processing and information extraction from near ...

PROCESSING AND INFORMATION EXTRACTION FROM NEAR INFRARED (NIR) DIFFUSE REFLECTANCE SPECTROSCOPY OF OIL SANDS FOR BITUMEN GRADE MEASUREMENT Theodore Garver, Ph.D. Matthew Morrison, E.I.T. Nancy Luo Canadian Mineral Processor’s Conference (CMP) October 8, 2014 Fort McMurray, AB

Overview  Introduction Overview of the challenge of NIR

measurement of oil sands

 Sources of NIR signal  Diffuse reflectance  Vibrational and electronic transitions  Overtones and combinations  Components: bitumen, clay, sand, water

 Effects of Particle Size, Packing Density  Sample preparation methodology is key

 Characterization of spectra for ore quality descriptors.  Calibration of field instruments to lab measurements

AITF Instruments NIR Analyzer™ % bitumen & water

Tank Gauge Level Measurement

K40 Analyzer™ % clay

Bitumen Quality Solvent in Bitumen

OILSAND ORE SURFACE MINE

FROTH TREATMENT

water CONDITIONING SLURRY PREP

chemical TAILING S

K40 Analyzer™ % clay

Tailings Analyzer™ % bitumen in tails

FROTH SLURRY

PRIMARY SEPARATOR SEPARATION

MIDDLINGS

Solvent in Tailings

Tailings Analyzer™ % bitumen in middlings Level Detector™ Interface Measurements

Wall Thickness Wireless Pipeline Monitor

TAILINGS

Tailings Analyzer™ % bitumen in tails

BITUMEN

PRODUCT

K40 Analyzer™ % clay

Instrumentation Output Accurate and Reproducible Spectroscopy

Data Acquisition

Process Relevant Information Extracted as Descriptors

Data Enhanced to Improve Information Extraction

Data Pre-processing

Information Extraction

Bitumen Water Fines Embedded in Instrument

Control Methodology

Information Use Client

Instrument Design

Base-line Correction

Peak Height

Feed Forward Process Control

Instrument Reliability

Filtering

Integration

Feed Backward Process Control

Smoothing

Derivatives

Mining Control

Derivatives

Defined wavelength

Inventory and Mixing Control

Multiplicative Scatter Correction

Principle Component Analysis Least Squares Fitting to Reference Spectra Chemometics

AITF is involved in improving the information quality that is available to the client for use with process control. Activities to improve instrument quality may range from issues related to spectroscopic configuration, reliability, data pre-processing and chemometric approaches to information extraction from the acquired data.

NIR Ore Analyzer  NIR Ore Analyzer is placed above a belt or apron feeder.  Measures diffuse reflectance near infrared light from oil sands sample.  Signal is derived primarily from vibrational energies of functional group (-CH2, -CH3, R-OH, HOH) vibrational modes in the near-infrared region.  Instrument software upgrade provides:   

Corrected reflectance Analysis of bitumen water and clay components Diagnosis for spectrum integrity, lamp age and interference from humidity.

System Configuration

The Challenge of Diffuse Reflectance NIR Measurement of Oil Sands  The diffuse reflectance signal varies with:  Sample texture, roughness or granularity  Particle size of mineral components

 Sample heterogeneity.

 The NIR surface measurement may not be related in a simple way to bulk property measurements.  Clay may be covered with a layer of bitumen and sand;

other times the bitumen may be adsorbed on the clay.  Differences in the absorptivity and scattering efficiency of different components tends to favor signal from absorbing substances (hydrocarbon).

