking correlations at lower chalcopyrite contents isplanned.

As a consistency check on the magnetic resonance measure-ment procedure, the eight mill fragments were combined intoone composite sample and remeasured in a larger sensor, using adifferent calibration standard. This procedure yielded a compositechalcopyrite mass of 0.66 g, compared to 0.69 g for the sum of indi-vidual rock measurements. The agreement of the two procedures(within calibration scale factors and resolution) demonstrates

Fig. 5. Measurements of the analyser predicted concentration versus simulatedslurry samples of chalcopyrite diluted in silica (38 wt% solids loading) for the range0.5–4.0 wt% for 60 s measurement time. Two independent measurements weremade for each sample as shown.

824 D. Bennett et al. / Minerals Engineering 22 (2009) 821–825

internal consistency of the procedure across two differentconfigurations.

These results demonstrate the unique capability of the mag-netic resonance technique for non-destructive and direct quantifi-cation of bulk mineral content in samples of ore. The results alsoshow promise for the potential development of field deployableinstruments based on magnetic resonance, perhaps for drill chip,mine face or borehole measurement. While the measurements re-ported here only relate to chalcopyrite content, other copper min-erals such as tenorite, cubanite and covellite are also expected toyield similar sensitivity. This initial study was limited to very smallsensor volume, but measurements are described in the next sec-tion relating to larger volumes, with comparatively fast measure-ment integration times.

4.2. Simulated slurry experiment

A second experiment was performed to determine analyser per-formance on slurries. A test rig simulating the presentation of slur-ry sample was constructed for the measurements. The sensor was asolenoid positioned around an electrically insulating pipe,mounted horizontally. Plastic bottles (diameter 75 mm, volume500 mL) containing slurry were inserted inside the pipe, and with-in the volume enclosed by the solenoid. In order to mimic the mo-tion of solids in actual slurries, the containers were coupled to anelectric motor spinning at 1 Hz. At this spinning rate significantsolids collection at the bottom of the containers was avoided.

Eight synthetic slurry samples were prepared from chalcopyriteconcentrate (of approximately 85% purity and less than 100 lmparticle size) obtained from the Cobar District, New South Wales,Australia. Commercial silica (less than 100 lm particle size) wasused as a dilutant and the samples were mixed to represent a rangeof chalcopyrite concentrations from 0.5 to 4.0 wt% with a constantsolids loading of 38 wt%. Tap water was added to suspend the mix-ture of chalcopyrite and silica. An approximate volume of 50 mL ofair remained in the bottles. Before commencement of each mea-surement, each bottle of slurry was shaken vigorously for severalminutes and rotated in the instrumentation for a further 5 min.For each measurement, RF excitation was chosen to optimise mea-surement time. The signal to noise ratio was improved by averag-ing each pulse sequence 6000 times (each sequence having aperiod of 10 ms, representing a signal integration time of 60 s).For each sample the averaged echo magnitude was recorded. Eachintegrated measurement was repeated in order to gauge measure-ment reproducibility.

Since the samples contain a known amount of chalcopyrite, alinear regression between echo magnitude and chalcopyrite con-centration can provide a measure of the accuracy and detectionlimit for this configuration. The results are presented in Fig. 5.The variation between measurements of the same sample is pre-dominantly due to incoherent thermal noise in the sample coiland spectrometer preamplifier. For the time interval used, thenoise contributes to the uncertainty at each of the sample concen-trations, thus setting the lower limit on resolution. The resultsshow a good correlation between signal magnitude and chalcopy-rite concentration. The standard error of the regression is 0.1 wt%with a detection limit of approximately 0.3 wt%. Since the noiseis thermal in origin, noise reduction is expected to vary as thesquare root of measurement integration time. This was verifiedby successive measurements with different integration times.Therefore, a 4 min averaging time provides a factor of two increasein resolution and a reduction in detection limit quoted above. Mea-surement integration times of this order would normally be com-patible with online requirements.

To test the analyser on commercial process slurries, a slurrysample containing chalcopyrite was obtained from the Mount Isa

Cloncurry District, Australia and comparative measurements madein the test instrument on a bottled 500 mL sub sample having 38wt% solids loading and pH 7.3. A chalcopyrite signal was observed.Based on comparison to the calibration data obtained with syn-thetic slurries, a mineral content of 2 wt% was inferred, whichwas consistent with plant operating assays obtained at the timeof collection. The flotation agents added to the water had a negli-gible affect on the measurement. The gangue materials such asquartz, feldspars and clays did not affect the quantitative determi-nation of the target species.

