Application of artificial neural network for the removal of azo dye by Multiwall carbon nano tubes coating on titanium

The present work discusses the removal of Acid red 33 (AR33) dye from aqueous solution by electrochemical advanced oxidation process using titanium coated with multiwall carbon nanotubes as anode. The said anode was prepared by the electrophoretic deposition (EPD) in an aqueous solution method and was characterized by field emission scanning electron microscopy (FESEM). The important process parameters, such as initial pH, current density and reaction time were investigated on color removal efficiency. The optimum color and COD removal efficiency of onto MWCNTs/Ti were determined as 90% and 15% respectively, at pH = 8, current density of 5.5 mA/cm2 and reaction time of 60 min. Also, a three-layered feed forward back propagation artificial neural network (ANN) model was used for predicting removal(%) of AR33 dye based on experimental data. The coefficient of determination (R2) of 0.9959 and the mean square error (MSE) MSE of 0.306×103 confirm accuracy of the neural network for modeling and can predict the decolorization efficiency with acceptable accuracy.