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 8-$\mathrm{\mu m}$ 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.

压降和分离效率是气体颗粒分离器设计中的两个关键性能参数。在这项研究中，使用响应面方法（RSM）和计算流体力学（CFD）对轴流式气固分离器进行多目标优化，以最大程度地降低压降并提高分离效率。首先，通过实验验证了雷诺应力模型和离散相模型预测的气流和颗粒数值模拟的准确性。其次，建立筛选实验以从9个感兴趣的因素中选择重要因素。在RSM中使用中央复合设计研究了四个因素，并对两个响应进行了二阶响应曲面建模。通过期望函数方法获得结构优化设计。最后，解释了原始设计和优化设计之间的差异。与原始设计相比，经过优化的设计提高了8的去除效率$\mathrm{微米}$颗粒减少100％，静压下降69.32％。在分析流场和颗粒轨迹的基础上，解释了性能变化的原因。基于静态压降和分离效率之间的权衡，获得了优化的设计。