Application of artificial neural networks for formulation and modeling of dye adsorption onto multiwalled carbon nanotubes

Application of artificial neural networks for formulation and modeling of dye adsorption onto... In this study, an artificial neural network (ANN) has been developed to predict the adsorption amount of dye (methylene blue) onto multiwalled carbon nanotubes. Batch experiments have been carried out to obtain experimental data. Important parameters in the adsorption system such as initial dye concentration, adsorbent dosage, temperature, pH and contact time have been used as the inputs of the network, while the output is the final concentration of dye in aqueous solution after adsorption. The neural network structure has been optimized by testing various training algorithms and different number of neurons in a hidden layer. An empirical equation for determination of final dye concentration in aqueous solutions after adsorption has been developed by using the weights of the optimized network. The results of the optimized ANN have been compared with conventional models in equilibrium and kinetic fields. According to error analysis and determination coefficient, the ANN was found to be the most appropriate model to describe this adsorption process. Sensitivity analysis showed that initial dye concentration, pH and contact time are the most effective parameters in this process. The influence percentages of these parameters on the output were 28, 24 and 24 %, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Chemical Intermediates Springer Journals

Application of artificial neural networks for formulation and modeling of dye adsorption onto multiwalled carbon nanotubes

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Publisher
Springer Netherlands
Copyright
Copyright © 2012 by Springer Science+Business Media Dordrecht
Subject
Chemistry; Catalysis; Physical Chemistry; Inorganic Chemistry
ISSN
0922-6168
eISSN
1568-5675
D.O.I.
10.1007/s11164-012-0865-6
Publisher site
See Article on Publisher Site

Abstract

In this study, an artificial neural network (ANN) has been developed to predict the adsorption amount of dye (methylene blue) onto multiwalled carbon nanotubes. Batch experiments have been carried out to obtain experimental data. Important parameters in the adsorption system such as initial dye concentration, adsorbent dosage, temperature, pH and contact time have been used as the inputs of the network, while the output is the final concentration of dye in aqueous solution after adsorption. The neural network structure has been optimized by testing various training algorithms and different number of neurons in a hidden layer. An empirical equation for determination of final dye concentration in aqueous solutions after adsorption has been developed by using the weights of the optimized network. The results of the optimized ANN have been compared with conventional models in equilibrium and kinetic fields. According to error analysis and determination coefficient, the ANN was found to be the most appropriate model to describe this adsorption process. Sensitivity analysis showed that initial dye concentration, pH and contact time are the most effective parameters in this process. The influence percentages of these parameters on the output were 28, 24 and 24 %, respectively.

Journal

Research on Chemical IntermediatesSpringer Journals

Published: Oct 31, 2012

References

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