Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO(2) surface
IR@CGCRI: CSIR-Central Glass and Ceramic Research Institute, Kolkata
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| Title |
Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO(2) surface
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| Creator |
Dutta, Suman
Parsons, Simon A. Bhattacharjee, Chiranjib Bandyopadhyay, Sibdas Datta, Siddhartha |
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| Subject |
Processing Science
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| Description |
Development of an automated wastewater treatment plant is very difficult as the parameters of an industrial effluent change severely; accordingly the change in output of treatment plant. A computer-simulated model is required for interrelating the input and output parameters of wastewater treatment plant. An artificial neural network model has been proposed for the prediction of adsorption and photocatalysis efficiency of TiO(2) photocatalyst. The network was trained using the experimental data obtained at different pH with different TiO(2) dose and initial dye concentration. Different algorithms and transfer functions for hidden layer have been tested to find the most suitable and reliable network. The optimum number of neurons in the hidden layer was found by trial and error method. These neural network models efficiently predict the adsorption efficiency (% dye removal), adsorption capacity (loading) and photocatalytic efficiency of the process. Solution of reactive black 5 was used as simulated dye wastewater for this study. The effect of different operating parameters on process efficiency was studied. (C) 2010 Elsevier Ltd. All rights reserved.
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| Publisher |
PERGAMON-ELSEVIER SCIENCE LTD
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| Date |
2010-12
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://cgcri.csircentral.net/734/1/dutta2010.pdf
Dutta, Suman and Parsons, Simon A. and Bhattacharjee, Chiranjib and Bandyopadhyay, Sibdas and Datta, Siddhartha (2010) Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO(2) surface. EXPERT SYSTEMS WITH APPLICATIONS, 37 (12). pp. 8634-8638. ISSN 0957-4174 |
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| Relation |
http://cgcri.csircentral.net/734/
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