Modeling dynamic viscosity of n-alkanes using LSSVM technique
One of the important thermophysical properties is viscosity which expresses the resistance of fluid to flow. The least squares support vector machine (LSSVM) algorithm is proposed as a novel method for prediction of dynamic viscosity of different normal alkanes in a wide range of pressure
and temperature. As this study is purely computational, 228 experimental data points were gathered from literature for training and validation of the model. The outcomes of the LSSVM algorithm were compared with the actual data with acceptable average absolute relative deviation and the coefficient
of determination (R
2) of 1.014 and 0.9968, respectively. The comparisons showed that the predicting model has the potential of prediction of n-alkane dynamic viscosity in terms of pressure, temperature, and carbon number of n-alkane, so this strategy can be used as a simple
tool for predicting the behavior of reservoir fluids.
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Document Type: Research Article
Department of mechanical Engineering, Payame Noor University (PNU), Tehran, IranAQ2
Young Researcher and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Mathematics Department, Faculty of Basic Sciences, Khatam-ol-Anbia (PBU) University, Tehran, IRAN
Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Chemical Engineering Department, Amirkabir University of Technology, Mahshahr, Iran
Publication date: August 18, 2018