Correlation Between Froth Properties and Flotation Performance

Process Technology

A Correlation Between Visiofroth™Measurements and the Performance of a

Flotation Cell

Kym Runge, Jaclyn McMaster Michael Wortley, David La RosaOlivier Guyot

Correlation of Visiofroth Parameters with Flotation Cell Performance2 Process Technology

Froth Vision Systems

- Operator often makes decisions based on the appearance of the froth and how it flows

- Vision systems enable us to capture this information quantitatively and use in process control strategies

Correlation of Visiofroth Parameters with Flotation Cell Performance3 Process Technology

VisioFrothTM

• Algorithms calculate froth parameters- Quantify how fast the froth is moving- Evaluate bubble size distribution and

loading- Determine image stability and froth

collapse rates- Quantify the froth colour- Indicate a froth textural change

Correlation of Visiofroth Parameters with Flotation Cell Performance4 Process Technology

VisioFrothTM : Software Display

Correlation of Visiofroth Parameters with Flotation Cell Performance5 Process Technology

VisioFroth/OCS SystemVisioFroth/OCS System

OCSOCS©©

DCSDCSPLCPLC

Correlation of Visiofroth Parameters with Flotation Cell Performance6 Process Technology

Velocity• Modified fourier transform technique calculates the displacement between

two consecutive images • Velocity measured in both the x and y directions• Ability to process 30 frames/second• Commonly measured to assess and control the mass pull rate from a

flotation cell

Parameters Measured by Visiofroth

Correlation of Visiofroth Parameters with Flotation Cell Performance7 Process Technology

Bubble Size Measurement• Watershed techniques used to delineate bubble contours and calculate

bubble surface area• Measured in real time on all frames• The segmented image and bubble size distribution are displayed pictorially

within the software• Ability to tune watershed algorithm parameters• Bubble segmentation affected by camera zoom setting

Parameters Measured by Visiofroth

Correlation of Visiofroth Parameters with Flotation Cell Performance8 Process Technology

Colour and Brightness Descriptors• Visiofroth analyses a segment of the image and calculates the parameters

associated with three different colour models:- RGB Colour Cube- HSV Colour Model- Lab Colour Model

• The average colour descriptors of the image are reported as well as the proportion of pixels within a subset of the colour descriptors.

• Lighting and reflectance off the bubbles affects value of colour descriptors

Parameters Measured by Visiofroth

Colour Model Representations (after Gonalez and Woods, 2002 and Morar et al, 2005)

Correlation of Visiofroth Parameters with Flotation Cell Performance9 Process Technology

Collapse Rate• Relative measure of the rate of bubble coalescence on the

froth surface• Measured as the percentage change in bubble surface area

per frame pair• Related to the size and presence of bubbles• Affected by froth velocity

Parameters Measured by Visiofroth

Correlation of Visiofroth Parameters with Flotation Cell Performance10 Process Technology

Experimental Testwork (AMIRA P9 Campaign)

Rougher Feed Cleaner ScavengerTailing

Rougher ScavengerScavengerTailing

RougherConcentrate

ScavengerConcentrate

CollectorCollectorNASHFrother

• 1st Rougher, 3rd Rougher, 1st Scavenger and 3rd Scavenger cells run at three different air rates and froth depths

• Feed, timed concentrate, tailing and top of froth samples collected at each cell condition

• Five to 15 minutes of froth vision recorded using a JVC hand held camera mounted above each cell

Correlation of Visiofroth Parameters with Flotation Cell Performance11 Process Technology

Metallurgical Assessment of Flotation Cell Performance

1st Rougher

0

10

20

30

40

50

60

20 30 40 50 60

Copper Recovery (%)

Copp

er G

rade

(%)

Top of Froth GradeConcentrate Grade

(a)

3rd Scavenger

05

101520253035

0 5 10 15 20

Copper Recovery (%)

Copp

er G

rade

(%) Top of Froth Grade

Concentrate Grade

(b)

Correlation of Visiofroth Parameters with Flotation Cell Performance12 Process Technology

Metallurgical Assessment of Flotation Cell Performance

0.0

10.0

20.0

30.0

40.0

50.0

0.0 10.0 20.0 30.0 40.0 50.0

Water Flow to Concentrate (%)

Conc

entra

te G

rade

(%)

Rougher 1Rougher 3Scavenger 3Scavenger 1

0.0

10.0

20.0

30.0

40.0

50.0

0.0 20.0 40.0 60.0

Top of Froth Grade (%)

Conc

entra

te G

rade

(%)

Rougher 1Rougher 3Scavenger 3Scavenger 4

Correlation of Visiofroth Parameters with Flotation Cell Performance13 Process Technology

Correlations Associated with Flow

0.05.0

10.015.020.025.030.035.040.045.0

0.0 5.0 10.0 15.0 20.0 25.0

Froth Velocity (cm/sec)

Flow

(TPH

)

SolidsWaterSolids + Water

(a)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0.0 2.0 4.0 6.0 8.0 10.0

Froth Velocity (cm/sec)

Flow

(TPH

)

SolidsWaterSolids + Water

(b)

Rougher 1 Scavenger 3

• Flow best correlated with froth velocity• Relationship not linear

Correlation of Visiofroth Parameters with Flotation Cell Performance14 Process Technology

Correlations Associated with Flow

• Flow versus velocity relationship changesfrom day to day

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Froth Velocity (cm/sec)

Flow

(TPH

)

Solids 8/8/2001Solids 9/8/2001Water 8/8/2001Water 9/9/2001

Correlation of Visiofroth Parameters with Flotation Cell Performance15 Process Technology

Process Control Implications

• Appropriate to use froth velocity to control mass pull rate• Froth velocity cannot be used as a measure of mass pull rate

Correlation of Visiofroth Parameters with Flotation Cell Performance16 Process Technology

Correlations Associated with Grade

Rougher 1 – 40.9% Rougher 3 – 25.5%

Scavenger 1 – 13.3% Scavenger 3 – 2.7%

Correlation of Visiofroth Parameters with Flotation Cell Performance17 Process Technology

Correlation Between Grade and Colour Parameters

0.560.6325Lab b

0.420.4725Lab a

0.0680.05825LuminanceLab

0.130.1025Value/Intensity

0.190.3425Saturation

0.640.6325HueHSV

0.380.3925Blue

0.0980.08025Red

0.0240.01725GreenRGB Colour Cube

Top of Froth AssayConcentrate Copper Assay

Correlation Co-efficient (R2)Number ofObservations

ParameterColour Model

Correlation of Visiofroth Parameters with Flotation Cell Performance18 Process Technology

Correlation Between Grade and Colour Parameters

0.020.040.060.080.0

100.0120.0140.0160.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0

Top of Froth Grade (%)

Hue

(deg

rees

)

Rougher 1 Rougher 3 Scavenger 1 Scavenger 3

Correlation of Visiofroth Parameters with Flotation Cell Performance19 Process Technology

Correlations Between Grade and Bubble Size

R2 = 0.7162

R2 = 0.7951

R2 = 0.4779

0.0

5.0

10.0

15.0

20.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0

Copper Grade (%)

Aver

age

Bubb

le S

ize

(cm

)

Con Grade (Zoom 1) (24 observations)Con Grade (Zoom 2) (30 observations)Top of Froth Grade (Zoom 2) (25 observations)

• Grade related to bubble size measured on surface• Relationship better correlated with top of froth grade• Zoom setting affected bubble sizing measurement

Correlation of Visiofroth Parameters with Flotation Cell Performance20 Process Technology

Correlations Between Grade and Collapse Rate

• Grade best correlated with the collapse rate parameter• Relationship better correlated with top of froth grade• Zoom setting didn’t affected collapse rate measurement

R2 = 0.7931

R2 = 0.9088

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0

Copper Grade (%)

Colla

pse

Rate

(% p

er fr

ame

pair)

Con Grade (Zoom 1 & 2) (58 observations)Top of Froth Grade (Zoom 2) (25 observations)

Correlation of Visiofroth Parameters with Flotation Cell Performance21 Process Technology

Concentrate Grade Prediction

0.0

10.0

20.0

30.0

40.0

50.0

0.0 10.0 20.0 30.0 40.0 50.0

Actual Concentrate Grade (%)

Pred

icte

d Co

ncen

trate

G

rade

(%) Rougher 1

Rougher 3Scavenger 1Scavenger 3

c Velocity b Rate Collapse a

1 a econcentrat ++=

Correlation of Visiofroth Parameters with Flotation Cell Performance22 Process Technology

Process Control Implications

• Concentrate grade and top of froth grade were well correlated with parameters measurable by the Visiofrothsystem

• Potential to use these correlations within a model to optimise bank performance

Correlation of Visiofroth Parameters with Flotation Cell Performance23 Process Technology

Conclusions

• Visiofroth is a system which measures parameters that are correlated to flotation cell performance

• Solids and water flow from a flotation cell are correlated with froth velocity and thus can be used to increase or decrease mass pull rates within a process control strategy

• Top of froth grade was correlated with bubble collapse rate• Concentrate grade was best predicted using both bubble

collapse rate and a velocity term• Potential to use Visiofroth to estimate concentrate purity for

use in a process control strategy • Bubble collapse rate seems to be dependent solely on the

grade of attached particles and not mass loading

Correlation of Visiofroth Parameters with Flotation Cell Performance24 Process Technology

Flotation Process Control in the Future

• Prediction of concentrate grade using froth properties

• Optimise the grade versus recovery relationship in a bank through control of froth velocity and stability

• Model based control - Model developed utilising process

instrumentation- Concentrate grade and recovery

targets established for each bank by a model

- Froth vision systems maintain operation at targeted conditions

Correlation of Visiofroth Parameters with Flotation Cell Performance25 Process Technology

Correlation Between Collapse Rate and Bubble size

• Inverse relationship between bubble size and collapse rate parameter

• Consequence of rapid surface disintegration (Hatfield, 2007)

R2 = 0.8331

R2 = 0.7209

0.02.04.06.08.0

10.012.014.016.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Average Bubble Size (cm)

Colla

pse

Rate

(% p

er

fram

e pa

ir)

Zoom 1 (30 observations) Zoom 2 (28 observations)

Correlation of Visiofroth Parameters with Flotation Cell Performance26 Process Technology

Acknowledgements

• Northparkes Metallurgical and technicians who assisted with the test program and reviewed the testwork results (Rick Dunn, Adam Clark, Heather Gaut, Tom Rivet)

• JKMRC and McGill researchers who assisted with the testwork (David Seaman, Eddy Sanwani, Cesar Gomez, Jorge Torrealba, Brigitte Seaman, Marco Vera, Ester Sodenand Michael Rosenfield)

• AMIRA P9 Sponsors for funding the testwork campaign