Sensitivity analysis of a grinding-flotation plant with the USIM PAC process simulator
Simulation is now widely used in many process industries. This paper presents the interest of process simulation for studying the sensitivity of mineral processing plants.
USIM PAC process simulator has been developed since 1986 for the mineral processing industry. USIM PAC handles the design and optimization of entire processing plants with a comprehensive set of mathematical models for unit operations that span from crushing to refining. A variety of objective functions and tools, such as the simulation supervisor, facilitate the global multi-criterion optimization.
The supervisor of simulation uses the automation concepts of sensors and actuators. At any place of the flowsheet, the user implements a “soft actuator” by choosing the process parameter such as equipment size or settings, stream flowrate, percent solids or d80, which can vary. Then, he can evaluate the effect of the variation of this value at any other place of the flowsheet by inserting a “soft sensor”. Thus, by example, it becomes possible to look at the evolution of the recoveries and/or the circulating loads when scanning an interval of the possible feed flowrates.
A classical grinding flotation circuit processing a gold ore
The example presented here is built from several sets of plant data coming from different plants and compiled to obtain realistic demonstration data.
The flowsheet of the plant is presented in Figure 1. It is a classical grinding flotation circuit processing a gold ore for producing a gold concentrate. At the initial stage the flotation is fed by the hydrocyclones overflow without regulation of the pulp density. The possibility to regulate the flotation feed density has been added as an option.
Figure 1. Flowsheet of the plant
The principle is to modify some of the operating parameters and the number of cells in the flotation circuit and to check the impact on the grade/recovery curve.
Main equipment sizes are given in Table 1. The circuit feed size distribution is given in Figure 2.
Figure 1. Flowsheet of the plant
In the initial configuration, with the operating parameters given in Table 2, the concentrate has a grade of 121 g/t Au with a recovery of 80.4 %. Detailed performances are given in table 3.
Variation of the %-solids of the hydrocyclones feed
The supervisor is run to study the variation of the percent solids of the hydrocyclones feed. A modification of the feed density will impact the pressure drop in cyclones thus the overflow size distribution, as well as the overflow percent solids. In a first run, this percent solids is set back to 20% for feeding the flotation using a density regulator. Like this, the performances of the flotation circuit will only be impacted by its feed size distribution.
As highlighted in Figure 3, the user selects the model parameters of unit 3, percent solids regulator, and indicates the range of variation, 30 to 45, around the initial value 35. The precision (step of the scanning procedure) is set to 1, meaning that 16 simulations will be run. Several parameters could be selected at the same time. However, for being able to understand the real role of each of them, it is better, at least in a first stage, to test them one by one.
Figure 3. Selection of the soft actuator
In the dialog box of Figure 4, the user selects all the performances that must be followed.
Figure 4. Selection of the soft sensors
Figure 5. Impact of the hydrocyclones operation on the overflow d80 and circuit gold recovery
The increase of pulp density for feeding hydrocyclones reduces the pressure drop thus increases the d80 of the flotation feed (see Figure 5). However the overall gold recovery is not significantly impacted, showing that this range of size is around the optimal liberation size.
Another supervisor cycle is run without regulating the flotation feed density. It appears that the recovery is increasing when the feed pulp density increases (see Figure 6). This is due to a lower dilution thus a higher residence time of the pulp in the flotation cells. The residence time is then the limiting factor of the circuit.
Figure 6. Impact of the hydrocyclones operation on the overflow d80 and circuit gold recovery (no regulation)
Variation of the number of cells
The supervisor is run again for testing the modification of the number of flotation cells. For this test the %-solids of the hydrocyclone feed is set to 40 % which corresponds to a flotation feed with 25.3 % solids and a d80 of 85µm.
Figure 7. Evolution of the grade-recovery curves versus the number of cells
In Figure 7, each curve corresponds to a given number of cleaning cells (from 3 to 10). The different points of a curve correspond to different numbers of rougher cells (from 10 to 20). It appears, as anticipated, that the recovery largely depends on the number of roughing cells, while the evolution of grades is more dependent on the number of cleaning cells. The economic impact has then to be estimated considering the investment and operating costs of additional cells and the revenue generated by higher recoveries and grades.
Process simulator: a tool of interest for exploring the sensitivity of a circuit
This paper shows the interest of simulation for exploring the sensitivity of a circuit. If the final decision has to be taken considering economic factors, the technical elements provided by such a tool largely assists the engineers in the selection of the best options.
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