Sources of Signal (I)

Infrared vibrational spectra of oil sand peaks relate to bitumen, clay, sand and water. Rivard, B., D. Lyder, et al. (2010). "Bitumen Content Estimation of Athabasca Oil Sand from Broad Band Infrared Reflectance Spectra." The Canadian Journal of Chemical Engineering 88(5): 830-838

Diffuse Reflectance NIR The near infrared instrument measures light that has scattered off of the illuminated materials. This light has interacted with the surface layers of the material and reflected, refracted and diffracted as well as absorbed. • Spectral reflectance tends to specular reflectance preserve spectrum integrity and provides little information on the surface absorbance. • Diffuse reflectance provides spectroscopic information on the absorbance of the surface. • Diffuse reflection depends on texture, particle size, packing, moisture. surface 20.00

diffuse reflectance

20.00

surface

Overtones and Combinations Second harmonic overtones (on diagonal, highlighted) and combinations. Combinations are shown in green. wavenumbers (1/cm)

wavelength (nm)

1/cm

1375

1450

2850

2920

2940

nm

7273 6897 3509 3425 3401

1375

2750

2825

4225

4295

4315

7273

3636 3540 2367 2328 2317

1450

2825

2900

4300

4370

4390

6897

3540 3448 2326 2288 2278

2850

4225

4300

5700

5770

5790

3509

2367 2326 1754 1733 1727

2920

4295

4370

5770

5840

5860

3425

2328 2288 1733 1712 1706

2940

4315

4390

5790

5860

5880

3401

2317 2278 1727 1706 1701

NIR of Oil Sand 0.2 nm Resolution Spectra

Sources of Signal (III)

Bitumen, Clay, Water Dougan described breaking the signal from oil sands into component signals from bitumen, water and mineral.(Dougan, 1989). DOUGAN, P. D. 1989. Near-Infrared Reflectance Analysis: Its Potential Application in Oil Sand Processing. AOSTRA Journal of Research, 5, 203-210.

Data Extraction Methods  With offset or baseline subtraction.  Peak height measurement  Peak area measurement

 Reference spectra fitting.  Derivatives (with smoothing)  Savitzky-Golay methods are most popular

 Empirical Fitting Methods  Partial least squares

 Principle component analysis

Peak Integration

Peak Derivatives

Fitting to Reference Spectra

Light interaction with Oil Sands Sample heterogeneity and granularity 1 0.9

A.

absorbance (baseline corrected)

0.8 0.7 0.6

sy001-1 sy001-2

0.5

sy001-3 sy001-4

0.4

sy001-5 sy001-6

0.3

0.2 0.1 0 850

B.

1050

1250

1450

1650 1850 wavelength (nm)

2050

2250

2450

2650

Packing and sample measurement with loose packing in A. and dense packing in B. Bitumen is sampled more times in the loose pack structure. The variation of NIR diffuse reflection signal from oil sands as a function of sample packing was initially described by Shaw et. al. SHAW, R. C. & KRATOCHVIL, B. 1990. Near-Infrared Diffuse Reflectance Analysis of Athabasca Oil Sand. Analytical Chemistry, 62, 167-174.

Oil Signal and Particle Sizes

Particle size can influence the calibration between bitumen concentration and observed NIR signal.

Calibration to Bitumen, Water, Clay  Outputs from the instrument have been calibrated to bitumen, water, and clay.  The best correlation is with bitumen (generally between 0.75

and 0.90 R2.  Correlation with clay or fines values is between 0.50 and 0.75 R2.

 Bitumen, clay and water descriptors can be used with non-linear and multiple regression models.  We are currently verifying calibration models with online data.

Data Processing

Conclusions  Sampling and sample preparation methods must account for signal variation due to particle texture (granularity).  Variation between sample presentation in the lab

and in the field must be accounted for when using a lab calibration in the field.  Buried clay may not be detected by the analyzer and should be accounted for.

 Different data extraction methods yield correlated (similar) results. The best method depends on spectral quality and resolution.

Final Comment  Our integrated instrumentation development program spans from core science to instrument engineering to field implementation and service.  We are working with partner companies to accelerate instrumentation development for the oil sands.  The improvements in the NIR ore grade analyzer discussed here substantially improve instrument accuracy and reliability while reducing maintenance needs.