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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Rostami:2017:JML,
  author =       "Alireza Rostami and Hojatollah Ebadi and 
                 Milad Arabloo and Mahdi Kalantari Meybodi and Alireza Bahadori",
  title =        "Toward genetic programming (GP) approach for
                 estimation of hydrocarbon/water interfacial tension",
  journal =      "Journal of Molecular Liquids",
  volume =       "230",
  pages =        "175--189",
  year =         "2017",
  ISSN =         "0167-7322",
  DOI =          "doi:10.1016/j.molliq.2016.11.099",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0167732216314222",
  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 R2 = 0.91 and AARD = 4.38percent 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.",
  keywords =     "genetic algorithms, genetic programming, Interfacial
                 tension, Hydrocarbon/water system, Error analysis",
}

Genetic Programming entries for Ali Reza Rezghi Rostami Hojatollah Ebadi Milad Arabloo Mahdi Kalantari Meybodi Alireza Bahadori

Citations