Dye adsorption from single and binary systems using NiO‐MnO2 nanocomposite and artificial neural network modeling. Issue 1 (31st August 2016)

• Record Type: Journal Article
• Authors: Mahmoodi, Niyaz Mohammad; Hosseinabadi‐Farahani, Zahra; Chamani, Hooman
• Is Part Of: Environmental progress & sustainable energy. Volume 36:Issue 1(2017)
• Page Start: 111
• Page End: 119
• Publication Date: 2016-08-31
• Subjects: adsorption -- artificial neural network -- nanoparticles -- dye -- wastewater treatment; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.12452
• Languages: English
• ISSNs: 1944-7442

Abstract

Abstract : NiO‐MnO2 nanocomposite was synthesized and its dye removal ability from single and binary systems was investigated. The characteristics of the synthesized nanocomposite were studied using scanning electron microscopy (SEM), X‐ray diffraction (XRD), and Fourier transform infrared (FTIR). Basic Blue 41 (BB41), Basic Red 18 (BR18), and Basic Red 46 (BR46) were used. Artificial neural network (ANN) as an intelligent system was used to model the dye removal process. The effect of operational parameters such as adsorbent dosage and initial dye concentration on dye removal was evaluated. It was found that adsorption of BB41, BR18, and BR46 on the nanocomposite followed the Langmuir, Freundlich, and Tempkin isotherms, respectively. The adsorption kinetics of dyes were found to conform to pseudo‐second‐order kinetics in both single and binary systems. The results showed that the predictions of the ANN models were in close agreement with experimental data. © 2016 American Institute of Chemical Engineers Environ Prog, 36: 111–119, 2017