Research on Prediction of Vertical Water Wall Temperature of Power Station Boiler Based on Deep Learning and Expert Experience

Autor:	Xia Liangwei, Wang Minghao, Cui Yujia, Huang Ying, Qiang Yu, Du Xiantao
Rok vydání:	2021
Předmět:	Data set History Power station Computer science business.industry Deep learning Artificial intelligence business Computer Science Applications Education Marine engineering Boiler (water heating)
Zdroj:	Journal of Physics: Conference Series. 1922:012011
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1922/1/012011
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a6d2f669d04ee888aef0d1c7bd140b66 https://doi.org/10.1088/1742-6596/1922/1/012011 Zobrazit plný text záznamu