Synthesis of CuO–NiO nanocomposite and dye adsorption modeling using artificial neural network. Issue 37 (8th August 2016)

• Record Type: Journal Article
• Authors: Mahmoodi, Niyaz Mohammad; Hosseinabadi-Farahani, Zahra; Bagherpour, Farzaneh; Khoshrou, Mohammad Reza; Chamani, Hooman; Forouzeshfar, Fahimeh
• Is Part Of: Desalination and water treatment. Volume 57:Issue 37(2016)
• Page Start: 17220
• Page End: 17229
• Publication Date: 2016-08-08
• Subjects: Synthesis -- CuO–NiO nanocomposite -- Dye removal modeling -- Artificial neural network -- Wastewater; Saline water conversion -- Periodicals; Saline water conversion; Water -- Purification; Periodicals; 628.167
• DOI: 10.1080/19443994.2015.1086895
• Languages: English
• ISSNs: 1944-3994

Abstract

Abstract: In this paper, CuO–NiO nanocomposite was synthesized and used to remove cationic dyes from wastewater. The scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction were used to characterize the nanocomposite. Basic Red 18 (BR18) and Basic Blue 41 (BB41) were used as cationic dyes. Artificial neural network (ANN) model was used to predict the efficiency of dye removal. The effect of adsorbent dosage and dye concentration on dye removal was evaluated. The studied operating variables were used as the input to the constructed neural network to predict the dye removal at any time as the output or the target. The backpropagation neural network with Levenberg–Marquardt training algorithm was used to predict adsorption efficiency with a tangent sigmoid transfer function (tansig) at hidden layer and a linear transfer function (purelin) at output layer. The results showed the dye adsorption kinetics followed pseudo-second-order kinetics model. Dye removal isotherm was fitted with Temkin and Freundlich models for BB41 and BR18, respectively. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient. In addition, ANN modeling could effectively predict and simulate the behavior of the process.