Exercise 5-3 Flotation plant using distributed rate constants.

Use the flowsheet and system data that were set up for exercise 5-2.

Edit the system data and specify 25 size classes, 3 grade classes and 4 S classes. 4 S classes will allow each of the 3 grade classes to be divided into a floatable and a non-floatable class. (Note that 4 not 6 S classes are required to do this). Click the "set up S classes" button on the system data form. Specify the values of the kinetic constants in the four S classes as follows.

 S class

 K value

 1

 0.0

 2

 0.12

 3

 0.8

 4

 2.5

Table 1. K values associated with S classes

Note in particular that S class 1 has a kinetic constant of zero and therefore is associated with non-floatable particles.

Make sure that the largest particle size is set to 500 microns (5.0E-4 m) and edit the data for the feed stream. Specify 20 mesh sizes and generate a Rosin-Rammler distribution with D63.2 = 100 microns and lambda = 1.2. Feed rate should be 20.83 kg/s at 28% solids. Check that grade distribution is 80% silicates, 8.5% pyrite and 11.5% chalcopyrite.

Specify the distribution over S classes to reflect the kinetic behavior of each mineral type according to the data in Table 2.

 Mineral

 Ultimate recovery

 Kinetic constant 1/min

 Silcates

 12

 0.12

 Pyrite

 53

 0.8

 Chalcopyrite

 83

 2.5

Table 2. Kinetic constants for chalcopyrite-pyrite ore.

Thus for the grade class that is associated with silicates, the particles are distributed as 0.88 in S class 1 and 0.12 in S class 2. For the grade class that is associated with pyrite, the particles are distributed as 0.47 in S class 1 and 0.53 in S class 3. For the grade class associated with chalcopyrite, the particles are distributed as 0.17 in S class 1 and 0.83 in S class 4.

This completes the specification of the system data. Accept the system data and edit the unit model parameters.

Choose model FLTN for the three flotation stages. Since MODSIM will now keep track of each S class as the particles move from cell to cell in a bank of flotation cells, it is possible to specify that each stage is actually a bank of cells as would normally be found in practice. Specify the rougher and scavenger as 5 cells each of 2 cubic meters and the cleaner as 4 cells of 0.5 cubic meters. Notice that the data input form automatically defaults the kinetic constants to those defined for the S classes in the system data. You can change these values for this particular unit if you wish. This is done, for example, when additional collector is added in the circuit ahead of a particular bank to boost the flotation kinetics. It is also necessary when the chemical environment is changed for a particular flotation stage to change the flotation behavior radically. The chemical environment is changed by changing the type of collector, the pH, the frother or by the addition of depressants. In the present example this would occur if we wanted to make a differential separation of chalcopyrite from pyrite by floating the mixed chalcopyrite-pyrite concentrate. There is a 5 point grade bonus for any for-credit student who can post a feasible suggestion for a flotation reagent regime to make such a differential separation.

We do not want to investigate these aspects of flotation technology in this exercise so do not change any of the flotation kinetic constants in any of the three stages.

The trickiest parameter to set when using the FLTN model for flotation is the solids hold-up in the cells. This is the parameter that defines the water balance. The behavior of the individual cells and the plant as a whole is very sensitive to the value of this parameter and it must be chosen with care. The following values are recommended for this exercise: rougher 300 kg/m^3, scavenger 300 kg/m^3 and cleaner 250 kg/m^3. It is common to run cleaner cells at lower pulp densities than roughers and scavengers. This parameter defines the solid content in the floation cell in terms of the solid hold up per unit cell volume.

All other unit parameters should be left at their default values. If you started from a successful simulation in exercise 5-2, the current data should lead to a successful simulation. Run the simulation and you should make a concentrate that assays 23% Cu and 31% Fe at a copper recovery of 79%. This represents a more realistic simulation than exercise 5-2. As an interesting point you should note the large increase in residence time as the pulp progresses from cell to cell down the cleaner bank. Why does this happen and does it suggest a possible design modification to you? (You can see the residence times for the individual cells in the report file for the unit.) Try a few alternatives and see what the simulator tells you.