University of Nevada, Reno
The Prediction of Gold Recovery by Carbon-in-Leach Cyanidation using Visible Near-infrared (VNIR) Spectroscopy of Pulverized Ore Samples from the Cortez Hills Underground Mine
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Metallurgical Engineering
by
Jeffrey Olson
Dr. Thom Seal/Thesis Advisor
December 2016
THE GRADUATE SCHOOL
We recommend that the thesis prepared under our supervision by
JEFFREY L. OLSON
Entitled
The Prediction of Gold Recovery by Carbon-in-Leach Cyanidation using Visible Near-infrared (VNIR) Spectroscopy of Pulverized Ore Samples from the Cortez Hills Underground Mine
be accepted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
Thom Seal, Ph.D., Advisor
Charles K. Kocsis, Ph.D., Committee Member
John L. Muntean, Ph.D., Graduate School Representative
David W. Zeh, Ph.D., Dean, Graduate School
December 2016
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ABSTRACT
A semi-quantitative model was calibrated to predict gold recovery by carbon-in-leach
(CIL) cyanidation using visible near-infrared (VNIR) spectroscopy of pulverized ore
samples. A model was calibrated using the Cubist rule and instance based regression algorithm on reflectance spectra transformed to the standard normal variate of apparent absorption ( SNV( Log(1/r) ) and reduced to eight components using principal component analysis (PCA). This model, calibrated to the entire dataset has an R-squared value of
0.976 and a root mean squared error of calibration (RMSEC) of 4.87%. Bootstrap validation of the model estimates an R-squared value of 0.813 and a root mean squared error (RMSE) of 13.45% for the population of samples represented by this dataset.
Interpretation of the calibrated model suggests that spectral features of iron oxide minerals have a strong influence on gold recovery.
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DEDICATION
To my wife who did not let me quit every time I tried to and to my three children who did not see the beginning of this but will soon see the end.
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ACKNOWLEDGEMENTS
I wish to thank all of the current and former employees of Barrick Gold whom have provided so much support and encouragement for this project, especially Tony Carroll,
Emrah Yalcin, Jon Kamensky, Steve Cashin, Xiaodong Zhou, and Jack McPartland.
I also wish to thank McClelland Labs and Barrick Gold for the financial support that they have provided as I have progressed in my degree program through the last few years.
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TABLE OF CONTENTS
ABSTRACT ...... i
DEDICATION ...... ii
ACKNOWLEDGEMENTS ...... iii
TABLE OF CONTENTS ...... iv
LIST OF TABLES ...... v
LIST OF FIGURES ...... vi
1 BACKGROUND AND INTRODUCTION ...... 1
2 LITERATURE REVIEW ...... 8 2.1 Reflectance Theory ...... 9 2.2 Absorption Mechanisms ...... 16 2.3 Quantitative Analysis ...... 24 2.4 Conclusions – Literature Review ...... 36
3 EXPERIMENTAL ...... 37 3.1 Sample Description ...... 37 3.2 Near Infrared Spectra Collection ...... 37 3.3 Bench Top Carbon-in-Leach (CIL) Tests ...... 39
4 DATA ANALYSIS AND RESULTS ...... 41 4.1 Impact of Spectrometer ...... 41 4.2 Semi-Quantitative Modeling of Carbon-in-Leach Gold Recovery ...... 44
5 CONCLUSIONS...... 55
6 RECOMMENDATIONS ...... 59
7 BIBLIOGRAPHY ...... 61
APPENDIX ...... 68 Section 1 – Description of the Characteristic Assay Methods ...... 69 Section 2 – Descriptive and Characteristic Data ...... 70 Section 3 – R Script – PLS-DA for Instrument Identification ...... 72 Section 4 – R-Script – Training Loop for PLS Models ...... 74 Section 5 – R-Script – Training Loop for PCR-Cubist Models ...... 81 Section 6 – Training Figures for PLS Models ...... 93 Section 7 - Training Figures for PCR – Cubist Models ...... 99 Section 8 – VNIR Spectral Data ...... 107
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LIST OF TABLES
Table 1 – Sample Descriptions and Testing Conditions ...... 39
Table 2 – Results of discriminant models for identifying the instrument used to measure the spectra ...... 43
