Experimental and Modeling Investigations of the Viscosity of Crude Oil Binary Blends: New Models Based on the Genetic Algorithm Method

Abstract

Abstract:Achieving an estimation of viscosity in crude oil binary mixtures is often difficult because the relationship of viscosity, to the fraction of each crude oil, and many other parameters and constants is comply. This relationship can be expressed by mathematical models with different variables. Besides the known models for predicting the viscosity of crude oil mixtures, the petroleum industry demands other models which give accurate predictions. In this work, two new empirical models have been developed for the calculation of the viscosity of binary crude oil blends. Two techniques-least square (LS) and genetic algorithm (GA)-were used to determine the parameters of the proposed models. Dynamic viscosity of 12 sets of crude oil blends at 298.15 K and 25 different shear rates were measured, resulting in 300 sets of binary data. Moreover, 700 sets of kinematic viscosity binary data were collected from literature sources and used along with 200 of the 300 sets of experimental binary data with a wide range of American Petroleum Institute (API) gravity (9.89-41.2) and viscosity (1.054-165,860 cSt) to examine existing available models as well as the newly developed models in this study. The remaining 100 experimental data points which were not used in the regression process were used for validating the models. The results in terms of the absolute average relative deviation (AARD%) were 33. 546 and 14.195 for the LS method and 13.113 and 13.672 for the GA method for proposed models one and two, respectively. The results of statistical parameters based on the GA and LS methods showed that the GA is a superior method for new model parameter estimation as compared with the traditional LS technique. The GA-based models developed in this study provided the highest accuracy for viscosity calculation of the crude oil blends over all existing models in the literature.

Kinematic Viscosity Prediction Guide: Reviewing and Evaluating Empirical Models for Diesel Fractions, and Biodiesel-Diesel Blends according to the Temperature and Feedstock

“…18 -KV prediction of biodiesel composed by soybean FAME at any temperature On the other hand, the system with the lowest accuracy was that of soybean FAEE biodiesel-diesel blend, with AAD = 1.04mm²/s and %AAD = 22.17, applying Linear [56] model at 40 °C (Table 5 at supplementary material). This might imply that the type of alcohol used during At 40°C, we observed that the prediction of KV of soybean FAME blend presented a similar behavior to Cragoe [49] apud [50], Chevron [57], Refutas [56] apud [37], Chirino [48] apud [37],…”

“…Some empirical models for KV of mixtures (𝜈 𝑚 ) were developed for mixtures consisting of nonpolar compounds and with similar structure [47] such as Chirinos [48] apud [37], Cragoe [49] apud [50], Arrhenius [51] apud [52], Bingham [53] apud [54], Linear [55], Kendal and Monroe [55],…”

“…However, for Cragoe model, two more parameters are required, 𝐿 𝑖 (Cragoe parameter of each compound i) and L (Cragoe parameter), which are calculated through the KV of each compound i (𝜐 𝑖 ) and mass fraction (𝑤 𝑖 ) [58] apud [49,50].…”

Kinematic Viscosity Prediction Guide: Reviewing and Evaluating Empirical Models for Diesel Fractions, and Biodiesel–Diesel Blends According to the Temperature and Feedstock

“…Some empirical models for KV of mixtures ( ) were developed for mixtures consisting of nonpolar compounds and with similar structure [47] such as Chirinos [48] apud [37], Cragoe [49] apud [50], Arrhenius [51] apud [52], Bingham [53] apud [54], Linear [55], Kendal and Monroe [55],…”

Optimization of Process Design Problems Using Differential Evolution Algorithm

“…They also reported their data and compared their data for various systems (including vapor liquid equilibrium, vapor-liquidliquid equilibrium, and liquid-liquid equilibrium) form the literature. In some other studies, GA was successfully applied for estimation of interaction parameters of other complex models for systems of LLE containing ionic liquids and also for modeling of viscosity of crude oil binary blends (Vatani et al 2012;Vakili-Nezhaad et al 2013;Vakili-Nezhaad et al 2014;Al-Maamari et al 2015).…”