Cyclone separators are commonly used for gas-solid separation in a gas field.It is of great significance to accurately predict the separation efficiency of a cyclone separator in order to guide its structure design and optimiza-tion.On the basis of correlation analysis of data sets,factor analysis(FA)was used to simplify the variables to re-duce the complexity of the prediction model,and the improved salp swarm algorithm(ISSA)was used to optimize the model parameters of projection pursuit regression(PPR)to form a combinatorial optimization model.The re-sults show that the ten variables in the original dataset can be simplified and merged into four common factors by the FA model,representing the effects of size parameters,particle settling characteristics,particle trajectories and equivalent particle size on separation efficiency.Compared with semi-empirical models and other machine learning models,our new combined model has advantages in prediction accuracy and training time.The MAE on test sam-ples was 0.005 91,and the R2 reached 0.995,demonstrating the accuracy,robustness and generalization of the model for small samples and nonlinear data analysis.
维普
万方数据