Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO2 surface

Authors:
Suman Dutta;Simon A. Parsons;Chiranjib Bhattacharjee;Sibdas Bandhyopadhyay;Siddhartha Datta
Affiliations:
Chemical Engineering Department, Jadavpur University, Kolkata 32, India;Centre for Water Science, Cranfield University, Cranfield, Bebfordshire MK43 0AL, UK;Chemical Engineering Department, Jadavpur University, Kolkata 32, India;Central Glass and Ceramic Research Institute, Jadavpur, Kolkata 32, India;Chemical Engineering Department, Jadavpur University, Kolkata 32, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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.