Artificial neural network (ANN) approach for modeling of Cr(VI) adsorption from aqueous solution by zeolite prepared from raw fly ash (ZFA)

In this present work, artificial neural networks (ANN) are applied for prediction of percentage adsorption efficiency for the removal of Cr(VI) ions from aqueous solution by zeolite (ZFA) prepared from raw fly ash (RFA). The off operational parameters such as initial pH, adsorbent dosage, contact ti...

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Published in:Journal of industrial and engineering chemistry (Seoul, Korea) 2013, 19(3), , pp.1044-1055
Main Authors: Asl, SeyedMostafa Hosseini, Ahmadi, Maral, Ghiasvand, Mohamad, Tardast, Ali, Katal, Reza
Format: Article
Language:English
Subjects:
Adsorption
ANN
Artificial neural networks
Ash
Cr(VI)
Estimates
Fly ash
Isotherm
Kinetic
Learning theory
Mathematical models
Neural networks
Sorption
Thermodynamic
화학공학
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Summary:In this present work, artificial neural networks (ANN) are applied for prediction of percentage adsorption efficiency for the removal of Cr(VI) ions from aqueous solution by zeolite (ZFA) prepared from raw fly ash (RFA). The off operational parameters such as initial pH, adsorbent dosage, contact time and temperature is studied to optimize the conditions for maximum removal of Cr(VI) ions. Three equations, i.e. Morris–Weber, Lagergren, and pseudo second order have been tested to track the kinetics of removal process. The Langmuir, Freundlich, Redlich–Peterson, Temkin, and D-R are subjected to sorption data to estimate sorption capacity. Thermodynamic parameters showed that the adsorption of Cr(VI) onto ZFA was feasible, spontaneous and endothermic. Artificial neural networks are effective in modeling and simulation of highly non-liner multivariable relationships. The comparison of the removal efficiencies of Cr(VI) using ANN model and experimental results showed that ANN model can estimate the behavior of the Cr(VI) removal process under different conditions.
ISSN:1226-086X
1876-794X
DOI:10.1016/j.jiec.2012.12.001