The double vortex-finder hydrocyclone formed by a coaxial insertion of an internal vortex-finder with a smaller diameter inside the conventional single vortex-finder used to obtain two kinds of products from the internal and external overflows in one classification has attracted wide attention. To further improve the classification performance of the hydrocyclone, the effects of the internal vortex-finder diameter and length on the classification performance were studied by numerical simulation and response surface modeling with the behavior of fluid and particle motion in the double vortex-finder hydrocyclone as the research object. The results showed that the split ratio and pressure drop of internal and external overflow increased with the diameter of the internal vortex-finder. The classification performance was optimal when the diameter ratio of internal and external overflow was 0.88, the yield of −20 μm particles was more than 80.0%, and the highest was 95.0%. Increasing the internal vortex-finder length could reduce the coarse particle content and improve the classification accuracy of the internal overflow product. When the length of the internal vortex-finder is larger than 80 mm, the +30 μm yield was lower than 20.0%, and the maximum k value was 16.3%; the k is the significant factor used to characterize the effectiveness of −20μm particle collection. The response surface modeling revealed that the internal vortex-finder diameter was the most important factor affecting the distribution rate of internal overflow. This paper is expected to advance the development of the classification industry.

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In order to clarify the influence of feed rate on a hydrocyclone flow field, numerical simulation was employed to model the influence of feed rate on the pressure field, velocity field, air column, turbulent kinetic energy, and split ratio. The results revealed that static pressure, tangential velocity, and radial velocity increased with an increase in the feed rate. When the feed rate at the inlet increases from 1 m/s to 5 m/s, the static pressure increases from 5.49 kPa to 182.78 kPa, tangential velocity increases from 1.97 m/s to 11.16 m/s, and radial velocity increases from 0.20 m/s to 1.16 m/s demonstrating that a high feed rate facilitated the strengthening separation of the flow field. Meanwhile, with the increase in the feed rate, the split ratio of the hydrocyclone decreased, indicating that the concentration effect of the hydrocyclone improved. Additionally, the formation time of the air column was reduced, and the flow field became more stable. Nevertheless, the axial velocity and the turbulent kinetic energy also increased with the increase in the feed rate, and the increase in the axial velocity reduced the residence time of the material in the hydrocyclone, which was not conducive to the improvement of separation accuracy. In addition, the increase in turbulent kinetic energy led to an increase in energy consumption, which was not conducive to the improvement of the comprehensive performance of the hydrocyclone. Therefore, choosing an appropriate feed rate is of great significance to the regulation of the flow field and the improvement of hydrocyclone separation performance.

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In the present research, we propose the use of a novel hydraulic classifier equipped with a W-shaped reflector to enhance classification performance. The effects of the structural dimensions of a W-shaped reflector on the flow field of a classifier and its classification performance were investigated using numerical simulations and experiments. The results demonstrate that the reflection of the W-shaped reflector results in the return of the feed material back to the classification cavity. After this, the materials are mixed with a rising water flow in order to avoid the settlement of particles. Thus, the particles can stay longer in the classification cavity, facilitating the generation of a suspension bed and effectively improving the classification efficiency and accuracy. Our data indicates that the overall classification efficiency of the classifier embedded with the W-shaped reflector was 11.19% higher than that of a traditional classifier. Our results provide a reference for classifier optimization.