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Modeling dynamic viscosity of n-alkanes using LSSVM technique

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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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Keywords: Dynamic viscosity; LSSVM; n-alkane; predicting model; reservoir condition

Document Type: Research Article

Affiliations: 1: Department of mechanical Engineering, Payame Noor University (PNU), Tehran, IranAQ2 2: Young Researcher and Elite Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran 3: Mathematics Department, Faculty of Basic Sciences, Khatam-ol-Anbia (PBU) University, Tehran, IRAN 4: Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran 5: Chemical Engineering Department, Amirkabir University of Technology, Mahshahr, Iran

Publication date: August 18, 2018

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