Linear Genetic Programming for Prediction of Nickel Recovery from Spent Nickel Catalyst

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Ossman:2010:AJEAS,
  author =       "Mona E. Ossman and Walaa Sheta and Y. Eltaweel",
  title =        "Linear Genetic Programming for Prediction of Nickel
                 Recovery from Spent Nickel Catalyst",
  journal =      "American Journal of Engineering and Applied Sciences",
  year =         "2010",
  volume =       "3",
  number =       "2",
  pages =        "482--488",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://thescipub.com/pdf/10.3844/ajeassp.2010.482.488",
  DOI =          "doi:10.3844/ajeassp.2010.482.488",
  size =         "7 pages",
  ISSN =         "19417020",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:ceb69159d4d8f9ef5c936c95f426a554",
  abstract =     "Problem statement: In this study Linear Genetic
                 Programming (LGP) and statistical regression are used
                 in predicting Current Efficiency (CE) of Electro
                 deposition cell used for recovery of nickel from spent
                 nickel catalyst. Approach: The Nickel electro
                 deposition from spent catalyst leachate solutions was
                 studied to determine the effect of the operative
                 conditions such as nickel concentration, temperature,
                 current density and time on the CE of the unit cell.
                 Results: For this purpose, LGP and regression models
                 were calibrated with training sets and validated by
                 testing sets. Additionally, the robustness of the
                 proposed LGP and regression models were evaluated by
                 experimental data, which are used neither in training
                 nor at testing stage. The results showed that both
                 techniques predicted the CE data in quite good
                 agreement with the observed ones and the predictions of
                 LGP are challenging. Conclusion/Recommendations: The
                 performance of LGP, which was moderately better than
                 statistical regression, is very promising and hence
                 supports the use of LGP in simulating the electro
                 deposition of Nickel from spent Nickel catalyst.",
}

Genetic Programming entries for Mona E Ossman Walaa Sheta Y Eltaweel

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