Multi-objective optimization of axial-flow-type gas-particle cyclone separator using response surface methodology and computational fluid dynamics

Pressure drop and separation efficiency are two critical performance parameters in the design of gas-particle separators. In this study, multi-objective optimization of an axial-flow-type gas-particle cyclone separator is conducted using the response surface methodology (RSM) and computational fluid dynamics (CFD) to minimize the pressure drop and maximize the separation efficiency. First, the accuracy of numerical simulation of airflow and particles predicted by the Reynolds stress model and discrete phase model is verified by experiments. Second, a screening experiment is set up to select the significant factors out of nine factors of interest. Four of the factors are studied using a central composite design in the RSM, and second-order response surface modeling is performed for two responses. A structural optimized design is obtained by the desirability function approach. Finally, the differences between the original and optimized designs are explained. Compared with the original design, the optimized design increases the removal efficiency for 81.4.m particles by 100% and the static pressure drop by 69.32%. Based on the analysis of the flow field and the particle trajectory, the cause of performance change is explained. The optimized design is obtained based on a trade-off between static pressure drop and separation efficiency.