Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination

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

@InProceedings{Jaewuk:2014:HIC,
  author =       "Koo Jaewuk and Shin Yonghyun and Sangho Lee and 
                 Juneseok Choi",
  title =        "Application Of Data Mining For Reverse Osmosis Process
                 In Seawater Desalination",
  booktitle =    "11th International Conference on Hydroinformatics",
  year =         "2014",
  pages =        "Paper 443",
  address =      "New York, USA",
  month =        aug # " 17-21",
  organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-692-28129-1",
  URL =          "http://academicworks.cuny.edu/cc_conf_hic/443/",
  URL =          "http://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1442&context=cc_conf_hic.pdf",
  broken =       "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1682/1785.pdf",
  size =         "4 pages",
  abstract =     "Reverse osmosis (RO) membrane process has been
                 considered a promising technology for water treatment
                 and desalination. However, it is difficult to predict
                 the performance of pilot- or full-scale RO systems
                 because numerous factors are involved in RO
                 performance, including variations in feed water
                 (quantity, quality, temperature, etc), membrane
                 fouling, and time-dependent changes (deteriorations).
                 Accordingly, this study intended to develop a practical
                 approach for the analysis of operation data in
                 pilot-scale reverse osmosis (RO) processes. Novel
                 techniques such as artificial neural network (ANN) and
                 genetic programming (GP) technique were applied to
                 correlate key operating parameters and RO permeability
                 statistically. The ANN and GP models were trained using
                 a set of experimental data from a RO pilot plant with a
                 capacity of 1000 cubic meters per day and then used to
                 predict its performance. The comparison of the ANN and
                 GP model calculations with the experiment results
                 revealed that the models were useful for analysing and
                 classifying the performance of pilot-scale RO systems.
                 The models were also applied for an in-depth analysis
                 of RO system performance under dynamic conditions.",
  notes =        "Order of within authors' names unclear, alternative:
                 Jaewuk Koo and Yonghyun Shin and Sangho Lee and
                 Juneseok Choi. http://www.hic2014.org/xmlui/",
}

Genetic Programming entries for Koo Jaewuk Shin Yonghyun Sangho Lee Juneseok Choi

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