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A bubble generator is a crucial component in the flotation system, and the bubble size is directly related to bubble mineralization. This paper focused on the microbubble size control of a swirl flow microbubble generator. The computational fluid dynamics coupled population balance model (CFD-PBM) was developed to evaluate the flow characteristic and the bubble generation performance of the microbubble generator with five novel internal baffles. The results showed that the baffle could improve the bubble dispersion without extra energy loss, and the single helical baffle could generate tiny bubbles with a Sauter mean diameter of 0.36 mm at a liquid flow rate of 2000 L/h. The baffle could limit the positive axial velocity and hinder the tangential velocity effect in the straight pipe section, weakening the swirl flow effect. Hence, although the baffle slightly reduced the pressure gradient magnitude and the turbulent intensity, it still presented an effective improvement in the bubble dispersion.

Flotation has garnered increasing attention as an indispensable technology for oily wastewater treatment for sustainable and green development. Bubbles are an essential element during the flotation process. Hence, it is important to examine bubble size control in a bubble generator employed in a flotation deoiling system. Herein, a swirling-flow bubble generator was studied via experiments, simulation, and machine learning methods. As the liquid Reynolds number increased from 16,047 to 32,095, the Sauter mean diameter decreased by 27.3%. Compared to the other open-hole schemes, the spiral-bottom open scheme was advantageous in reducing the bubble size, while decreasing the number of open holes reduced the bubble size further. When the surface tension changed from 0.06 to 0.03 N/m, D 32 decreased by 27.1%. Decreasing the height diameter ratio and increasing the angle between adjacent holes helped generate more smaller bubbles. However, for a given perforation velocity, the bubble size was insensitive to the size of the open holes. The relationship between D 32 and the involved variables was determined accurately by a support vector machine. Finally, a swirling-flow bubble generator with suitable specifications was installed in the flotation deoiling system to achieve an acceptable deoiling performance.