BAI Jiliang;LI Bin;ZHU Jinqi;HAN Ping;WU Wanzhu;XIAO Xianbin;

• 1:Guoshen Company of CHN Energy
• 2:National Engineering Laboratory of Biomass Power Generation Equipment
• 3:North China Electric Power University

Abstract：

The carbon content of fly ash is an important indicator that affects the thermal efficiency of the boiler and the economy operation of the units. An improved BP neural network model based on the Levenberg-Marquardt algorithm was established to predict the carbon content of fly ash in a 150 MW CFB boiler,including a parent model and three sub-models. The parent model selected seven parameters such as the technical analysis and low calorific value of coal as input parameters. The sub-models investigated the coal quality deviation parameters on other input parameters of the parent model. The improved BP neural network was used to train the samples to predict the carbon content of fly ash.The training results were compared with the results obtained by traditional polynomial regression methods or empirical methods. The results show that the correlation coefficient R2 of BP neural network,Polynomial linear regression,and Polynomial nonlinear regression are 0.957 1,0.605 1,0.766 7,respectively,and the relative mean error RME are 4.84%,17.02%,12.46%,respectively. The improved BP neural network model has higher prediction accuracy and better generalization ability for fly ash carbon content.

Key Words： carbon content of fly ash;CFB boiler;BP neural network;coal quality;predictive model

Abstract:

Keywords:

Foundation: 国家重点研发计划资助项目(2016YFB0600205)

Authors: BAI Jiliang;LI Bin;ZHU Jinqi;HAN Ping;WU Wanzhu;XIAO Xianbin;

DOI: 10.13226/j.issn.1006-6772.20090301

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