石油与天然气化工2025，Vol.54Issue(1)：9-17,9.DOI:10.3969/j.issn.1007-3426.2025.01.002
基于集成学习算法的尾气处理装置SO2排放预测模型
Sulfur dioxide emissions predictive model of tail gas treatment unit based on ensemble learning algorithm
摘要
Abstract
Objective The aim is to accurately predict the emission mass concentration of sulfur dioxide(SO2)in the flue gas of the tail gas treatment unit of natural gas purification plants.Method The data set was constructed using 44 000 hourly tail gas treatment daily report data from a natural gas purification plant from 2018 to 2023.Data processing was conducted,and 27 important features were extracted using importance analysis methods.Aiming at the prediction task of SO2 emission mass concentration in flue gas,three ensemble learning algorithms—namely,Random Forest,Gradient Boost,and XGBoost—and a Support Vector Machine(SVM)based on a Radial Basis Function(RBF)kernel were used to model the process instead of simulation models.Result The prediction accuracy of the three ensemble learning models was higher than the SVM single model.Among them,the Random Forest model exhibited the best performance,with a coefficient of determination of 0.89 and a mean square error of 1 250.59.Relative to a data set containing 8 800 real test set samples,its prediction deviation was 9.86%.Compared to the Random Forest model without data treatment,its coefficient of determination increased by 61.82%.Conclusion The Random Forest model has practical production application value in accurately predicting SO2 emission mass concentration of the tail gas treatment unit and can provide reliable model support for the subsequent process parameter optimization of the tail gas treatment unit.关键词
天然气净化/硫磺回收/尾气处理/二氧化硫排放/预测模型/集成学习算法Key words
natural gas purification/sulfur recovery/tail gas treatment/sulfur dioxide emission/prediction model/ensemble learning algorithm引用本文复制引用
张宝东,杜支文,闫昭,侯磊..基于集成学习算法的尾气处理装置SO2排放预测模型[J].石油与天然气化工,2025,54(1):9-17,9.
