Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension

Abstract

Abstract The interfacial tension (IFT) of hydrocarbon/water system is one of the most important parameters in various fields of chemical, petroleum and process industries. Laboratory measurement of interfacial tension is laborious, time demanding and involves costly experimental setup. Current study presents genetic programming (GP) as a powerful tool in order to develop a novel correlation for estimation of IFT in hydrocarbon/water systems under wide ranges of experimental conditions. To achieve this mission, a comprehensive databank comprising 1075 experimentally measured data points were acquired from the literature reports. Four influencing factors of hydrocarbon critical temperature, experiment temperature, pressure and hydrocarbon/water density difference were considered as independent correlating variables to design and develop the correlation. Comprehensive error analysis demonstrates the superiority of the proposed correlation with R 2 = 0.91 and AARD = 4.38% in comparison with literature data. The predictability of the genetic model was further compared with a recently published model and other well-known empirical correlations reported in literature. The result suggests that the proposed tool is of great value for fast and precise estimation of hydrocarbon/water IFT.