Artificial neural network modeling for removal of azo dye from aqueous solutions by Ti anode coated with multiwall carbon nanotubes
作者： Farideh Nabizadeh Chianeh , Jalal Basiri Parsa , Hadi Rezaei Vahidian
DOI: 10.1002/EP.12650
关键词:
摘要: In this study, a titanium based electrode, coated with multiwall carbon nanotubes (MWCNTs/Ti) was applied as anode in laboratory-made electrochemical reactor. The MWCNTs/Ti electrode prepared by the electrophoretic deposition (EPD) method an aqueous solution and characterized field emission scanning electron microscopy. used decolorization of C.I. Acid Red 33 typical target pollutant solutions. Effect pH, current density, reaction time evaluated on color removal efficiency. experimental studies revealed that efficiency TOC were 90 15%, respectively, at optimum conditions: pH = 8, density 5.5 mA/cm2 60 min. Also, modeled using three-layered feed-forward back propagation artificial neural network (ANN), consisting “trainbfg” learning algorithm, “tansig” transfer function hidden layer 10 neuron “purelin” output order to predict Comparison between predicted values selective data showed developed ANN model has high correlation coefficient (R2) 0.995 can acceptable accuracy. © 2017 American Institute Chemical Engineers Environ Prog,
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