Prediction of the natural gas consumption in chemical processing facilities with genetic programming

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@Article{Kovacic:2016:GPEM,
  author =       "Miha Kovacic and Franjo Dolenc",
  title =        "Prediction of the natural gas consumption in chemical
                 processing facilities with genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2016",
  volume =       "17",
  number =       "3",
  pages =        "231--249",
  keywords =     "genetic algorithms, genetic programming, Natural gas
                 consumption prediction, Chemical processing,
                 Modelling",
  ISSN =         "1389-2576",
  URL =          "http://link.springer.com/article/10.1007/s10710-016-9264-x?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst",
  DOI =          "doi:10.1007/s10710-016-9264-x",
  size =         "19 pages",
  abstract =     "Cinkarna Ltd. is a chemical processing company in
                 Slovenia and the country's largest manufacturer of
                 titanium oxides (TiO2). Chemical processing and
                 titanium oxide manufacturing in particular requires
                 high natural gas consumption, and it is difficult to
                 accurately pre-order gas from suppliers. In accordance
                 with the Energy Agency of the Republic of Slovenia
                 regulations, each natural gas supplier regulates and
                 determines the charges for the differences between the
                 ordered (predicted) and the actually supplied
                 quantities of natural gas. Yearly charges for these
                 differences total 1.11 percent of supplied natural gas
                 costs (average 50960 EUR per year). This paper presents
                 natural gas consumption prediction and the minimization
                 of associated costs. The data on daily temperature,
                 steam boilers, sulphuric acid and TiO2 production was
                 collected from January 2012 until November 2014. Based
                 on the collected data, a linear regression and a
                 genetic programming model were developed. Compared to
                 the specialist's prediction of natural gas consumption,
                 the linear regression and genetic programming models
                 reduce the charges for the differences between the
                 ordered and the actually supplied quantities by 3.00
                 and 5.30 times, respectively. Also, from January until
                 November 2014 the same genetic programming model was
                 used in practice. The results show that in a similar
                 gas consumption regime the differences between the
                 ordered and the actually supplied quantities are
                 statistically significant, namely, they are 3.19 times
                 lower (t test, p < 0.05) than in the period in which
                 the specialist responsible for natural gas consumption
                 made the predictions.",
}

Genetic Programming entries for Miha Kovacic Franjo Dolenc

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