An experimental modeling of cyclone separator efficiency with PCA-PSO-SVR algorithm

Abstract Accurate prediction of the complicated nonlinear relationship among the grade efficiency, geometrical dimensions, and operating parameters based on limited experimental data is the most effective way to design a high-efficiency cyclone separator. Herein, a hybrid PCA-PSO-SVR model is proposed to predict the grade efficiency of cyclone separators with the operating parameters based on 217 sets of experimental data provided in the literature. The experimental data are preprocessed using the random sampling technique together with the normalization method and principal component analysis (PCA) at first; subsequently, the particle swarm optimization (PSO) algorithm is incorporated to optimize the parameters for the support vector regression (SVR), including the penalty factor C, kernel function parameter g, and insensitive loss e. Finally, the SVR model with the optimized parameters is trained with 80% pretreatment data, and the generalization ability of the model is tested with the remaining 20% data. The mean squared error of the test sets is 6.948 × 10−4 with a correlation coefficient of 0.982. The comparison results show that the PCA-PSO-SVR model has higher accuracy, better generalization ability, and stronger robustness than the existing models for predicting the cyclone separator efficiency in the case with only a few experimental data.