Development of Experimental Results by Artificial Neural Network Model for Adsorption of Cu2+ Using Single Wall Carbon Nanotubes

Abstract

Removal of copper ions from aqueous solution using single wall carbon nanotubes (SWCNTs) as a function on pH was studied using batch technique. The results indicate that adsorption is strongly dependent on pH. The adsorption of Cu2+ on SWCNTs increases slowly with increasing pH value at pH<7.0 and then the adsorption increases rapidly with increasing pH at pH>7.0. The equilibrium adsorption data were analyzed by the Langmuir, Freundlich, and Temkin adsorption isotherm models. The Freundlich adsorption model agrees well with experimental data. The pseudo-second order kinetic was the best fit kinetic model for the experimental data. The experimental results were also constructed an artificial neural network (ANN) to predict removal of copper ions. A four-layer ANN, an input layer with four neurons, two hidden layers with 13 neurons, and an output layer with one neuron (4-8-5-1) is constructed. Different training algorithms are tested on the model proposed to obtain the best weights and bias values for ANN. Our results suggest that SWCNTs have a good potential application in environmental protection. This novel modeling tool is newly grown and has been used yet to model the above-mentioned experiments for SWCNTs.