Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system

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@Article{Suh2009137,
  author =       "Chang-Won Suh and Joong-Won Lee and 
                 Yoon-Seok Timothy Hong and Hang-Sik Shin",
  title =        "Sequential modeling of fecal coliform removals in a
                 full-scale activated-sludge wastewater treatment plant
                 using an evolutionary process model induction system",
  journal =      "Water Research",
  volume =       "43",
  number =       "1",
  pages =        "137--147",
  year =         "2009",
  DOI =          "DOI:10.1016/j.watres.2008.09.022",
  URL =          "http://www.sciencedirect.com/science/article/B6V73-4TK477C-C/2/16302307b5ee5c7add9e0e3897c452f7",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 process model induction system, Sequential modeling
                 paradigm, Fecal coliform bacteria, Prediction of fecal
                 coliform concentration, Full-scale activated-sludge
                 wastewater treatment plant",
  ISSN =         "0043-1354",
  abstract =     "We propose an evolutionary process model induction
                 system that is based on the grammar-based genetic
                 programming to automatically discover multivariate
                 dynamic inference models that are able to predict fecal
                 coliform bacteria removals using common process
                 variables instead of directly measuring fecal coliform
                 bacteria concentration in a full-scale municipal
                 activated-sludge wastewater treatment plant. A
                 sequential modeling paradigm is also proposed to derive
                 multivariate dynamic models of fecal coliform removals
                 in the evolutionary process model induction system. It
                 is composed of two parts, the process estimator and the
                 process predictor. The process estimator acts as an
                 intelligent software sensor to achieve a good
                 estimation of fecal coliform bacteria concentration in
                 the influent. Then the process predictor yields
                 sequential prediction of the effluent fecal coliform
                 bacteria concentration based on the estimated fecal
                 coliform bacteria concentration in the influent from
                 the process estimator with other process variables. The
                 results show that the evolutionary process model
                 induction system with a sequential modeling paradigm
                 has successfully evolved multivariate dynamic models of
                 fecal coliform removals in the form of explicit
                 mathematical formulas with high levels of accuracy and
                 good generalization. The evolutionary process model
                 induction system with sequential modeling paradigm
                 proposed here provides a good alternative to develop
                 cost-effective dynamic process models for a full-scale
                 wastewater treatment plant and is readily applicable to
                 a variety of other complex treatment processes.",
}

Genetic Programming entries for Chang-Won Suh Joong-Won Lee Yoon-Seok Hong Hang-Sik Shin

Citations