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

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 (%) 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 0.3% and 1.3% 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 3.02% and 12.77%, respectively. The GP-GA based strategy introduced here can be gainfully utilized for modeling and optimization of similar type of contaminant-removal processes.