Pressure drop (Δp) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of Δp and η. The validation results of CFD showed 0.5% and 2% errors for Δp and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D_x, inlet width a, inlet height b and cone height H_co have a significant effect on η and Δp. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for Δp ($R^2=99.7$, RMSE = 0.102) $R_{\text{adjusted}}^2=98.99$, RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction ($\eta$: ±0.0065, Δp: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for Δp ${\text{d}}_{50}$ and $\eta$, respectively.

压降（Δp）和收集效率（η）用于评估旋风分离器的分离性能。在这项研究中，我们使用响应面方法（RSM），反向传播神经网络（BPNN）和数据处理组（GMDH）网络进行了气旋模型的比较研究，以开发最佳的预测性气旋模型。此外，我们使用遗传算法（GA）进行了多目标优化，以最大化模型和最小化模型。使用CFD代替实验方法来获得用于Δp和η建模的估计值。CFD的验证结果表明，与实验数据相比，Δp和η的误差分别为0.5％和2％。其次，对10个旋风分离器的几何参数进行了实验设计（DOE）分析以获得重要的几何参数。D _x，入口宽度a，入口高度b和圆锥高度H _co对η和Δp有显着影响。但是，几何参数之间的相互作用影响很小。在25个CFD训练集的基础上，建立了RSM，BPNN和GMDH的气旋模型。通过25个CFD测试装置比较了气旋模型的预测性能结果。GMDH方法对Δp（${\mathrm{[R}}^2=99.7$，RMSE = 0.102） ${\mathrm{[R}}_{\text{已调整}}^2=98.99$，RMSE = 0.0119），而不是RSM，BPNN气旋模型。另外，进行不确定性分析以估计旋风模型的定量性能。结果表明，GMDH模型的不确定性宽度达到了最佳预测（$\eta$：±0.0065，Δp：±0.0188）。最后，应用遗传算法同时优化GMDH模型。GA产生了70个非主要解决方案。使用CFD验证了五个最佳点的重现性。权衡最佳点显示出Δp分别提高了24.31％，18％和8.79％${\text{d}}_{50}$ 和 $\eta$， 分别。