XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 2823
because an orthogonal (i.e., symmetric) fractional factorial
design was used. The magnitude of the change in the mean
copper recovery between the levels can be used to assess the
degree to which that factor influences the copper recovery
and whether it has a positive or negative influence. It is,
therefore, a useful tool to categorize the importance of the
factors.
The frother addition had a significant effect on copper
recovery. To a lesser degree, the fresh feed flow rate, feed sol-
ids concentration, and vacuum pressure also affected cop-
per recovery. Froth depth had a negative effect on copper
recovery, especially at deep froth depths. Wash water addi-
tion did not strongly affect the copper recovery achieved.
The results from the screening tests were also used to
perform a regression analysis to determine the relationship
between the copper recovery (Cu Rec) and the Jameson
cell operating variables. The confidence level was set at
95% (two-sided), and a stepwise elimination method was
applied to remove terms with an alpha value lower than
0.15. The model equation derived is shown in Equation 1,
where FF is the fresh feed flow rate (kg/h of slurry), %Sol is
the feed solids concentration (%m/m), VacP is the vacuum
pressure (kPa), FD is the froth depth (mm) and FRTH the
frother addition (ppm).
.840
%.064 .495
.00851
Cu Rec FF
Sol VacP
FD FD FRTH
41.0 0.00383 0
1 0
0
#
##
###
=+-
+-
+
(1)
The model can fit the experimental data well, with the coef-
ficients of determination of the relationship being accept-
able for pilot-scale industrial data (Table 4).
The parameters have a high degree of significance,
with the P-values of the coefficients being significantly less
than 0.05 (i.e., the 95% confidence criterion), as shown in
Table 5.
Figure 8 compares the observed and predicted Cu
recoveries for the tests. There is a reasonable correlation
between the observed and predicted copper recovery values.
It is important to note that a model validation data set was
Table 4. Regression model statistics
Standard Error R2 R2 Adjusted
6.14 79.8% 73.5%
Table 5. Statistics associated with the regression model parameters
Term Coefficient Standard Error P value
Constant 41.0 13.7 0.009
Fresh Feed Flow 0.00383 0.00166 0.035
Feed %Solids –0.840 0.306 0.014
Vacuum Pressure 1.064 0.348 0.007
Froth Depth –0.495 0.122 0.001
Froth Depth × Frother Addition 0.00851 0.00132 0.000
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80
Predicted Cu Recovery (%)
Figure 8. Observed vs. predicted copper recovery for the screening tests
Observed
Cu
Recovery
(%)
because an orthogonal (i.e., symmetric) fractional factorial
design was used. The magnitude of the change in the mean
copper recovery between the levels can be used to assess the
degree to which that factor influences the copper recovery
and whether it has a positive or negative influence. It is,
therefore, a useful tool to categorize the importance of the
factors.
The frother addition had a significant effect on copper
recovery. To a lesser degree, the fresh feed flow rate, feed sol-
ids concentration, and vacuum pressure also affected cop-
per recovery. Froth depth had a negative effect on copper
recovery, especially at deep froth depths. Wash water addi-
tion did not strongly affect the copper recovery achieved.
The results from the screening tests were also used to
perform a regression analysis to determine the relationship
between the copper recovery (Cu Rec) and the Jameson
cell operating variables. The confidence level was set at
95% (two-sided), and a stepwise elimination method was
applied to remove terms with an alpha value lower than
0.15. The model equation derived is shown in Equation 1,
where FF is the fresh feed flow rate (kg/h of slurry), %Sol is
the feed solids concentration (%m/m), VacP is the vacuum
pressure (kPa), FD is the froth depth (mm) and FRTH the
frother addition (ppm).
.840
%.064 .495
.00851
Cu Rec FF
Sol VacP
FD FD FRTH
41.0 0.00383 0
1 0
0
#
##
###
=+-
+-
+
(1)
The model can fit the experimental data well, with the coef-
ficients of determination of the relationship being accept-
able for pilot-scale industrial data (Table 4).
The parameters have a high degree of significance,
with the P-values of the coefficients being significantly less
than 0.05 (i.e., the 95% confidence criterion), as shown in
Table 5.
Figure 8 compares the observed and predicted Cu
recoveries for the tests. There is a reasonable correlation
between the observed and predicted copper recovery values.
It is important to note that a model validation data set was
Table 4. Regression model statistics
Standard Error R2 R2 Adjusted
6.14 79.8% 73.5%
Table 5. Statistics associated with the regression model parameters
Term Coefficient Standard Error P value
Constant 41.0 13.7 0.009
Fresh Feed Flow 0.00383 0.00166 0.035
Feed %Solids –0.840 0.306 0.014
Vacuum Pressure 1.064 0.348 0.007
Froth Depth –0.495 0.122 0.001
Froth Depth × Frother Addition 0.00851 0.00132 0.000
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80
Predicted Cu Recovery (%)
Figure 8. Observed vs. predicted copper recovery for the screening tests
Observed
Cu
Recovery
(%)