silica - magnetite presentation (singer%2c charlie r)
TRANSCRIPT
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