东南大学学报（英文版）2019，Vol.35Issue(2)：179-184,6.DOI:10.3969/j.issn.1003-7985.2019.02.006
基于偏最小二乘和超限学习机结合的电站锅炉NOx排放建模
NOx emission model for coal-fired boilers using partial least squares and extreme learning machine
摘要
Abstract
To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares (PLS) and the extreme learning machine (ELM) for the establishment of the NOx emission model of utility boilers is proposed.First,the initial input variables of the NOx emission model are determined according to the mechanism analysis.Then,the initial input data is extracted by PLS.Finally,the extracted information is used as the input of the ELM model.A large amount of real data was obtained from the distributed control system (DCS) historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model.The modeling performance of the PLS-ELM was compared with that of the back propagation (BP) neural network,support vector machine (SVM) and ELM models.The mean relative errors (MRE) of the PLS-ELM model were 1.58％ for the training dataset and 1.69％ for the testing dataset.The prediction precision of the PLS-ELM model is higher than those of the BP,SVM and ELM models.The consumption time of the PLS-ELM model is also shorter than that of the BP,SVM and ELM models.关键词
NOx排放/偏最小二乘/超限学习机/电站锅炉Key words
NOx emission/partial least squares/extreme learning machine/coal-fired boiler分类
能源科技引用本文复制引用
董泽,马宁,李长青..基于偏最小二乘和超限学习机结合的电站锅炉NOx排放建模[J].东南大学学报（英文版）,2019,35(2):179-184,6.基金项目
The National Natural Science Foundation of China (No.71471060),Natural Science Foundation of Hebei Province (No.E2018502111). （No.71471060）
