Genetic programming (GP) approach for prediction of supercritical CO2 thermal conductivity

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Rostami:2017:CERD,
  author =       "Alireza Rostami and Milad Arabloo and 
                 Hojatollah Ebadi",
  title =        "Genetic programming (GP) approach for prediction of
                 supercritical {CO2} thermal conductivity",
  journal =      "Chemical Engineering Research and Design",
  volume =       "122",
  pages =        "164--175",
  year =         "2017",
  ISSN =         "0263-8762",
  DOI =          "doi:10.1016/j.cherd.2017.02.028",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263876217301247",
  abstract =     "Gas thermal conductivity is one of the thermophysical
                 properties that inevitably enters into mathematical
                 models of real systems used in the design of chemical
                 engineering processes or in the gas industry. In this
                 study, via implementing a powerful and newly applied
                 equation generator algorithm known as, genetic
                 programming (GP) mathematical strategy, a novel
                 correlation for estimation of supercritical CO2 thermal
                 conductivity is established. The proposed correlation
                 is developed and validated based on a comprehensive
                 databank of 752 thermal conductivity datasets from open
                 literature. By using various statistical quality
                 measures, the result of the proposed GP model was
                 compared with commonly used literature models. As a
                 result, the proposed GP model gives the best fit and
                 satisfactory agreement with the target data with an
                 average absolute relative error of 2.31percent and R2 =
                 0.997. A parametric sensitivity analysis showed that
                 pressure and density of the CO2 gas stream have
                 considerable impact on the thermal conductivity at
                 supercritical condition. The results of this study can
                 be of enormous practical worth for scientist and
                 expertise in order to efficiently compute the thermal
                 conductivity in any supercritical industry involving
                 CO2.",
  keywords =     "genetic algorithms, genetic programming, Supercritical
                 CO2 thermal conductivity, Empirical correlation,
                 Comprehensive error analysis",
}

Genetic Programming entries for Ali Reza Rezghi Rostami Milad Arabloo Hojatollah Ebadi

Citations