A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon

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@Article{Garg:2017:ASC,
  author =       "A. Garg and Jasmine Siu Lee Lam and B. N. Panda",
  title =        "A hybrid computational intelligence framework in
                 modelling of coal-oil agglomeration phenomenon",
  journal =      "Applied Soft Computing",
  volume =       "55",
  pages =        "402--412",
  year =         "2017",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2017.01.054",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494617300777",
  abstract =     "The phenomenon of Coal-Oil agglomeration for
                 recovering the coal fines by agitating the coal-water
                 slurries in oil is often practised by coal industry to
                 ensure a safe and healthy environment. Experimental
                 procedure for implementing this phenomenon is complex
                 which involves three main mechanisms: crushing,
                 ultimate and proximate analysis. Past studies have
                 often focused on studying this phenomenon by the
                 application of statistical modelling based on response
                 surface designs. The response surface designs hold an
                 assumption of pre-definition of the model structure,
                 which may introduce uncertainty in the predictive
                 ability of the model. Alternatively, the computational
                 intelligence approach of Genetic programming (GP) that
                 evolves the explicit models automatically can be used.
                 However, the effective functioning of GP is often
                 affected by its nature of producing the models of
                 complex size. Therefore, this work develops a hybrid
                 computational intelligence approach namely, Support
                 vector regression-GP (SVR-GP) to study the coal-oil
                 agglomeration phenomenon. Experimental studies based on
                 five inputs, namely, oil dosage, agitation speed,
                 agglomeration time, temperature, and pH are used to
                 measure the organic matter recovery (OMR (percent))
                 from the coal water slurries. A hybrid computational
                 intelligence approach of SVR-GP is proposed in
                 formulating the relationship between OMR (percent) and
                 the five inputs. The performance comparison and
                 validation of the SVR-GP model is done based on the
                 coefficient of determination, root mean square error
                 and mean absolute percentage error. 2-D and 3-D surface
                 analysis based on parametric and sensitivity approach
                 is then conducted on the proposed model to find the
                 relevant relationships between OMR (percent) and
                 inputs. The findings suggest that the pH of coal slurry
                 has a significant effect on the OMR (percent) and hence
                 is important for reducing coal waste generation and
                 promoting a cleaner environment.",
  keywords =     "genetic algorithms, genetic programming, Coal waste,
                 Coal-oil agglomeration, Organic matter recovery,
                 Support vector regression",
}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam Biranchi Narayan Panda

Citations