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,
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author = "Veena Patil-Shinde and K. B. Mulani and
Kamini Donde and N. N. Chavan and S. Ponrathnam and
Sanjeev S. Tambe",
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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",
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journal = "Journal of Environmental Chemical Engineering",
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volume = "4",
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number = "4, Part A",
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pages = "4275--4286",
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year = "2016",
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ISSN = "2213-3437",
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DOI = "doi:10.1016/j.jece.2016.09.030",
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URL = "http://www.sciencedirect.com/science/article/pii/S2213343716303517",
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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.",
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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 Jagannath Donde
N N Chavan
S Ponrathnam
Sanjeev S Tambe
Citations