Prediction of permeation flux decline during MF of oily wastewater using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "H. Shokrkar and A. Salahi and N. Kasiri and 
                 T. Mohammadi",
  title =        "Prediction of permeation flux decline during MF of
                 oily wastewater using genetic programming",
  journal =      "Chemical Engineering Research and Design",
  volume =       "90",
  number =       "6",
  pages =        "846--853",
  year =         "2012",
  note =         "Special Issue on the 3rd European Process
                 intensification Conference",
  ISSN =         "0263-8762",
  DOI =          "doi:10.1016/j.cherd.2011.10.002",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Modelling,
                 Microfiltration, Oily wastewater",
  abstract =     "Genetic programming is an orderly method for getting
                 computers to regularly solve a problem. The genetic
                 programming creates a computer program from an obtained
                 data and solves the problem. In this work, treatment of
                 oily wastewaters with synthesised mullite ceramic
                 microfiltration membranes was studied and a new
                 approach for modelling of the membrane flux is
                 presented. The model used input parameters for
                 operating conditions (flux and filtration time) and
                 feed oily wastewater quality (oil concentration,
                 temperature, trans-membrane pressure and cross-flow
                 velocity). The genetic programming used here delivers a
                 mathematical function for the membrane flux as a
                 function of the independent variables stated above.
                 Parameters for controlling and termination criterion
                 for a run are provided by the user. Result is provided
                 as a tree of functions and terminals. The results thus
                 obtained from the genetic programming model
                 demonstrated good representation of the experimental
                 data with an average error of less than 5percent.",

Genetic Programming entries for H Shokrkar A Salahi N Kasiri T Mohammadi