Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)

The filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under spec...

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Bibliographic Details
Published in:Energy science & engineering 2023-08, Vol.11 (8), p.2885-2898
Main Authors: Zhang, Lixin, Chen, Jie, Zhao, Shunan, Chen, Yongbao, Song, Huijuan, Liu, Jingnan
Format: Article
Language:English
Subjects:
Artificial neural networks
Big Data
Chemical analysis
Cooling
cooling tower
Cooling towers
Data analysis
Empirical analysis
Experiments
filler
Fillers
Flow resistance
Heat transfer
Interpolation
Mass transfer
Mathematical analysis
Neural networks
Performance evaluation
Pressure drop
Radial basis function
RBF ANN
resistance performance
Salinity
Seawater
thermal performance
Thermal resistance
Water analysis
Water quality
Water temperature
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Summary:The filler is the core component of the cooling tower, filler performance refers to both its thermal and flow resistance characteristics, which use empirical formulas of tower characteristic N, volumetric mass transfer coefficient βxv, and pressure drop ΔP obtained through experimentation under specific conditions. However, the performance equations for identical countercurrent fillers can vary at different heights or seawater concentrations. Linear interpolation is the conventional method for obtaining the filler performance under different conditions, but its uncertainty limits the application. This paper explores the use of the radial basis function artificial neural network (RBF ANN) to analyze filler performance based on existing performance equations. The data set is generated by the filler performance equations. The results demonstrate that RBF ANN has a preferable prediction effect with high correlation (the determination coefficient R2 > 0.99) and prediction accuracy (the proportion of relative error within 10% N10 > 90%). Furthermore, the predicted results are consistent with the experimental results of the filler performance. Therefore, RBF ANN can accurately predict filler performance at varying heights and seawater concentrations, making it universal and providing a basis for cooling tower design.
ISSN:2050-0505
2050-0505
DOI:10.1002/ese3.1498