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Artificial Neural Network Modeling for Adsorption of Dyes from Aqueous Solution using Rice Husk Carbon
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 4 |
| Year of Publication: 2012 |
| Authors: R. D. Khonde, S. L. Pandharipande |
10.5120/5526-7567
|
R. D. Khonde, S. L. Pandharipande . Artificial Neural Network Modeling for Adsorption of Dyes from Aqueous Solution using Rice Husk Carbon. International Journal of Computer Applications. 41, 4 ( March 2012), 1-5. DOI=10.5120/5526-7567
Abstract
Adsorption is one of the important industrial processes used for removal of colour, odour, turbidity & reduction of COD. In adsorption, the solute present in dilute concentration in liquid or gas phase is removed by contacting with suitable solid adsorbent so that the transfer of the component first takes place on the surface of solid and then into the pore of the solid. The present work is aimed at exploring rice husk carbon as low cost adsorbent for removal of various dyes from synthetically prepared aqueous solutions and modeling the adsorption process using artificial neural network. Rice husk carbon, developed in the present work at the laboratory scale, is analyzed for its BET surface area & is observed to be very effective for removal of dyes, namely bromocresol red, alizarin red, malachite green and methylene blue from their aqueous solutions. Further, the RHC developed. The present work highlighted the efficacy of ANN as an effective tool in modeling adsorption. The ANN models developed are using elite-ANN ©. The architecture of artificial neural network is initialized & training has been carried out using the experimental data. The trained neural network is used to predict output for the given set of input parameters.
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