Table 3 – Results from model search and parameter training ...... 49
Table 4 – Variable usage for the best fit model...... 51
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LIST OF FIGURES
Figure 1 – Location Map (Bergen 2012) ...... 2
Figure 2 – Schematic of Underground Deposits and Workings (Bergen 2012) ...... 3
Figure 3 – A spectra collected using a PANalytical TerraSpec® 4 unit across the VNIR range...... 8
Figure 4 – Cross sectional diagram of a powder layer illustrating the basic terms in Kubelka-Munk reflectance theory. Torrent and Barron (2008) ...... 10
Figure 5 – Geometric arrangement and terms in the Hapke bidirectional reflectance model...... 12
Figure 6 – Conceptual illustration of forward, isotropic, and backward scattering...... 14
Figure 7 - Absorption features due to the excitation of d-orbital electrons of ferrous iron in bronzite [(Mg,Fe)SiO3] and olivine [(Mg,Fe)2SiO4] ...... 17
Figure 8 – Monazite (Miguel County, NM) with chemical grade neodymium, samarium, and praseodynium oxides...... 18
Figure 9 – Reflectance spectra for cinnabar (Manhattan, NV), sulfur (reagent grade), and goethite (Kent, CT)...... 20
Figure 10 – Conceptual illustration of energy absorption by vibration (Kasap 2006). .... 21
Figure 11 – Reflectance spectra for Calcite (Alligator Ridge mine, NV) and Dolomite (Grapevine Mountains, NV)...... 23
Figure 12 – Graphic Representation of Principal Component Analysis...... 30
Figure 13 – A data cloud with the first and second principal components...... 32
Figure 14 – Example of a “committee” model produced by the Cubist algorithm...... 35
Figure 15 – Drill core intervals sampled from the Breccia (cyan), Middle (blue), Lower (purple), Upper Breccia (red), and Deep South (green) Zones of the Cortez Hills underground mine...... 38
Figure 16 – A Terraspec 4 Hi-Res with a contact probe (left) and a ...... 38
Figure 17 – Reflectance spectra for belt cut composites and Deep South samples...... 40
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Figure 18 – Reflectance Spectra for Upper Breccia, Breccia, Middle Zone, and Lower Zone samples...... 40
Figure 19 – Spectra collected using a hand held TerraSpec Halo (blue) or with a benchtop ...... 43
Figure 20 – Box and whisker plots illustrating the bias between sample sets analyzed using the hand held Halo instrument or the benchtop TerraSpec 4 instrument...... 44
Figure 21 – Measured versus predicted for the calibrated model and the out-of-bag bootstrap samples...... 50
Figure 22 – Principal component scores for PC’s 1, 3, and 8. Of the 8 components used in the model, these three three components were selected most often for rules to segregate the samples...... 51
Figure 23 – The orignal reflectance spectra, the pre-treated spectra, and the loadings for PC’s 1, 3, and 8...... 52
Figure 24 – The impact of fitting parameters to RMSE of out-of-bag bootstrap samples...... 53
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1 BACKGROUND AND INTRODUCTION
The Cortez Gold Project is located in the Great Basin region of Northeastern Nevada, 80 miles southwest of Elko, Nevada, USA. The discovery of the Cortez Hills complex began in 1998 with the identification of the alluvial Pediment deposit. Follow-up drilling in the area identified the nearby Cortez Hills deposit in 2002 and then the Middle and
Lower zones in 2005. Development of concurrent open pit and underground mines began in 2008.
The geology of the gold deposit is a “Carlin” style sedimentary rock hosted and porphyry/epithermal deposits. Deposits of this type are formed along fractured geologic structures that allowed mineralizing solutions to contact favorable limestone host rocks, depositing gold as fine micrometer-sized gold grains and solid solutions in arsenian pyrites. These same structures allowed fluvial solutions to flow through the deposits, oxidizing pyrites and altering the carbonate host rocks. Changes to the structures between mineralization and oxidation events created complex channels that allowed for significant oxidation at depth and intermingling of unaltered (refractory) and oxide ores.
