THE DEVELOPMENT OF A REAGENT ENVIRONMENT FOR A SILICA-MAGNETITE FROTH FLOTATION SYSTEM

UROP Project July-September 2015Anna Caklais and Charlie Singer

Project Aims• Develop a simplified bench-scale system with which to test

design modifications to laboratory-scale flotation tanks.• Understand the principles of froth flotation of silica through

examining the fundamental principles of air pressure, pH, and reagent dosages.

• Advance understanding from a single silica species system through investigating the effects of varying reagent concentrations on a silica-magnetite froth flotation system.

• Analyse data to determine an optimum reagent environment that would enable separation of the two species.

Planning• Determined the effects of pH, frother concentration and air speed

variation on a single silica species system.- Allowed application to two species system

• Investigation into two projects:- Charlie : Silica-barite- Anna: Silica-magnetite

• Problems encountered:- Magnetic separation not applicable to barite- Attempted dissolving the barite using sodium carbonate - unsuccessful on time scale- Attempted using density contrasts between silica and barite to deduce the relative proportions in overflow samples - however, could not physically separate the two species

Planning• Sample sizing of silica and magnetite to determine feed

grain size distributions.

-38 +38 -45

+45 -53

+53 -63

+63 -75

+75 - 90

+90 -106

+1060

102030405060708090

100

Silica Feed Size Distribution

+44 -88 +30 - 62 50:50

Particle Size (µm)

Mas

s (g

)

<38 38-45 45-53 53-63 63-75 75-90 90-106 >1060

5

10

15

20

25

30

35

40

45

50Feed Size Distribution

Magnetite Feed Silica Feed Feed Used

Particle Size (µm)

Mas

s (g

)

Planning• Undertook literature searches - scoping work by Filippov. L. O.

et al (2010) was used in the selection of reagents and their concentrations.

• Reverse cationic flotation – dominant method used in research and industry whereby the collector adopts a positive charge to float the gangue mineral (silica)

Reagents:• Collector: Dodecylamine at 98%-purity • Activator: 1-Tridecanol• Depressor: Modified cornstarch • For each reagent a maximum, optimum and minimum

concentration was selected for investigation.

Risk Assessments• Completed personal hazard assessments• Updated COSHH files with reagents and materials• Completed SOP forms with experimental procedure

Experimental design• Experimental design:

• Fractional factorial programme was employed using an augmented Box –Behnken test.

• Series of 15 representative experiments constructed to vary collector, activator, and depressor dosages top understand effects on both grade and recovery.

Experimental Methods:

Chemical  -1  0  1 Dodecylamine  30 g/t; 0.027 g  45 g/t; 0.0405 g  60 g/t; 0.054 g 1-Tridecanol  5 g/t; 0.0045 g  15 g/t; 0.0135 g  25 g/t; 0.0225 g Starch  500 g/t; 0.45 g  750 g/t; 0.675 g  1000 g/t; 0.9 g 

Flotation methods• Flotation undertaken using a Denver Cell:

• 60:40 ratio silica: magnetite:• 270 g +30-62; 270 g +44-88; 360 g magnetite• 2.1 L deionised water• 30% solids

Method:• Mixing of solids and deionised water for 2 minutes and pH reading taken• Activator and depressor added – 5 minutes conditioning• pH reading taken and collector added to slurry and allowed to condition for 10 minutes

to allow absorption to the silica• No PH modifier added – varied between 9.8 and 10.2• Frother incorporated into mixture for 1 minute• Air and impeller speed maintained constant• Fast overflow collector for 30 seconds, and slow overflow for 90 seconds• Wet masses measured• Oven dried to determine dry masses.

Sampling methods• Dry masses recorded• Each sample placed on top of a stack of sieves and shaken for 15

minutes:• 106 μm• 90 μm• 75 μm• 63 μm• 53 μm• 45 μm• 38 μm- Samples re-weighed and masses recorded.- Horseshoe magnets were used to separate magnetite grains from the silica – each placed in separate sample bags.- Remaining silica weighed to calculate grade and recovery.

