A simple modelling approach for prediction of standard state real gas entropy of pure materials

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Bagheri:2015:SAR_QSAR_ER,
  author =       "M. Bagheri and T. N. G. Borhani and A. H. Gandomi and 
                 Z. A. Manan",
  title =        "A simple modelling approach for prediction of standard
                 state real gas entropy of pure materials",
  journal =      "SAR and QSAR in Environmental Research",
  year =         "2014",
  volume =       "25",
  number =       "9",
  pages =        "695--710",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming (LGP), standard state absolute
                 entropy of real gases (SSTD), feed forward neural
                 network (FFNN), quantitative structure entropy
                 relationship, exergy analysis",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/1062936X.2014.942356",
  URL =          "http://www.tandfonline.com/doi/full/10.1080/1062936X.2014.942356",
  DOI =          "doi:10.1080/1062936X.2014.942356",
  abstract =     "The performance of an energy conversion system depends
                 on exergy analysis and entropy generation minimisation.
                 A new simple four-parameter equation is presented in
                 this paper to predict the standard state absolute
                 entropy of real gases (SSTD). The model development and
                 validation were accomplished using the Linear Genetic
                 Programming (LGP) method and a comprehensive dataset of
                 1727 widely used materials. The proposed model was
                 compared with the results obtained using a three-layer
                 feed forward neural network model (FFNN model). The
                 root-mean-square error (RMSE) and the coefficient of
                 determination (r2) of all data obtained for the LGP
                 model were 52.24 J/(mol K) and 0.885, respectively.
                 Several statistical assessments were used to evaluate
                 the predictive power of the model. In addition, this
                 study provides an appropriate understanding of the most
                 important molecular variables for exergy analysis.
                 Compared with the LGP based model, the application of
                 FFNN improved the r-squared to 0.914. The developed
                 model is useful in the design of materials to achieve a
                 desired entropy value.",
}

Genetic Programming entries for Mehdi Bagheri Tohid Nejad Ghaffar Borhani A H Gandomi Zainuddin Abdul Manan

Citations