(Bergen 2012)
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Figure 1 – Location Map (Bergen 2012)
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Figure 2 – Schematic of Underground Deposits and Workings (Bergen 2012)
When considering the economics of an entire deposit, the variability of metallurgical
recovery is sometimes not too important as long as the assumed value(s) is close to the
average expected for the life of the mine. Recovery will vary up and down but the
economics for the life of the project will remain approximately the same. However,
variability during production becomes much more important as it may mean the
difference between positive or negative cash flow in a given year or quarter. An
improved understanding of the metallurgical variability in a period of weeks or months, or within 25 feet, allows for accurate short range forecasts, better assessments of
4 individual levels or headings, and the option to adjust mining to maximize the net- present-value of a deposit.
Bench-top bottle roll testing for carbon-in-leach (CIL) cyanidation is a good predictor for gold recovery in a full scale CIL circuit. The amount of sample required, cost, and duration of a test limit the number of tests that are completed during evaluations and mining. If visible-near-infrared spectroscopy can be used to estimate the gold recovery from a CIL bench-top test, then a non-destructive test taking only two minutes can be used to generate information only otherwise available from a test requiring at least 200 grams of sample and taking 4 days to complete. Spectra could be collected from ore control samples or every sampled interval of exploration core with little extra cost providing a detailed description of variability from heading to heading and throughout the entire deposit.
Near infrared mineral analysis is a laboratory technique that was developed for aerial geologic surveys that could be conducted from aircraft or satellites. The technique relies on the interactions between specific wavelengths of light and the chemical bonds in the minerals to produce a signal. What can be observed in the spectra are functions of refraction, scattering, and absorption. These interactions between light and minerals are directly related to structure and composition on a molecular scale. Many studies have been published to demonstrate that signals from specific minerals can be identified in the visible near-infrared (VNIR) spectra, including at least one study regarding the identification of clays at the Cortez mine. The mineral signals that are important for this
5 study include iron oxides, carbonate, and alteration clays (smectite, montmorillinite).
(Gladwell 1985, Hauff)
One of the principal challenges of VNIR analysis is interpretation. Spectral “dips” or adsorption peaks are often weak, broad, and overlapping while, at the same time, particle size impacts the entire spectral range. These effects have limited the usefulness of NIR because almost every application requires a unique approach in order to correctly interpret the signals and provide the information desired. In a large part, the successful applications all depend on the use of empirical modeling techniques.
It is useful to think of how an empirical, or soft, model contrasts with a fundamental, or hard, model. Suppose a set of n samples, each with m measurements and y observations.
In order to quantitatively describe the relationship between m measurements and y observations, a hard model or a soft model will be used. Hard models depend on prior knowledge of the relationship between the measurements and observations, whereas soft models require no prior knowledge of the relationship. An example from chemistry would be a model to predict oxygen consumption for different fuel mixes. Assume a dataset containing the percentage of gases A to D (m = 4) for 12 mixes (n = 12) that were burned to measure the oxygen consumption (y = 12) for each mix. A hard model for predicting the oxygen consumption could be made using prior knowledge of the chemical composition of each gas and calculating the oxygen consumption using the stoichiometric ratios for each combustion reaction. A soft model could be made using a multivariate regression without any prior knowledge or assumptions of the combustion reactions.
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It is often difficult to use a hard model to describe the complexities of a real system so a good model is often a combination of both approaches. In engineering it is common to see these soft modeling adjustments presented as efficiency, safety, or error factors. In contrast, if the fundamental relationships between the measurements and observations are ignored, a soft modeling approach may be built on measurements that have only a coincidental relationship to the observations such as the time of day of an experiment to oxygen consumption or the zip code to the strength of structural steel. It is easy to imagine that a series of experiments could be planned in a day so that coincidentally the oxygen consumption would appear to increase later in the day, or samples collected for a steel study which coincidentally varied in strength by zip code as the local requirements, preferences, or suppliers varied in different parts of the country.
