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首页> 美国卫生研究院文献>Materials >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
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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

机译：人工神经网络和遗传算法辅助还原氧化石墨烯负载的Fe3O4复合材料优化水溶液中低浓度脱汞

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摘要

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.
机译：本研究采用还原氧化石墨烯负载的Fe3O4（Fe3O4 / rGO）复合材料，通过人工神经网络（ANN）建模和遗传算法（GA）优化，从水溶液中去除低浓度汞。通过溶剂热法制备Fe3O4 / rGO复合材料，并通过X射线衍射（XRD），透射电子显微镜（TEM），原子力显微镜（AFM），N2吸附，X射线光电子能谱（XPS），傅里叶表征变换红外光谱（FTIR）和超导量子干涉仪（SQUID）。响应面方法（RSM）和ANN用于模拟不同操作条件（温度，初始pH，初始Hg离子浓度和接触时间）对Fe3O4 / rGO复合材料从水溶液中去除低浓度汞的影响。与RSM模型结果（预测误差低于10％）相比，ANN-GA模型结果（预测误差低于5％）与实验数据的一致性更好。低浓度汞的去除过程遵循Freudlich等温线和伪二级动力学模型。此外，Fe3O4 / rGO复合材料的再生实验表明，这些复合材料可重新用于从水溶液中去除低浓度汞。

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