Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)
Abstract 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 u...
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| Formatua: | Artikulua |
| Hizkuntza: | English |
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Wiley
2023-08-01
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| Saila: | Energy Science & Engineering |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1002/ese3.1498 |
| _version_ | 1827577745830313984 |
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| author | Lixin Zhang Jie Chen Shunan Zhao Yongbao Chen Huijuan Song Jingnan Liu |
| author_facet | Lixin Zhang Jie Chen Shunan Zhao Yongbao Chen Huijuan Song Jingnan Liu |
| author_sort | Lixin Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T21:32:14Z |
| format | Article |
| id | doaj.art-7042a977a3374b51a1693b1bf3b04e4d |
| institution | Directory Open Access Journal |
| issn | 2050-0505 |
| language | English |
| last_indexed | 2024-03-08T21:32:14Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj.art-7042a977a3374b51a1693b1bf3b04e4d2023-12-21T06:55:47ZengWileyEnergy Science & Engineering2050-05052023-08-011182885289810.1002/ese3.1498Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN)Lixin Zhang0Jie Chen1Shunan Zhao2Yongbao Chen3Huijuan Song4Jingnan Liu5Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai ChinaAbstract 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.https://doi.org/10.1002/ese3.1498cooling towerfillerRBF ANNresistance performancethermal performance |
| spellingShingle | Lixin Zhang Jie Chen Shunan Zhao Yongbao Chen Huijuan Song Jingnan Liu Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) Energy Science & Engineering cooling tower filler RBF ANN resistance performance thermal performance |
| title | Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) |
| title_full | Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) |
| title_fullStr | Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) |
| title_full_unstemmed | Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) |
| title_short | Research of cooling tower filler based on radial basis function artificial neural network (RBF ANN) |
| title_sort | research of cooling tower filler based on radial basis function artificial neural network rbf ann |
| topic | cooling tower filler RBF ANN resistance performance thermal performance |
| url | https://doi.org/10.1002/ese3.1498 |
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