Population Balance Modeling and Multi-Response Optimization of a Swirling-Flow Nanobubble Generator

A novel model is proposed to optimize a swirling-flow nanobubble generator utilizing a combined model of computational fluid dynamics-population balance method (CFD-PBM) coupled model and response surface method (RSM). The CFD-PBM coupled model was validated by experiments based on the bubble size distribution and mass transfer. The validated model was utilized as an input for optimization using RSM. Optimization involved three factors and four target responses obtained by applying a central composite design (CCD). The adequacy of the models was evaluated by analysis of variance. Based on the optimization results, the proposed model can produce an optimized and feasible solution as a reference in the design of a nanobubble generator and can be applied further on a larger scale.

Automated summary

In this study, a novel model was proposed to optimize a swirling-flow nanobubble generator. Through the combination of a computational fluid dynamics-population balance method (CFD-PBM) coupled model and a response surface method (RSM), the model was validated by experiments and then utilized for optimization. The proposed model can serve as a reference in the design of nanobubble generators and can be applied on a larger scale.