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Materials 2017, 10(11), 1279; doi:10.3390/ma10111279

Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

1
Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
2
Cultivation Base of Guizhou National Key Laboratory of Mountainous Karst Eco-Environment, Guizhou Normal University, Guiyang 550001, China
3
Department of Applied Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Received: 26 October 2017 / Accepted: 4 November 2017 / Published: 7 November 2017
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Abstract

Reduced graphene oxide-supported Fe3O4 (Fe3O4/rGO) composites were applied in this study to remove low-concentration mercury from aqueous solutions with the aid of an artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. The Fe3O4/rGO composites were prepared by the solvothermal method and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), atomic force microscopy (AFM), N2-sorption, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR) and superconduction quantum interference device (SQUID). Response surface methodology (RSM) and ANN were employed to model the effects of different operating conditions (temperature, initial pH, initial Hg ion concentration and contact time) on the removal of the low-concentration mercury from aqueous solutions by the Fe3O4/rGO composites. The ANN-GA model results (with a prediction error below 5%) show better agreement with the experimental data than the RSM model results (with a prediction error below 10%). The removal process of the low-concentration mercury obeyed the Freudlich isotherm and the pseudo-second-order kinetic model. In addition, a regeneration experiment of the Fe3O4/rGO composites demonstrated that these composites can be reused for the removal of low-concentration mercury from aqueous solutions. View Full-Text
Keywords: water treatment; mercury; artificial neural networks; genetic algorithm; artificial intelligence water treatment; mercury; artificial neural networks; genetic algorithm; artificial intelligence
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cao, R.; Fan, M.; Hu, J.; Ruan, W.; Xiong, K.; Wei, X. Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm. Materials 2017, 10, 1279.

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