The Removal of arsenite [As(III)] and arsenate [As(V)] ions from wastewater using TFA and TAFA resins: Computational intelligence based reaction modeling and optimization

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@Article{PatilShinde:2016:JECE,
  author =       "Veena Patil-Shinde and K. B. Mulani and 
                 Kamini Donde and N. N. Chavan and S. Ponrathnam and 
                 Sanjeev S. Tambe",
  title =        "The Removal of arsenite [As(III)] and arsenate [As(V)]
                 ions from wastewater using {TFA} and {TAFA} resins:
                 Computational intelligence based reaction modeling and
                 optimization",
  journal =      "Journal of Environmental Chemical Engineering",
  volume =       "4",
  number =       "4, Part A",
  pages =        "4275--4286",
  year =         "2016",
  ISSN =         "2213-3437",
  DOI =          "doi:10.1016/j.jece.2016.09.030",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2213343716303517",
  abstract =     "Being significantly toxic, removal of arsenic forms an
                 important part of the drinking- and waste-water
                 treatment. Tannin is a polyphenol-rich substrate that
                 efficiently and adsorptively binds to the multivalent
                 metal ions. In this study, tannin-formaldehyde (TFA)
                 and tannin-aniline-formaldehyde (TAFA) resins were
                 synthesized and employed successfully for an adsorptive
                 removal of arsenite [As(III)] and arsenate [As(V)] ions
                 from the contaminated water. Next, a computational
                 intelligence (CI) based hybrid strategy was used to
                 model and optimize the resin-based adsorption of
                 As(III) and As(V) ions for securing optimal reaction
                 conditions. This strategy first uses an exclusively
                 reaction data driven modeling strategy, namely, genetic
                 programming (GP) to predict the extent (percent) of
                 As(III)/As(V) adsorbed on TFA and TAFA resins. Next,
                 the input space of the GP-based models consisting of
                 the reaction condition variables/parameters was
                 optimized using genetic algorithm (GA) method; the
                 objective of this optimization was to maximize the
                 adsorption of As(III) and As(V) ions on the two resins.
                 Finally, the sets of optimal reaction conditions
                 provided by GP-GA hybrid method were verified
                 experimentally the results of which indicate that the
                 optimized conditions have lead to 0.3percent and
                 1.3percent increase in the adsorption of As(III) and
                 As(V) ions on TFA resin. More significantly, the
                 optimized conditions have increased the adsorption of
                 As(III) and As(V) on TAFA resin by 3.02percent and
                 12.77percent, respectively. The GP-GA based strategy
                 introduced here can be gainfully used for modeling and
                 optimization of similar type of contaminant-removal
                 processes.",
  keywords =     "genetic algorithms, genetic programming,
                 Tannin-formaldehyde resin, Tannin-aniline-formaldehyde
                 resin, Adsorption of As(III) and As(V) ions",
}

Genetic Programming entries for Veena Patil-Shinde K B Mulani Kamini Donde N N Chavan S Ponrathnam Sanjeev S Tambe

Citations