热力发电2024，Vol.53Issue(12)：86-92,7.DOI:10.19666/j.rlfd.202404086
基于集成学习的CFB锅炉氮氧化物排放质量浓度在线建模研究
Research on online modeling of nitrogen oxides emission mass concentration of circulating fluidized bed boiler based on ensemble learning
摘要
Abstract
In view of the complex variation law and strong autocorrelation of nitrogen oxides emission mass concentration of circulating fluidized bed(CFB)boiler,by using relevant variables and their historical information,ensemble learning online models of nitrogen oxides emission mass concentration are established.The ensemble learning online models include the autoregressive integrated moving average(ARIMA),random forest(RF),gradient boosting(GBDT),and eXtreme gradient boosting(XGBoost)model.The prediction results are compared and selected,among which the GBDT regressor is the best.In order to further improve the prediction effect of the model,a GBDT differential regression model is established by combining the first-order difference with the GBDT regression algorithm.The tests show that the established GBDT differential regression model has better prediction performance than the aforementioned models.The mean squared error of the predicted value is 20.2%lower than that of the simple GBDT regressor,and 46.5%lower than that of the online sequential extreme learning machine(OS-ELM)model used in the reference.The online model also fully considers avoiding the influence of the instrument purge process,and has strong practicability.关键词
循环流化床锅炉/氮氧化物/ARIMA/集成学习/GBDT差分在线模型Key words
CFB boiler/nitrogen oxides/ARIMA/ensemble learning/GBDT differential online model引用本文复制引用
吴家标,刘兴高..基于集成学习的CFB锅炉氮氧化物排放质量浓度在线建模研究[J].热力发电,2024,53(12):86-92,7.基金项目
国家重点研发计划项目(2021YFC2101100) （2021YFC2101100）
国家自然科学基金项目(62073288,12075212)National Key Research and Development Program(2021YFC2101100) （62073288,12075212）
National Natural Science Foundation of China(62073288,12075212) （62073288,12075212）
