Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming

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

@Article{Lee2009285,
  author =       "Tae-Mun Lee and Hyunje Oh and Youn-Kyoo Choung and 
                 Sanghoun Oh and Moongu Jeon and Joon Ha Kim and 
                 Sook Hyun Nam and Sangho Lee",
  title =        "Prediction of membrane fouling in the pilot-scale
                 microfiltration system using genetic programming",
  journal =      "Desalination",
  volume =       "247",
  number =       "1-3",
  pages =        "285--294",
  year =         "2009",
  month =        oct,
  ISSN =         "0011-9164",
  DOI =          "doi:10.1016/j.desal.2008.12.031",
  URL =          "http://www.sciencedirect.com/science/article/B6TFX-4X502WT-11/2/27587f58d0280f3d90f2898992cdab65",
  keywords =     "genetic algorithms, genetic programming, Membrane
                 fouling, Prediction",
  abstract =     "In the recent past, machine learning (ML) techniques
                 such as artificial neural networks (ANN) or genetic
                 algorithm (GA) have been increasingly used to model
                 membrane fouling and performance. In the present study,
                 we select genetic programming (GP) for modeling and
                 prediction of the membrane fouling rate in a
                 pilot-scale drinking water production system. The model
                 used input parameters for operating conditions (flow
                 rate and filtration time) and feed water quality
                 (turbidity, temperature, algae pH). GP was applied to
                 discover the mathematical function for the pattern of
                 the membrane fouling rate. The GP model allows
                 predicting satisfactorily the filtration performances
                 of the pilot plant obtained for different water quality
                 and changing operating conditions. A valuable benefit
                 of GP modeling was that the models did not require
                 underlying descriptions of the physical processes. GP
                 has displayed the potential to evaluate membrane
                 performance as a feed-forward simulator toward an
                 'intelligent' membrane system.",
  notes =        "Presented at the First IWA Asia Pacific Young Water
                 Professionals Conference, Gwangju, South Korea,
                 December 8-10, 2008",
}

Genetic Programming entries for Tae-Mun Lee Hyunje Oh Youn-Kyoo Choung Sanghoun Oh Moongu Jeon Joon Ha Kim Sook Hyun Nam Sangho Lee

Citations