On Data-driven Soft Sensor of Nox Emission in Power Station Boiler
To predict the NOx emission level precisely in power station boiler,a data-driven method is presented to solve the problem of the absence of precision model.In this method,the operating data is utilized sufficiently to establish the model based on statistical learning theory.Firstly,the data on field is cleaned to avoid noise and abnormal value.To find the optimal model parameters,genetic algorithm is used for model optimization.So a modeling method is presented to describe the NOx emission level during varying load.The simulation is implemented on a 300MW coal-fired unit with several different working loads,and compared to the way based on neural network,the results show that the model can predict the NOx emission more precisely, which provides the foundation for further operating optimization.
HUANG Jingtao CHI Xiaomei JIANG Aipeng MAO Jianbo
Electronic &Information Engineering College,Henan University of Science and Technology,Luoyang 47100 School of Automation,Hangzhou Dianzi University,Hangzhou 310018,P.R.China Zhejiang Electric Power Test &Research Institute,Hangzhou 310014,P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）