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
吴家标 1刘兴高2
作者信息
- 1. 浙江大学工业控制技术全国重点实验室,浙江 杭州 310027;浙江大学工业控制科学与工程学院,浙江 杭州 310027;丽水市杭丽热电有限公司,浙江 丽水 323010
- 2. 浙江大学工业控制技术全国重点实验室,浙江 杭州 310027;浙江大学工业控制科学与工程学院,浙江 杭州 310027
- 折叠
摘要
针对循环流化床锅炉氮氧化物排放质量浓度变化规律复杂、自相关性强等特点,利用有关变量及其历史信息,分别建立了氮氧化物排放质量浓度的整合移动平均自回归(ARIMA)和随机森林(RF)、梯度提升树(GBDT)、极致梯度提升树(XGBoost)等集成学习在线模型,并对预测效果进行对比择优,其中以GBDT回归器为最优.为了进一步改进模型的预测效果,提出将一阶差分与 GBDT 回归算法相结合,建立了 GBDT 差分回归模型.测试表明所建立的GBDT差分回归模型比前述模型具有更好的预测性能,其预测值的均方差比单纯 GBDT 回归器降低了 20.2%,并比参考文献采用的在线贯序极限学习机(OS-ELM)模型低 46.5%.所建的在线模型还充分考虑避免仪表吹扫过程的影响,具有较强的实用性.
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
出版年
2024
国家科技期刊平台
NSTL
万方数据