Modelling a Transformer Oil Regeneration Process Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Hanselmann:1996:Chemeca,
  author =       "K. Hanselmann and G. W. Barton and B. McKay and 
                 M. J. Willis",
  title =        "Modelling a Transformer Oil Regeneration Process Using
                 Genetic Programming",
  booktitle =    "Chemeca 96: Excellence in Chemical Engineering;
                 Proceedings of the 24th Australian and New Zealand
                 Chemical Engineering Conference and Exhibition",
  year =         "1996",
  editor =       "Gordon Weiss",
  number =       "96/13",
  series =       "National conference publication",
  pages =        "9--84 [in volume 2]",
  address =      "Barton, ACT, Australia",
  publisher_address = "Australia",
  publisher =    "Institution of Engineers",
  keywords =     "genetic algorithms, genetic programming, Data
                 processing, Neural networks (Computer science),
                 Mathematical models, Linear programming, Mathematical
                 models, Offshore oil industry, Electric insulators and
                 insulation, Oils",
  ISBN =         "0-85825-658-4",
  URL =          "http://search.informit.com.au/documentSummary;dn=894065266629714;res=IELENG",
  abstract =     "Genetic programming and neural network techniques were
                 both used to predict the product distribution and yield
                 of product oil from a reactor in a transformer oil
                 regeneration process. All reactor models were developed
                 by fitting laboratory-scale data. For the (relatively)
                 small experimental data set available, it was found
                 that the accuracy of the reactor model was
                 significantly better when using genetic programming
                 than neural network modelling techniques. A flowsheet
                 of a pilot-scale version of the process was developed
                 (using commercial simulation packages) based on the
                 reactor model obtained using genetic programming, and
                 the optimal operating conditions determined so as to
                 give the maximum yield of regenerated transformer
                 oil.",
  notes =        "http://lorien.ncl.ac.uk/ming/infer/inferrefs.htm (1)
                 CSIRO Division of Coal and Energy Technology, Lucas
                 Heights, Sydney, Australia (2) Department of Chemical
                 Engineering, University of Sydney, Australia (3)
                 Department of Chemical Engineering, University of
                 Sydney, Australia (4) Department of Chemical and
                 Process Engineering, University of Newcastle, UK",
}

Genetic Programming entries for K Hanselmann Geoffrey W Barton Ben McKay Mark J Willis

Citations