A good soft model will be informed with knowledge of which measurements are likely to cause the variation in the observation. This means that a soft model should use measurements that are fundamentally related to the observations that are being predicted.
A soft model, combined with some fundamental understanding, allows the exact relationship to be estimated without specific knowledge of the nature of the relationship.
The goal of this study is to predict the percentage of contained gold that can be extracted by carbon-in-leach (CIL) cyanidation using visible-near-infrared spectral data collected from samples that are already produced for fire assays from the Cortez Hills
Underground mine. This will be done by investigating the physics and chemistry that
7 influence a spectral signal and then by applying modern empirical modeling techniques that can be used to approximate the relationship between the spectral signal and gold recovery.
Visible near-infrared spectroscopy has a history of use in various industrial and geological applications. These applications often rely on soft models that identify or quantify certain minerals or chemicals. The assumption of this project is that the VNIR signal is directly related to the mineral types and that these mineral types are related to the percentage of recoverable gold. An empirical model to estimate gold recovery directly from the VNIR signal will combine these two relationships. This has an advantage of being able to identify variability important for recovery that might otherwise be filtered out in a mineral identification. As will be discussed later, VNIR is sensitive to mineral composition and crystal structure. A mineral identification model may be able to identify and quantify goethite, however the variety geothites present with differing amounts of arsenic may be lost in that filter. The disadvantage is that the empirical model becomes more abstract and complex making interpretation more difficult.
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2 LITERATURE REVIEW
Reflectance spectroscopy is the analysis of light radiation that is reflected from a sample.
The wavelength range selected for study in remote sensing and spectral geology applications will vary by the capabilities of different spectrophotometers, but generally near-infrared spectroscopy will focus on ranges extending across the visible and infrared spectra. This project relies on spectral data collected from PANalytical-ASD units which collect data for wavelengths from 350 to 2500 nm. For simplicity, this range will be referred to as the visible-near-infrared, or VNIR, range. Figure 3 illustrates a reflectance spectra collected from a PANalytical TerraSpec® 4 laboratory unit as it compares to common definitions of spectral ranges used in physics.
Relative Reflectance VNIR UV Visible NIR 0.0 0.2 0.4 0.6 0.8 1.0 0 500 1000 1500 2000 2500 3000
Wavelength, nm
Figure 3 – A spectra collected using a PANalytical TerraSpec® 4 unit across the VNIR range.
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Reflectance represents the intensity of light received by the detector versus the intensity of light provided by the source for each wavelength. Laboratory detectors are calibrated using a white reference. The spectra plotted in Figure 3 was calibrated using a fluoropolymer, spectralon. Spectralon is a registered trademark owned and produced by
Labsphere. Although it has a strong absorbance at 2800 nm, absolute reflectance is generally greater than 99% between 350 and 2500 nm (Labsphere, “Technical Guide:
Reflectance Materials and Coatings”). The calibration will record the reflectance from a spectralon standard and define that digital signal as 100% reflectance. Measurements following that calibration will be reported relative to that white standard measurement.
2.1 Reflectance Theory
In a physical sense, relative reflectance for a mineral sample represents the probability that a photon at a specific wavelength will not be scattered or absorbed and reflected toward a detector after it encounters a sample. The Kulbelka and Munk (1931) or KM theory describes reflectance as radiation of a single wavelength which is passing through a material of thickness X. As radiation of intensity I is propagating downward (-x direction) scattered radiation of intensity J is simultaneously propagating upward (+x direction). As these propagating waves move through the material, the intensity is decreased by absorption and scattering and increased by scattered radiation moving in the opposite direction. The absorbing and scattering properties of a homogenous material are described by the coefficients K and S. The change in intensity to radiation moving
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through an infinitesimally small section of X with a thickness of dx is described by the following two equations and Figure 4.