Mining Applications and Chemometrics

Mining Applications and Chemometrics

SPECTRAL EVOLUTION

www.spectralevolution.com

SPECTRAL EVOLUTION


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


oreXpress Mineral

Analysis/Identificatio

n


Iron Minerals


CalciteTalc

Hornblende


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

Documents

Mining Applications and Chemometrics