Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies

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@Article{Ghugare:2016:JEI,
  author =       "Suhas B. Ghugare and Sanjeev S. Tambe",
  title =        "Genetic programming based high performing correlations
                 for prediction of higher heating value of coals of
                 different ranks and from diverse geographies",
  journal =      "Journal of the Energy Institute",
  year =         "2017",
  volume =       "90",
  number =       "3",
  pages =        "476--484",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Coal, Higher
                 heating value, Proximate analysis, Ultimate analysis",
  ISSN =         "1743-9671",
  DOI =          "doi:10.1016/j.joei.2016.03.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1743967115304578",
  abstract =     "The higher heating value (HHV) is the most important
                 indicator of a coal's potential energy yield. It is
                 commonly used in the efficiency and optimal design
                 calculations pertaining to the coal combustion and
                 gasification processes. Since the experimental
                 determination of coal's HHV is tedious and
                 time-consuming, a number of proximate and/or ultimate
                 analyses based correlations-which are mostly
                 linear-have been proposed for its estimation. Owing to
                 the fact that relationships between some of the
                 constituents of the proximate/ultimate analyses and the
                 HHV are nonlinear, the linear models make suboptimal
                 predictions. Also, a majority of the currently
                 available HHV models are restricted to the coals of
                 specific ranks or particular geographical regions.
                 Accordingly, in this study three proximate and ultimate
                 analysis based nonlinear correlations have been
                 developed for the prediction of HHV of coals by using
                 the computational intelligence (CI) based genetic
                 programming (GP) formalism. Each of these correlations
                 possesses following noteworthy characteristics: (i) the
                 highest HHV prediction accuracy and generalization
                 capability as compared to the existing models, (ii)
                 wider applicability for coals of different ranks and
                 from diverse geographies, and (iii) structurally lower
                 complex than the other CI-based existing HHV models. It
                 may also be noted that in this study, the GP technique
                 has been used for the first time for developing
                 coal-specific HHV models. Owing to the stated
                 attractive features, the GP-based models proposed here
                 possess a significant potential to replace the existing
                 models for predicting the HHV of coals.",
}

Genetic Programming entries for Suhas B Ghugare Sanjeev S Tambe

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