Modeling Gilliland Correlation using Genetic Programming

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@Article{OlteanuPaCa10,
  author =       "Marius Olteanu and Nicolae Paraschiv and 
                 Otilia Cangea",
  year =         "2010",
  title =        "Modeling Gilliland Correlation using Genetic
                 Programming",
  journal =      "International Journal of Computers, Communications \&
                 Control",
  volume =       "V",
  number =       "5",
  pages =        "837--843",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, matlab,
                 GPLAB",
  ISSN =         "1841-9836",
  URL =          "http://univagora.ro/jour/index.php/ijccc/article/view/2244",
  URL =          "http://www.journal.univagora.ro/download/pdf/469.pdf",
  size =         "7 pages",
  abstract =     "The distillation process is one of the most important
                 processes in industry, especially petroleum refining.
                 Designing a distillation column assesses numerous
                 challenges to the engineer, being a complex process
                 that is approached in various studies. An important
                 component, directly affecting the efficient operation
                 of the column, is the reflux ratio that is correlated
                 with the number of the theoretical stages, a
                 correlation developed and studied by Gililland. The
                 correlation is used in the case of simplified control
                 models of distillation columns and it is a graphical
                 method. However, in many situations, there is the need
                 for an analytical form that adequately approximates the
                 experimental data. There are in the literature
                 different analytical forms which are used taking into
                 account the desired precision. The present article
                 attempts to address this problem by using the technique
                 of Genetic Programming, a branch of Evolutionary
                 Algorithms that belongs to Artificial Intelligence, a
                 recently developed technique that has recorded
                 successful applications especially in process
                 modelling. Using an evolutionary paradigm and by
                 evolving a population of solutions or subprograms
                 composed of carefully chosen functions and operators,
                 the Genetic Programming technique is capable of finding
                 the program or relation that fits best the available
                 data.",
  notes =        "journal.univagora.ro/",
}

Genetic Programming entries for Marius Olteanu Nicolae Paraschiv Otilia Cangea

Citations