化工进展2026，Vol.45Issue(7)：3885-3896,12.DOI:10.16085/j.issn.1000-6613.2025-1144
基于混合改进XGBoost的炼化装置进出料管道腐蚀预测方法
Corrosion prediction method for inlet and outlet pipelines of refining plant based on hybrid-optimized XGBoost
摘要
Abstract
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.关键词
腐蚀速率预测/极端梯度提升/灰狼优化算法/炼化装置/硫酸烷基化Key words
corrosion rate prediction/extreme gradient boosting(XGBoost)/grey wolf optimizer(GWO)/refining and petrochemical units/sulfuric acid alkylation分类
能源科技引用本文复制引用
马铭骏,胡瑾秋,张来斌,陆宇航..基于混合改进XGBoost的炼化装置进出料管道腐蚀预测方法[J].化工进展,2026,45(7):3885-3896,12.基金项目
国家重点研发计划(2024YFC3013500). （2024YFC3013500）
