广东电力2025，Vol.38Issue(8)：1-11,11.DOI:10.3969/j.issn.1007-290X.2025.08.001
基于VIP-MIC-SBS变量筛选的火电厂烟气流量软测量研究
Research on Soft Measurement of Flue Gas Flow in Thermal Power Plants Based on VIP-MIC-SBS Hybrid Variable Selection
摘要
Abstract
The continuous emission monitoring system(CEMS),as an efficient and traceable approach,is gradually being applied in China's carbon measurement field.However,accurately monitoring flue gas flow is challenging due to monitoring data interruption and abnormality caused by the large diameter of chimneys,the complex flow fields,and issues such as maintenance of flowmeters and dust blockage.To address these challenges,this study proposes a soft measurement model for flue gas flow based on support vector machine(SVM),incorporating a hybrid variable selection strategy that integrates variable importance in projection(VIP),the maximal information coefficient(MIC)and sequential backward selection(SBS)algorithms.Based on the operating data from an F-class gas-steam combined cycle power generation unit,this study uses the VIP values to evaluate the significance of auxiliary variables,as well as combines MIC and SBS for redundancy elimination and variable set optimization.Thereby,the proposed approach enhances the prediction accuracy and generalization capability of the soft measurement model.The experimental results show that the SVM model outperforms the long short-term memory(LSTM)model and exhibits better generalization ability compared to the BP neural network.The model performance is the best with 12 selected auxiliary variables,and the root mean square error(RMSE)and mean absolute percentage error(MAPE)on the test set are lower,verifying the effectiveness of the variable selection method.Furthermore,under both steady and transient operating conditions,the proposed model maintains an average MAPE below 0.7%and exhibits a filtering effect on the predicted signals.关键词
烟气流量/软测量技术/变量投影重要性分析/最大信息系数/后向搜索/支持向量机Key words
flue gas flow/soft measurement technology/variable importance in projection(VIP)/maximal information coefficient(MIC)/sequential backward selection(SBS)/support vector machine(SVM)分类
信息技术与安全科学引用本文复制引用
邹祥波,熊凯,陈公达,刘泽明,陈创庭,卢志民,卢伟业,陈小玄,姚顺春..基于VIP-MIC-SBS变量筛选的火电厂烟气流量软测量研究[J].广东电力,2025,38(8):1-11,11.基金项目
国家重点研发计划项目(2021YFF0601001) （2021YFF0601001）
广东省能源集团有限公司科技项目(GEG/AJS-22-002) （GEG/AJS-22-002）
