Decolorizing brilliant green by mesoporous Pd–Fe magnetic nanoparticles immobilized on reduced graphene oxide: artificial neural network modeling

• Published in: International journal of environmental science and technology (Tehran) Vol. 19; no. 5; pp. 3935 - 3946
• Main Authors: Hou, Y., Qi, J. M., Hu, J. W., Ruan, W. Q., Xiang, Y. Q., Wei, X. H.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022
• Subjects: adsorption; algorithms; Aquatic Pollution; aqueous solutions; decolorization; Earth and Environmental Science; Ecotoxicology; endothermy; Environment; Environmental Chemistry; environmental science; Environmental Science and Engineering; graphene oxide; magnetism; nanocomposites; neural networks; Original Paper; porous media; prediction; response surface methodology; Soil Science & Conservation; sorption isotherms; thermodynamics; toxicity; Waste Water Technology; Water Management; Water Pollution Control; Backpropagation artificial neural network; Magnetic nanocomposites; Isotherm study; Kinetics study; Brilliant green
• ISSN: 1735-1472; 1735-2630
• DOI: 10.1007/s13762-021-03283-5

The mesoporous Pd–Fe magnetic nanoparticles immobilized on the reduced graphene oxide were employed in the present work for the decolorization of toxic brilliant green in aqueous phase. The decolorization process was modeled using backpropagation artificial neural network and optimized by genetic algorithm and particle swarm optimization. These magnetic nanocomposites were synthesized by the two-step reaction in aqueous phase method and then characterized with various methods. According to response surface methodology, the effect of operating parameters on the decolorization of brilliant green in aqueous solution was studied through batch experiments. On the basis of these experiments, the prediction ability of response surface methodology and backpropagation neural network method was assessed. The decolorization process follows Freundlich isotherm and pseudo-second-order kinetics. Furthermore, thermodynamics studies demonstrate that the adsorption of brilliant green onto the nanocomposites was endothermic and spontaneous. Overall, these mesoporous nanomaterials have the advantages of strong adsorption capacity and fast decolorization for brilliant green, and modeling of the removal process with artificial neural network was successful.