Synthesis of nanostructured adsorbent and dye adsorption modeling by an intelligent model for multicomponent systems

Mahmoodi, Niyaz Mohammad; Hosseinabadi-Farahani, Zahra; Chamani, Hooman

doi:10.1007/s11814-015-0198-4

Cited by 12 publications

(1 citation statement)

References 43 publications

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“…Among these methods, adsorption is a suitable choice for wastewater treatment due to its design simplicity, ease of operation, and insensitivity to toxic substances . Several inorganic materials with various capacities were used to remove dyes . Biomaterials such chitosan as an N‐deacetylated derivative of chitin are used to remove anionic dyes.…”

Section: Introductionmentioning

confidence: 99%

Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling

Mahmoodi

Hosseinabadi-Farahani

Chamani

2016

Env Prog and Sustain Energy

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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

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Section: Introductionmentioning

confidence: 99%

Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling

Mahmoodi

Hosseinabadi-Farahani

Chamani

2016

Env Prog and Sustain Energy

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Removal of anionic dyes from aqueous solutions using polyacrylamide and polyacrylic acid hydrogels

Didehban

Hayasi

Kermajani

2017

Korean J. Chem. Eng.

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Modified electrochemical processes for industrial-scale treatment of wastewater having high TKN content

Jahromi

Elektorowicz

2020

Electrochimica Acta

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Synthesis of nanostructured adsorbent and dye adsorption modeling by an intelligent model for multicomponent systems

Cited by 12 publications

References 43 publications

Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling

Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling

Removal of anionic dyes from aqueous solutions using polyacrylamide and polyacrylic acid hydrogels

Modified electrochemical processes for industrial-scale treatment of wastewater having high TKN content

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Synthesis of nanostructured adsorbent and dye adsorption modeling by an intelligent model for multicomponent systems

Cited by 12 publications

References 43 publications

Dye adsorption from single and binary systems using NiO‐MnO2 nanocomposite and artificial neural network modeling

Dye adsorption from single and binary systems using NiO‐MnO2 nanocomposite and artificial neural network modeling

Removal of anionic dyes from aqueous solutions using polyacrylamide and polyacrylic acid hydrogels

Modified electrochemical processes for industrial-scale treatment of wastewater having high TKN content

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Product

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Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling

Dye adsorption from single and binary systems using NiO‐MnO₂ nanocomposite and artificial neural network modeling