ExperimentRun 1 (%) Run 2 (%)

Total flow gradeTotal flow recovery Total flow grade

Total flow recovery

1 [-1] [-1] [0] 74.222 9.478   

2 [-1] [1] [0] 83.350 15.654 80.589 24.555

3 [1] [-1] [0] 85.717 42.846 92.205 27.710

4 [1] [1] [0] 83.371 43.956 77.728 37.115

5 [-1] [0] [-1] 87.824 37.341 81.249 23.774

6 [-1] [0] [-1] 72.273 8.311 78.714 19.338

7 [1] [0] [-1] 83.787 38.535 83.548 47.291

8 [1] [0] [1] 89.024 50.787 81.379 48.814

9 [0] [-1] [-1] 84.669 47.111 76.037 40.810

10 [0] [-1] [1] 86.618 47.134 87.185 41.034

11 [0] [1] [-1] 88.056 32.091 86.406 30.691

12 [0] [1] [1] 76.413 17.099 77.865 20.449

13 [0] [0] [0] 77.499 38.310 77.650 51.606

14 [0] [0] [0] 87.351 46.555 79.949 46.785

15 [0] [0] [0] 84.374 37.718 81.471 39.969

Grade = Mass of silica obtained in sieve x 100Total mass of overflow solids

Recovery = Mass of silica retained on sieve x 100Initial mass of silica in tank pulp

Results from First Linear Regression

Variable Effect Standard Error t-value p-valueIntercept 84.061 1.009 83.315 9.62E-25

x1 C 3.8752 1.5959 2.4282 0.025882

x2 A -1.0464 1.4119 -0.74116 0.46816

x3 D -3.1492 1.2846 -2.4516 0.024662

x4 O -1.9506 1.6695 -1.1684 0.25788

x1:x2 C:A

-3.8893 1.5165 -2.5646 0.019493

x1:x3 C:D 1.1614 1.7323 0.67046 0.51107

x1:x4 C:O -1.4321 2.3777 -0.6023 0.55448

x2:x3 A:D -3.2478 1.5482 -2.0978 0.050308

x2:x4 A:O 1.2101 2.0556 0.58869 0.56339

x3:x4 D:O 4.2758 3.0187 1.4164 0.17372

Model: y ~ 1 + x1 + x2 + x3 + x4 + (x1*x2) + (x1*x3) + (x1*x4) + (x2*x3) + (x2*x4) + (x3*x4)

R-squared: 0.67 Adjusted R-Squared: 0.486 p-value : 0.00827

Variable Effect Standard Error t-value p-value

Intercept 84.138 0.91242 92.214 7.04E-29

x1 C 3.5794 1.0379 3.4487 0.002406

x2 A -0.63544 0.97449 -0.65207 0.52143

x3 D -3.3619 1.1273 -2.9823 0.007103

x4 O -2.4212 1.5483 -1.5638 0.13281

x1:x2 C:A -3.8591 1.4464 -2.668 0.014392

x2:x3 A:D -2.9149 1.4548 -2.0037 0.058174

x3:x4 D:O 5.6073 2.5611 2.1894 0.040005

Model: y ~ 1 + x1 + x2 + x3 + x4 + (x1*x2) + (x2*x3) + (x3*x4)

R-squared: 0.64 Adjusted R-Squared: 0.52 p-value : 0.00128

First Step-wise Linear Regression

Variable Effect Standard Error t-value p-value

Intercept 83.534 1.0045 83.16 6.13E-28

x1 C 3.9178 1.651 2.373 0.027269

x2 A -0.062962 1.3645 -0.04614 0.96363

x3 D -3.0283 1.3274 -2.2814 0.033063

x4 O -1.6489 1.5826 -1.0419 0.30931

x1:x2 C:A -4.032 1.5608 -2.5833 0.017336

x1:x3 C:D 1.3275 1.7882 0.74236 0.46609

x1:x4 C:D -1.3725 2.4499 -0.56024 0.58125

x2:x3 A:D -3.3235 1.6012 -2.0757 0.050395

x2:x4 A:O 0.29558 1.9716 0.14992 0.88226

x3:x4 D:O 3.9729 3.1174 1.2744 0.21643

Model: y ~ 1 + x1 + x2 + x3 + x4 + (x1*x2) + (x1*x3) + (x1*x4) + (x2*x3) + (x2*x4) + (x3*x4)

R-squared: 0.613Adjusted R-Squared: 0.429 p-value: 0.00977

Linear Regression with Additional 1-Tridecanol Data

Variable Effect Standard Error t-value p-value

Intercept 83.657 0.90964 91.967 4.25E-32

x1 C 3.5646 1.0718 3.3259 0.002828

x2 A -0.030364 0.93536 -0.03246 0.97437

x3 D -3.2728 1.1649 -2.8094 0.009714

x4 O -2.018 1.4733 -1.3697 0.18346

x1:x2 C:A -4.0698 1.4878 -2.7354 0.011528

x2:x3 A:D -2.9536 1.503 -1.9651 0.06108

x3:x4 D:O 5.4524 2.6348 2.0694 0.049444y ~ 1 + x1 + x2 + x3 + x4 + (x1*x2) + (x2*x3) + (x3*x4)

R-squared: 0.587 Adjusted R-Squared 0.467 p-value = 0.00156

Step-wise Linear Regression with Additional 1-Tridecanol Data

Non-Linear Regression