3.2.1. The Rough Separation
Particle size analysis was conducted on the underflow and overflow of the hydrocyclone postseparation. The obtained results are depicted in
Figure 4. It is evident from the data that the overflow particle size for One-stage 5 and 9 exhibited relatively fine characteristics, with D
80 (the volume of particles smaller than this diameter accounts for 80% of the volume of all particles) values of 4.88 μm and 5.45 μm, respectively. The underflow particle sizes for One-stage 2 and 6 appeared coarse, with D
80 particle sizes of 8.2 μm and 8.60 μm, respectively.
Table 6 presents the particle size (D
80) property and yield data for the overflow and underflow of the hydrocyclone. It is evident from the table that One-stage 1, 3, 5, and 8 exhibited relatively high overflow yields. Among these four experimental groups, One-stage 5 demonstrated the highest overflow yield and the smallest overflow D
80, suggesting a higher content of palygorskite. From this perspective, One-stage 5 can be considered the optimal group for the rough separation.
It is important to highlight that the underflow D
80 of all the one-stage experiments did not show a significant deviation from the D
80 of the raw ore (
Figure 4). Furthermore, the overflow yield was not high. These observations suggest that a considerable number of palygorskite particles were not effectively recovered but instead reported to the underflow. Hence, it is crucial to implement a scavenging process for the underflow in order to enhance the recovery of palygorskite particles.
The above results were analyzed based on the particle size and yield of the separated product. Next, further evaluation was required to assess the significance of the orthogonal experiment and the separation efficiency of the hydrocyclone.
The significance of the factors in the orthogonal tests was evaluated by analyzing the overflow D80 as the criterion for analysis. The results indicate that the overflow D80 decreased with an increase in the underflow port diameter. As the feeding pressure increased, the overflow D80 initially decreased and then increased. Moreover, with an increase in feed concentration, the overflow D80 decreased.
The analysis reveals that the underflow port diameter plays a significant role in determining the cone ratio of the hydrocyclone and the yield of the overflow [
20]. Increasing the underflow port diameter leads to the discharge of more coarse and medium-sized particles through the underflow port, resulting in a decrease in both the yield and particle size of the overflow.
The feeding pressure is another crucial parameter that affects the classification performance of the hydrocyclone. Insufficient feeding pressure leads to a low centrifugal force within the hydrocyclone, making it difficult to achieve satisfactory classification. Conversely, excessively high feeding pressure or low feed concentration can create an unstable hydrodynamic environment within the hydrocyclone, thereby deteriorating the classification efficiency. Therefore, it is essential to carefully select an appropriate feeding pressure and feed concentration to optimize the hydrocyclone’s performance.
A significance analysis of the three factors (underflow port diameter, feeding pressure, and feed concentration) was conducted, and the results, as shown in
Table 7, indicate that feed concentration has the highest significance, followed by the underflow port diameter, while feeding pressure has the lowest significance. These findings are consistent with Yu Jianfeng’s conclusions [
29].
The classification performance of the hydrocyclone was evaluated via the particle size distribution rate according to nine sets of experimental results, as shown in
Figure 5. According to
Figure 5, the classification performance was evaluated by classification particle size, classification efficiency, and mismatched material, as shown in
Table 8. The specific calculation formulas are as follows:
η—Classification efficiency, %
Ec—The recovery efficiency of coarse particle, %
Ef—The recovery efficiency of fine particle, %
γu—Underflow yield, %
uc—Coarse particle content in underflow, %
Fc,r—Coarse particle content in calculated feed, %
Ff,r—Fine particle content in calculated feed, %
uf—Fine particle content in underflow, %
- 2.
Average mismatched material calculation formula:
PEm—Average mismatched material
PEu—Upper mismatched material
PEL—Lower mismatched material
S75—Particle size corresponding to a classification efficiency of 75% on the particle size distribution rate curve
Sp—Partition size corresponding to a classification efficiency of 50% on the particle size distribution rate curve
S25—Particle size corresponding to a classification efficiency of 25% on the particle size distribution rate curve.
It was found that the overall actual classification particle size of the nine groups of experiments was quite different. The classification particle sizes of One-stage 6 and 9 were small, and One-stage 1 had the largest actual classification particle size. The classification efficiency of One-stage 1, 2, and 5 was large, and for One-stage 8 and 9 was small, while One-stage 7 had no classification effect. It could be seen from
Figure 5 that only the distribution rate curves of One-stage 1, 2, 3, 5, 6, and 9 were complete, and the upper and lower mismatched material under these experimental conditions could be completely obtained. For the other experiments, the classification effect was weak, and the mismatched material could not be obtained. It could be seen from
Figure 5 that the curve of One-stage 7 did not cross the horizontal line on which the distribution rate was 50%, so it had no classification effect. The curves of One-stage 4 and 6 did not cross the horizontal line on which the distribution rate was 75%, so the upper mismatched material could not be obtained. The results showed that among the six groups of experiments that had an effective classification effect, One-stage 9 had the least mismatched material, but their classification efficiency was low.
Based on the analysis above, it can be concluded that One-stage 5 had the largest overflow yield and the smallest classification particle size. Although its actual classification particle size was large, its classification efficiency was high. Therefore, One-stage 5 was the most effective group for the rough separation. However, in order to improve the recovery of palygorskite, underflow scavenging is required.
3.2.2. Underflow Scavenging
Particle size analysis was conducted for the underflow and overflow of the hydrocyclone after scavenging. The results are presented in
Figure 6. It can be observed that the overflow particle size of Two-stage 9 was fine, with a D
80 of 3.40 μm. The difference in overflow particle size among the other groups was relatively small. The underflow particle sizes of Two-stage 2 and Two-stage 6 were coarse, with D
80 particle sizes of 9.93 μm and 11.38 μm, respectively.
Table 9 displays the particle size (D
80) property and yields of the total overflow and underflow of the hydrocyclone after two-stage separation. It can be observed that the overflow yields of Two-stage 1, 4, and 5 were relatively high, similar to those observed in the rough separation. The overflow yield for all experimental conditions was approximately 70%, with the highest being 78.04% in Two-stage 5. In terms of palygorskite recovery, Two-stage 5 exhibited the optimal separation performance. However, it is worth noting that the overflow D
80 particle size for Two-stage 5 was coarse, while the underflow D
80 particle size was fine.
Figure 7 and
Table 10 present the particle size distribution rate curve and classification performance of the hydrocyclone experiments for underflow scavenging. It was observed that Two-stage 1 had a larger actual classification particle size, while Two-stage 9 had a smaller classification particle size. The classification efficiency was high for Two-stage 1, 6, and 9, while Two-stage 7 showed no classification effect. From
Figure 7, it can be seen that only the distribution rate curves of Two-stage 1, 2, 3, 4, 5, 8, and 9 are complete, indicating that the upper and lower mismatched materials could be completely obtained under these experimental conditions. However, for the other experiments, the classification effect was weak, and the mismatched material could not be obtained. The explanation for this phenomenon has been previously elucidated in the rough separation of the report, thus there is no need to reiterate it. The results demonstrate that Two-stage 8 had the least amount of mismatched material, but its classification efficiency was low.
Based on the above analysis, it can be found that Two-stage 2 had a relatively coarse underflow particle size, and the difference between underflow particle size and overflow particle size was large. Two-stage 6 displayed a coarse underflow particle size, along with higher classification efficiency and a lower average of mismatched material. Two-stage 9 demonstrated a finer overflow particle size, a finer classification particle size, and a higher classification efficiency. Consequently, Two-stage 2, Two-stage 6, and Two-stage 9 emerged as the optimal groups for underflow scavenging.