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The Scientific World Journal
Volume 2014 (2014), Article ID 438782, 10 pages
Research Article

Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence

1College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, China
2Down Hole Company, Chuanqing Drilling Company, CNPC, Chengdu 610051, China
3Dongsheng Group Co., Ltd. of Shengli Oilfield, Dongying 257000, China

Received 30 November 2013; Accepted 12 February 2014; Published 18 March 2014

Academic Editors: F. J. Keil and K. I. Ramachandran

Copyright © 2014 Mingzhong Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.