mining applications and chemometrics
TRANSCRIPT
Mining Applications and Chemometrics
SPECTRAL EVOLUTION
www.spectralevolution.com
SPECTRAL EVOLUTION
www.spectralevolution.com
Incorporated 2004Full line supplier of UV-VIS-NIR
spectrometers for lab, inline process & field portable remote sensing
Mfg facility in North Andover , MA
OEM manufacturer>100 field portable UV-VIS-NIR
instruments in field use worldwide
Products offered
Field portable full range UV-VIS-NIR spectrometers & spectroradiometers
Laboratory full range UV-VIS-NIR spectrometers & spectroradiometers
Single detector InGaAs photodiode array lab spectrometers
Single detector Si spectrometers, spectroradiometers & spectrophotometers
Light sources & accessories
Mining Spectrometers
Spectrometers for mining exploration, mineral identification, and production oreXpress™
Full range portable spectrometer for mining and mineral identification
oreXpress PlatinumAlso includes a range of FOV lenses, internal battery, membrane control panel for standalone operation, and on-board storage for 1,000 spectra
oreXpress & oreXpress Platinum True field portability <7 lbs Full range UV/VIS/NIR – 350-2500nm Fast/High Signal to Noise ratio
for better reflectance values Unmatched stability & performance
through SWIR2 DARWin SP Data Acquisition
software saves scans as ASCII files foruse with 3rd party software
EZ-ID real-time mineral ID with USGS & SpecMIN libraries
Field Portable units
EZ-ID™ Software with Library Builder Module Real-time mineral identification
in the field USGS and SpecMIN libraries Select different spectral regions of interest Compare unknown mineral sample spectra
to known library Best match score quickly and automatically
displayed
Real-time Mineral ID
Qualitative & Quantitative Analysis Use EZ-ID for mineral identification and
qualitative analysis What is there
Use reflectance spectroscopy and chemometrics for quantitative analysis How much is there
Qualitative & Qualitative
Widely used in mining exploration and mineralidentification
Identification of key alterationminerals associated with potentialeconomic deposits
Qualitative mineralogy describes the process of using NIR to quicklyID mineral species during exploration
Reflectance Spectroscopy
Advanced Argillic
Argillic
Phyllic
Propylitic
Potassic
Usage is typically bound by cost (high) and speed (slow)
Available examples: Qemscan/MLA Quantitative X-ray diffraction
Better solution – Quantitative Reflectance Spectroscopy Analyze a greater number of samples in less
time, at an affordable cost
Quantitative Mineralogy
Use mineralogical and metallurgical information from a representative set of samples and correlated reflectance spectra to develop statistical calibration models
Calibration “trains” the spectrometer to analyze additional unknown samples
Leverage the detailed, more costly analysis of a few samples to analyze a much larger set of related samples
Quantitative Reflectance Spectroscopy
Useful for mining process optimization Real-time or near real-time knowledge of
mineralogical and metallurgical properties that impact metal recovery, allows for▪ Intelligent ore sorting▪ Optimization of ore processing
Useful for gangue minerology to minimize process cost and increase yield Gangue can affect extractability▪ Talc and hornblende interfere with flotation▪ Carbonates increase acid costs▪ Clays can reduce yield due to loss of heap permeability
Quantitative Reflectance Spectroscopy
www.spectralevolution.com
oreXpress Mineral
Analysis/Identificatio
n
www.spectralevolution.com
Iron Minerals
www.spectralevolution.com
CalciteTalc
Hornblende
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Clays
Advantages of reflectance spectroscopy High throughput
Hundreds to thousands of samples per day – ideal for rapid blast hole chip analysis
Frequent (<1 minute intervals) measurements for in-process sensors
Non-contact measurements Simultaneously determine multiple
properties
Reflectance Spectroscopy
Calibration Process
Create Standards
CollectSpectra
Predict Concentrations
Build, Optimize & Test Model
Measure Unknown
Access Model
Prepare Calibration Set Samples should reflect the physical properties and
diversity that will be encountered in the field Analyze the properties of interest using appropriate
reference analytical methods, such as: Qemscan X-ray diffraction Acid consumption Other metallurgical tests
Measure the reflectance spectra
Step 1: Prepare Set
Things to consider in measuring spectra Features can overlap and may not be from a single
component Spectral features in minerals can result from
crystal field effects, charge transfer, color centers, and conduction band transitions
Spectral features in organic and industrial samples come primarily from CH, NH, OH, and SH bonds
Multivariate models can consider all, or a substantial portion of the whole spectrum
Step 1: Prepare Set
Develop and validate your calibration Match each reflectance spectrum you have
collected to the corresponding reference analyses Develop calibration equations using multivariate
chemometric techniques like partial least-squares regression
Validate the performance of the calibration by using an independent set of samples
Step 2: Develop & Validate
How to select a reference method NIR is a secondary method – the reference needs to
be well controlled with the lowest possible error The Standard Error of Laboratory (SEL) should be
known and documented If there are changes in the reference method, new
reference data may be substantially different from your original data
Submission of known samples is a good idea
Step 3: Reference Method
Things to consider in collecting spectra Verify your system performance using wavelength
standards Control particle size, moisture, temperature, and
sample packing , or stabilize your model to resist changes in these parameters
Use the same sample preparation as optical geometry can affect your outcome
Step 4: Spectral Collection
Now apply your calibration Prepare unknown samples with the same method
used for calibration samples Measure the reflectance spectrum of the unknown
using the same set-up used in building the calibration
Apply the calibration to the unknown reflectance spectrum to predict mineralogical and metallurgical properties
Step 5: Apply Calibration
How many samples will I need for calibration and test? Reserve 20% of samples for an independent test
set 60-90 samples for a feasibility study 120-180 for starting model >180 for a robust production model
Number of Samples
How many samples will I need for calibration and validation? Cover the anticipated range of composition Scan in the form that will be analyzed by the model
– make them match Contain a natural combination of minerals - avoid
blending as it can cause problems, beware cross correlations
Calibration & Validation Samples
Ensuring that your model retains its integrity Watch out for samples with high spectral residual
and samples that predict at or near the extremes of your model
Establish a consistent monitoring program with reference analysis done frequently
Implement a plan and schedule for improvement of the model including identifying new samples
Establish criteria for revising the model based on time, increased validation error, or similar characteristics
Maintaining the Model
Examples of chemometric analyses using reflectance spectroscopy
Examples