基于混合改进XGBoost的炼化装置进出料管道腐蚀预测方法OA

To achieve accurate prediction of corrosion rates,this study proposed a corrosion rate modeling method based on joint feature selection and a hybrid enhanced XGBoost algorithm.Firstly,kernel principal component analysis(KPCA)was employed to evaluate the linear characteristics of process parameters.Then,Pearson correlation coefficients and the maximal information coefficient(MIC)were utilized to assess feature structures and select the most relevant input variables.Subsequently,an XGBoost-DART model incorporating a Dropout mechanism was constructed to improve generalization and robustness.To further enhance prediction accuracy,a hybrid dynamic perturbation strategy grey wolf optimization(HDPSGWO)algorithm was applied to optimize key hyperparameters.Finally,the model was validated on an engineering dataset from a typical refining unit in Northwest China,achieving excellent performance on the test set with RMSE=0.003044 and R²=0.9586,significantly outperforming traditional SVM and non-optimized XGBoost models.