Open AccessPrediction of outlet air temperature of direct air condenser based on BP neural network
Author(s) -
Jianyun Bai,
Xinyu Meng,
Yan Qu,
Feng Geng
Publication year - 2019
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 - 10.1088/1742-6596/1303/1/012014
Subject(s) - condenser (optics) , artificial neural network , air temperature , airflow , air cooling , environmental science , meteorology , constant air volume , flow (mathematics) , engineering , simulation , computer science , mechanical engineering , artificial intelligence , mechanics , geography , light source , physics , optics
At present, the dynamic parameter monitoring technology of the direct air cooling system is relatively weak. According to the study of outlet air flow temperature characteristics, this paper proposes a direct air condensing unit outlet air flow temperature prediction model based on BP neural network for studying dynamic parameters of direct air cooling units. The historical data of each factor is collected from the on-site DCS system. After the data is preprocessed, the BP neural network is used to establish the air temperature prediction model of the air condenser. Finally, the model is validated by the data that does not participate in the training. The model can predict the outlet air temperature of the air condenser under different working conditions, providing a basis for the next step of developing dynamic parameter monitoring technology, and also provide a reference for the cleaning cycle of the air condenser.