The deployment of a slurry analyser based on the configurationdescribed above requires a short section of electrically non-con-ducting pipe. Electrically insulating materials are required to allowRF fields to penetrate into the measurement zone. In this respect,deployment of an analyser would require modification of existingpipe work. However the modification, either in the form of a by-line or as a modified main line section, should be possible in manycircumstances. It may also be possible for sensors to be positionedin or above flotation cells, or submerged in tanks that accept met-allurgical test samples, to monitor mineralogical mix of key spe-cies. In both cases the analyser would require correction forslurry density variations to determine weight fraction. This couldbe provided by standard density gauges. Variability in solids load-ing is not expected to have a significant impact on the magneticresonance measurements.

Magnetic resonance detection of bulk mineralogy in slurries of-fers several potential advantages, such as true bulk material anal-ysis on large volume samples over a short period. Higherresolutions could be achieved with an increase in acquisition timeand larger sample volume (only limited by RF engineering). Poten-tially, the method could yield mineral detection at very low levelsfound in tailings. These developments towards a working proto-type magnetic resonance slurry analyser for key minerals arecontinuing, with development of an industrial analyser plannedfor the future.

5. Conclusion

The experiments reported here demonstrate that magnetic res-onance measurements on rock samples of ore are straight forward,requiring no sample preparation. In a laboratory environment,measurement of chalcopyrite mass better than 15 mg is achievableon volumes larger that 1 mL. In a simulated slurry experiment,measurements performed on a number of synthetic slurry samplesdemonstrated a robust response, with accuracies and detectionlimits useful for online monitoring of process streams. Initial

D. Bennett et al. / Minerals Engineering 22 (2009) 821–825 825

measurements on industry process slurry proved encouraging foronline deployment.

Acknowledgements

The authors acknowledge Zeljka Pokrajcic from the JKMRC forloaning the rock fragments and performing the quantitative XRDmeasurements.

References

Abdullin, R.S., Kal’chev, V.P., Pen’kov, I.N., 1987. Investigation of copper minerals byNQR: crystallochemistry electronic structure lattice dynamics. Physics andChemistry of Minerals 14, 258–263.

Abragam, A., 1961. The Principles of Nuclear Magnetism. Oxford University Press,London.

Bennett, D., Miljak, D., Khachan, J., 2007. Quantitative measurement of coppermineralogy using magnetic resonance. Minerals Engineering 20, 1344–1350.

Das, T.P., Hahn, E.L., 1958. Nuclear Quadrupole Resonance Spectroscopy,Supplement 1 of Solid State Physics Advances in Research and Application.Academic Press Inc., New York.

Holliday, J.R., Wilson, A.J., Blevin, P.L., Tedder, I.J., Dunham, P.D., Pfitzner, M., 2002.Porphyry gold–copper mineralisation in the Cadia district, eastern Lachlan FoldBelt, New South Wales, and its relationship to shoshonitic magmatism.Mineralium Deposita 37, 100–116.

Hoult, D.I., Richards, R.E., 1976. The signal to noise ratio of the nuclear magneticresonance experiment. Journal of Magnetic Resonance 24, 71–85.

Jackson, J.A., Burnett, L.J., Harmon, J.F., 1980. Remote (inside–out) NMR III. Detectionof nuclear magnetic resonance in a remotely produced region of homogeneousmagnetic field. Journal of Magnetic Resonance 41, 411–421.

Koay, C.G., Basser, P.J., 2006. Analytically exact correction scheme for signalextraction from noisy magnitude MR signals. Journal of Magnetic Resonance179, 317–322.

Lipsicas, M., Banavar, J.R., Willemsen, J., 1986. Surface relaxation and pore size inrocks – a nuclear magnetic resonance analysis. Applied Physics Letter 48, 1544–1546.

Shultz, H.D., Karr Jr., C., 1969. Quantitative aspects of nuclear quadrupole resonancespectrometry of inorganics and minerals. Analytical Chemistry 41 (4), 661–664.