Microbubble Dispersion Process Intensification Using Novel Internal Baffles

Wu, Yuxue; Chen, Hang; Song, Xingfu

doi:10.1021/acs.iecr.2c01984

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…The initial bubble size at the air inlet was 4 mm; note that it is an insensitive parameter for flow field and final bubble size. 14 The simulations were carried out on a Dell workstation equipped with Intel Xeon Gold 6240 CPUs with 36 physical cores and 192 GB memory. The number of time steps in the simulations was 2 × 10 5 .…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…The geometry of bubble generators has been optimized from the viewpoint of gas–liquid flow fields based on an Eulerian–Eulerian framework; such optimization can guide the design and fabrication of the bubble generator. , Furthermore, the computational fluid dynamics (CFD)-coupled population balance model (PBM) was recently developed for a swirling-flow bubble generator. Previously, , bubble breakup and coalescence models were developed to predict the bubble size. The turbulent dissipation rate primarily occurs downstream of the nozzle, where bubbles are generated.…”

Section: Introductionmentioning

confidence: 99%

“…8,12 Furthermore, the computational fluid dynamics (CFD)-coupled population balance model (PBM) was recently developed for a swirling-flow bubble generator. Previously, 13,14 bubble breakup and coalescence models were developed to predict the bubble size. The turbulent dissipation rate primarily occurs downstream of the nozzle, where bubbles are generated.…”

Section: ■ Introductionmentioning

confidence: 99%

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Computer-Aided Bubble Size Control for a Swirling-Flow Bubble Generator

Chen

Song

2023

ACS Sustainable Chem. Eng.

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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.

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Section: ■ Materials and Methodsmentioning

confidence: 99%