山东电力技术2026，Vol.53Issue(1)：88-97,10.DOI:10.20097/j.cnki.issn1007-9904.240431
基于模糊聚类和深度置信神经网络的燃煤机组NOx浓度预测方法研究
Research on NOx Concentration Prediction Method of Coal-fired Units Based on Fuzzy Clustering and Deep Belief Neural Network
摘要
Abstract
Aiming at the prediction of NOx concentration at the inlet of the selective catalytic reduction(SCR)reactor in coal-fired units,a NOx concentration prediction method based on fuzzy clustering and deep belief neural network is proposed.Firstly,the variables that have a greater impact on NOx concentration are selected through mechanism analysis and feature weight analysis.Secondly,the fuzzy clustering algorithm is used to partition the operating conditions of the unit,and the distribution of NOx concentration at the inlet of the SCR under different operating conditions is analyzed.Finally,based on the selected auxiliary variables,historical operating data under different operating conditions are extracted,and a NOx concentration prediction model is established based on the deep belief neural network(DBN).The method is verified by actual data from a 660 MW circulating fluidized bed boiler,and the results show that the NOx prediction model based on fuzzy clustering and deep belief neural network can accurately predict the NOx concentration in the nonlinear time-series combustion process.关键词
模糊聚类/深度置信神经网络/NOx浓度Key words
fuzzy clustering/deep belief neural network/NOx concentration分类
信息技术与安全科学引用本文复制引用
冯林魁,冯垚飞,卢可,谢生璐,刘科..基于模糊聚类和深度置信神经网络的燃煤机组NOx浓度预测方法研究[J].山东电力技术,2026,53(1):88-97,10.基金项目
国网甘肃省电力公司科技项目(LNKJ-QT-20240218-YF01).Science and Technology Project of State Grid Gansu Electric Power Company(LNKJ-QT-20240218-YF01). （LNKJ-QT-20240218-YF01）
