Evolutionary self-organising modelling of a municipal wastewater treatment plant

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

@Article{Hong:2003:WR,
  author =       "Yoon-Seok Hong and Rao Bhamidimarri",
  title =        "Evolutionary self-organising modelling of a municipal
                 wastewater treatment plant",
  journal =      "Water Research",
  year =         "2003",
  volume =       "37",
  pages =        "1199--1212",
  number =       "6",
  abstract =     "Building predictive models for highly time varying and
                 complex multivariable aspects of the wastewater
                 treatment plant is important both for understanding the
                 dynamics of this complex system, and in the development
                 of optimal control support and management schemes.
                 genetic programming as a self-organising modelling
                 tool, to model dynamic performance of municipal
                 activated-sludge wastewater treatment plants. Genetic
                 programming evolves several process models
                 automatically based on methods of natural selection
                 ('survival of the fittest'), that could predict the
                 dynamics of MLSS and suspended solids in the
                 effluent.

                 The predictive accuracy of the genetic programming
                 approach was compared with a nonlinear state-space
                 model with neural network and a well-known IAWQ ASM2.
                 The genetic programming system evolved some models that
                 were an improvement over the neural network and ASM2
                 and showed that the transparency of the model evolved
                 may allow inferences about underlying processes to be
                 made. This work demonstrates that dynamic nonlinear
                 processes in the wastewater treatment plant may be
                 successfully modelled through the use of evolutionary
                 model induction algorithms in GP technique. Further,
                 our results show that genetic programming can work as a
                 cost-effective intelligent modelling tool, enabling us
                 to create prototype process models quickly and
                 inexpensively instead of an engineer developing the
                 process model.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6V73-47XW9PY-5/2/5581df84c89448cc706b69488765c7e1",
  keywords =     "genetic algorithms, genetic programming, Municipal
                 wastewater treatment plant, Self-organising modelling,
                 Model evolution, Neural network, ASM2",
  DOI =          "doi:10.1016/S0043-1354(02)00493-1",
  notes =        "PMID: 12598184",
}

Genetic Programming entries for Yoon-Seok Hong Rao Bhamidimarri

Citations