Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds

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@Article{Gharagheizi201271,
  author =       "Farhad Gharagheizi and Poorandokht Ilani-Kashkouli and 
                 Nasrin Farahani and Amir H. Mohammadi",
  title =        "Gene expression programming strategy for estimation of
                 flash point temperature of non-electrolyte organic
                 compounds",
  journal =      "Fluid Phase Equilibria",
  volume =       "329",
  month =        "5 " # sep,
  pages =        "71--77",
  year =         "2012",
  ISSN =         "0378-3812",
  DOI =          "doi:10.1016/j.fluid.2012.05.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378381212002130",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Flammability characteristics,
                 Flash point",
  abstract =     "The accuracy and predictability of correlations and
                 models to determine the flammability characteristics of
                 chemical compounds are of drastic significance in
                 various chemical industries. In the present study, the
                 main focus is on introducing and applying the gene
                 expression programming (GEP) mathematical strategy to
                 develop a comprehensive empirical method for this
                 purpose. This work deals with presenting an empirical
                 correlation to predict the flash point temperature of
                 1471 (non-electrolyte) organic compounds from 77
                 different chemical families. The parameters of the
                 correlation include the molecular weight, critical
                 temperature, critical pressure, acentric factor, and
                 normal boiling point of the compounds. The obtained
                 statistical parameters including root mean square of
                 error of the results from DIPPR 801 data (8.8, 8.9, 8.9
                 K for training, optimisation and prediction sets,
                 respectively) demonstrate improved accuracy of the
                 results of the presented correlation with respect to
                 previously-proposed methods available in open
                 literature.",
}

Genetic Programming entries for Farhad Gharagheizi Poorandokht Ilani-Kashkouli Nasrin Farahani Amir H Mohammadi

Citations