Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence
Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining bound...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/438782 |
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| author | Mingzhong Li Guodong Zhang Jianquan Xue Yanchao Li Shukai Tang |
| author_facet | Mingzhong Li Guodong Zhang Jianquan Xue Yanchao Li Shukai Tang |
| author_sort | Mingzhong Li |
| collection | DOAJ |
| description | Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results. |
| format | Article |
| id | doaj-art-6641b1e7ce5f49948b4c6b6ae10cfefb |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-6641b1e7ce5f49948b4c6b6ae10cfefb2025-02-03T05:43:49ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/438782438782Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial IntelligenceMingzhong Li0Guodong Zhang1Jianquan Xue2Yanchao Li3Shukai Tang4College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, ChinaCollege of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, ChinaCollege of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, ChinaDown Hole Company, Chuanqing Drilling Company, CNPC, Chengdu 610051, ChinaCollege of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, ChinaConsidering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.http://dx.doi.org/10.1155/2014/438782 |
| spellingShingle | Mingzhong Li Guodong Zhang Jianquan Xue Yanchao Li Shukai Tang Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence The Scientific World Journal |
| title | Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence |
| title_full | Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence |
| title_fullStr | Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence |
| title_full_unstemmed | Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence |
| title_short | Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence |
| title_sort | prediction of the wall factor of arbitrary particle settling through various fluid media in a cylindrical tube using artificial intelligence |
| url | http://dx.doi.org/10.1155/2014/438782 |
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