Integrated Intelligent Energy ›› 2022, Vol. 44 ›› Issue (3): 58-62.doi: 10.3969/j.issn.2097-0706.2022.03.009

• Intelligent Power • Previous Articles     Next Articles

Prediction on tube wall temperatures of boiler heating surfaces based on artificial intelligence

YAN Xinchun1(), CAO Huan1(), HUA Yunpeng2,*()   

  1. 1. Hebei Zhuozhou Jingyuan Thermal Power Company Limited,Zhuozhou 072750,China
    2. School of Energy and Power Engineering,North China Electric Power University,Beijing 102206,China
  • Received:2021-09-02 Revised:2021-09-13 Online:2022-03-25 Published:2022-03-28
  • Contact: HUA Yunpeng E-mail:171457409@qq.com;caohuanemail@163.com;2909108339@qq.com

Abstract:

In order to accurately predict the wall temperature of a boiler superheater at its outlet, the influencing factors for the heating surface temperature of the supercritical unit are analyzed. Then, according to the gray correlation analysis on the correlation degree between the influencing factors and metal wall temperature through, ten variables with correlation degree over 0.7 are chosen as the training samples. According to the variation characteristics of the data samples from the thermal power plant, through sliding window data judgment, multiple input variables in the extracted stable load sections are clustered to obtain cleaned data samples. The prediction model of the metal wall temperature is constructed through LSTM neural network. Making prediction on the heating surface temperature of a 350 MW supercritical unit, the maximum error between the predicted result and the measured value is 4.9 ℃, which proves the effectiveness of the model.

Key words: AI, LSTM, BP neural network, gray correlation analysis, boiler, superheater, overheating, prediction model

CLC Number: