Membrane fouling in microfiltration of oil-in-water emulsions; a comparison between constant pressure blocking laws and genetic programming (GP) model

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

@Article{Fouladitajar:2013:Desalination,
  author =       "Amir Fouladitajar and Farzin Zokaee Ashtiani and 
                 Ahmad Okhovat and Bahram Dabir",
  title =        "Membrane fouling in microfiltration of oil-in-water
                 emulsions; a comparison between constant pressure
                 blocking laws and genetic programming (GP) model",
  journal =      "Desalination",
  volume =       "329",
  pages =        "41--49",
  year =         "2013",
  ISSN =         "0011-9164",
  DOI =          "doi:10.1016/j.desal.2013.09.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S001191641300413X",
  keywords =     "genetic algorithms, genetic programming, Membrane
                 fouling, Blocking laws, Oil-in-water emulsion",
  size =         "41 pages",
  abstract =     "Microfiltration of oil-in-water emulsion with
                 different concentrations and TMPs was experimentally
                 performed to investigate the fouling mechanisms of oil
                 droplets. In this work, the performance of both
                 blocking laws and genetic programming model was
                 evaluated. Four individual and five combined blocking
                 models were applied to determine if they would provide
                 acceptable fits of the experimental data. In individual
                 models, the best predictions were obtained by the
                 intermediate model and the cake model failed to provide
                 any fit of the experimental data in all data sets.
                 Although the combined models used two fitted
                 parameters, they did not provide better fits of the
                 data than individual models. The intermediate model
                 combined with the cake filtration model and standard
                 model provided the same fit as the intermediate model
                 alone. In addition, genetic programming as a novel
                 approach in membrane fouling was used to predict both
                 permeate flux and oil rejection. It was found that for
                 the studied system, the GP model not only was able to
                 provide better fits of experimental data, but also
                 predicted the oil rejection with an acceptable
                 accuracy. The dominant fouling mechanisms were also
                 identified in different operating conditions.",
}

Genetic Programming entries for Amir Fouladitajar Farzin Zokaee Ashtiani Ahmad Okhovat Bahram Dabir

Citations