Modelling resorcinol adsorption in water environment using artificial neural network

Ramhari Aghav and Somnath Mukherjee

International Journal of Environmental Technology and Management, 2011, vol. 14, issue 1/2/3/4, 9-18

Abstract: The application of Artificial Neural Network (ANN) for the prediction of removal efficiency of resorcinol in water environment using low-cost carbonaceous adsorbents such as rice husk ash was studied in the present investigation. The input data used for training of the ANN model include adsorbent dose, adsorbate concentration, time of contact and pH. The various input variables were obtained in a laboratory experiment. The results obtained from ANN model for the prediction of resorcinol removal efficiency indicated that back-propagation ANN can be used for the modelling of batch adsorption kinetics.

Keywords: resorcinol adsorption; rice husk ash; modelling; artificial neural networks; ANNs; validation; water; resorcinol removal; modelling; batch adsorption kinetics. (search for similar items in EconPapers)
Date: 2011
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