TY - JOUR ID - 55719 TI - Viscosity Reduction of Heavy Crude Oil by Dilution Methods: New Correlations for the Prediction of the Kinematic Viscosity of Blends JO - Iranian Journal of Oil and Gas Science and Technology JA - IJOGST LA - en SN - 2345-2412 AU - Mohammadi, Saeed AU - Sobati, Mohammad Amin AU - Sadeghi, Mohammad AD - M.S. Student, School of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran AD - Assistant Professor, School of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran AD - Associate Professor, School of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran Y1 - 2019 PY - 2019 VL - 8 IS - 1 SP - 60 EP - 77 KW - Heavy crude oil KW - Kinematic viscosity KW - blending KW - Genetic Algorithm KW - Binary blend DO - 10.22050/ijogst.2018.97887.1405 N2 - Dilution is one of the various existing methods in reducing heavy crude oil viscosity. In this method, heavy crude oil is mixed with a solvent or lighter oil in order to achieve a certain viscosity. Thus, precise mixing rules are needed to estimate the viscosity of blend. In this work, new empirical models are developed for the calculation of the kinematic viscosity of crude oil and diluent blends. Genetic algorithm (GA) is utilized to determine the parameters of the proposed models. 850 data points on the viscosity of blends (i.e. 717 weight fraction-based data and 133 volume fraction-based data) were obtained from the literature. The prediction result for the volume fraction-based model in terms of the absolute average relative deviation (AARD (%)) was 8.73. The AARD values of the binary and ternary blends of the weight fraction-based model (AARD %) were 7.30 and 10.15 respectively. The proposed correlations were compared with other available correlations in the literature such as Koval, Chevron, Parkash, Maxwell, Wallace and Henry, and Cragoe. The comparison results confirm the better prediction accuracy of the newly proposed correlations. UR - https://ijogst.put.ac.ir/article_55719.html L1 - https://ijogst.put.ac.ir/article_55719_47d1c328365b2d960c0e21ccafe21eaf.pdf ER -