Open AccessResearch on Prediction of Vertical Water Wall Temperature of Power Station Boiler Based on Deep Learning and Expert Experience
Author(s) -
Xian-tao Du,
Yujia Cui,
Minghao Wang,
Liang-wei Xia,
Qiang Yu,
Ying Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1922/1/012011
Subject(s) - boiler (water heating) , power station , artificial neural network , engineering , artificial intelligence , deep learning , computer science , waste management , electrical engineering
Based on deep learning and expert experience to predict the temperature of the vertical water wall of a power station boiler, the research is based on real-time or offline data collected by the power station boiler, and the data is processed through expert experience and algorithms to divide the data set. Based on three deep learning algorithms, an algorithm learning machine is established. Through this learning machine and expert experience, the temperature of the vertical water wall of the power station boiler is predicted. At the same time, this paper also realizes rolling real-time prediction of vertical water wall temperature. The results show that the model can accurately predict the temperature of the vertical water wall, thereby providing reference guidance for the operation of power station boilers.