Correlation Between Froth Properties and Flotation Performance

HOME

A Correlation Between Visiofroth™ Measurements and the Performance of a Flotation Cell

Kym Runge, Jaclyn McMaster Michael Wortley, David La Rosa Olivier Guyot

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 Performance 2

Process Technology

VisioFrothTM •

Algorithms calculate froth parameters

-

Quantify how fast the froth is moving

-

Determine image stability and froth collapse rates

-

Quantify the froth colour

Evaluate bubble size distribution and loading

Indicate a froth textural change


Process Technology

VisioFrothTM : Software Display


Process Technology

VisioFroth/OCS VisioFroth/OCSSystem System

OCS © DCS PLC


Process Technology

Parameters Measured by Visiofroth 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


Process Technology

Parameters Measured by Visiofroth Bubble Size Measurement •

Watershed techniques used to delineate bubble contours and calculate bubble surface area

• •

Measured in real time on all frames

• •

Ability to tune watershed algorithm parameters

The segmented image and bubble size distribution are displayed pictorially within the software Bubble segmentation affected by camera zoom setting


Process Technology

Parameters Measured by Visiofroth 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

Colour Model Representations (after Gonalez and Woods, 2002 and Morar et al, 2005) Correlation of Visiofroth Parameters with Flotation Cell Performance 8

Process Technology

Parameters Measured by Visiofroth 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


Process Technology

Experimental Testwork (AMIRA P9 Campaign) Cleaner Scavenger Tailing

Rougher Feed Collector NASH Frother

Collector

Rougher

Scavenger

Rougher Concentrate

Scavenger Tailing

Scavenger Concentrate

•

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


Process Technology

Metallurgical Assessment of Flotation Cell Performance

1st Rougher

3rd Scavenger

60

35

) 50 % ( e d 40 a r G30 r e p 20 p o C 10

) 30 % ( e 25 d a r 20 G r 15 e p 10 p o C 5

Top of Froth Grade Concentrate Grade

Top of Froth Grade Concentrate Grade

0

0 20

30

40

50

Copper Recovery (%)

0

60 (a)

5

10

15

Copper Recovery (%)

20 (b)


Process Technology

Metallurgical Assessment of Flotation Cell Performance

) 50.0 % ( e 40.0 d a r G30.0 e t a r 20.0 t n e c 10.0 n o C

Rougher 1 Rougher 3 Scavenger 3 Scavenger 1

) 50.0 % ( e 40.0 d a r G30.0 e t a 20.0 r t n e c 10.0 n o C


0.0

0.0

0.0

10.0

20.0

30.0

40.0

Water Flow to Concentrate (%)

50.0

0.0

20.0

40.0

60.0

Top of Froth Grade (%)


Process Technology

Correlations Associated with Flow

40.0

45.0 40.0 35.0 ) H 30.0 P T ( 25.0 w20.0 o l F 15.0 10.0 5.0 0.0

Solids

35.0

Solids

Water

30.0

Water

) H 25.0 P T ( 20.0 w o 15.0 l F 10.0

Solids + Water

Solids + Water

5.0 0.0

0.0

5.0

10.0

15.0

20.0

Froth Velocity (cm/sec)

Rougher 1

25.0 (a)

0.0

2.0

4.0

6.0


8.0

10.0

(b)

Scavenger 3

• Flow best correlated with froth velocity • Relationship not linear


Process Technology

Correlations Associated with Flow

35.0

Solids 8/8/2001

30.0

Solids 9/8/2001

25.0

) H P 20.0 T (

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

w 15.0 o l F 10.0

5.0 0.0 0.0

2.0

4.0

6.0

8.0

10.0

12.0


• Flow versus velocity relationship changes from day to day


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


Process Technology

Correlations Associated with Grade

Rougher 1 – 40.9%

Rougher 3 – 25.5%

Scavenger 1 – 13.3%

Scavenger 3 – 2.7%


Process Technology

Correlation Between Grade and Colour Parameters Colour Model

RGB Colour Cube

HSV

Lab

Parameter

Number of Observations

Correlation Co-efficient (R 2) Concentrate Copper Assay

Top of Froth Assay

Green

25

0.017

0.024

Red

25

0.080

0.098

Blue

25

0.39

0.38

Hue

25

0.63

0.64

Saturation

25

0.34

0.19

Value/Intensity

25

0.10

0.13

Luminance

25

0.058

0.068

Lab a

25

0.47

0.42

Lab b

25

0.63

0.56


Process Technology

Correlation Between Grade and Colour Parameters 160.0 140.0 ) 120.0 s e e r 100.0 g e 80.0 d ( e 60.0 u H 40.0

20.0 0.0 0.0

10.0

20.0

30.0

40.0

50.0

60.0

Top of Froth Grade (%)

Rougher 1

Rougher 3

Scavenger 1

Scavenger 3


Process Technology

Correlations Between Grade and Bubble Size R2 = 0.4779

20.0

) m c ( e 15.0 z i S e l b 10.0 b u B e g 5.0 a r e v A

R2 = 0.7162

R2 = 0.7951

0.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Copper Grade (%) 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 Performance 19

Process Technology

Correlations Between Grade and Collapse Rate 16.0 e m 14.0 a r f 12.0 r e p 10.0 ) % r ( i e a 8.0 t a p 6.0 R e s 4.0 p a l l 2.0 o C

R2 = 0.9088

R2 = 0.7931

0.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

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

• Grade best correlated with the collapse rate parameter • Relationship better correlated with top of froth grade • Zoom setting didn’t affected collapse rate measurement Correlation of Visiofroth Parameters with Flotation Cell Performance 20

Process Technology

Concentrate Grade Prediction

50.0

e t a r 40.0 t n ) e c % n ( 30.0 o e C d d a r 20.0 e G t c i 10.0 d e r P


0.0 0.0

10.0

20.0

30.0

40.0

50.0

Actual Concentrate Grade (%)

a concentrate

=

1 a Collapse Rate + b Velocity + c


Process Technology

Process Control Implications

•

Concentrate grade and top of froth grade were well correlated with parameters measurable by the Visiofroth system

•

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


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

•

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

Concentrate grade was best predicted using both bubble collapse rate and a velocity term


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


Process Technology

Correlation Between Collapse Rate and Bubble size 16.0 r e 14.0 p 12.0 % ( ) r i e a 10.0 t a p R e 8.0 e m 6.0 s a r p f 4.0 a l l 2.0 o C 0.0

R2 = 0.7209

R2 = 0.8331

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Average Bubble Size (cm) Zoom 1 (30 observations)

Zoom 2 (28 observations)

• Inverse relationship between bubble size and collapse rate parameter • Consequence of rapid surface disintegration (Hatfield, 2007)


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 Soden and Michael Rosenfield)

• AMIRA P9 Sponsors for funding the testwork campaign


Process Technology

Correlation Between Froth Properties and Flotation Performance

Recommend Documents