G'/> Adsorption of Amido Black 10B from aqueous solution using polyaniline/SiO <ce:inf loc='post'>2</ce:inf> nanocomposite: Experimental investigation and artificial neural network modeling
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Adsorption of Amido Black 10B from aqueous solution using polyaniline/SiO 2 nanocomposite: Experimental investigation and artificial neural network modeling

机译:使用聚苯胺/ SiO的水溶液吸附Amido Black 10b 2 纳米复合材料:实验研究和人工神经网络建模

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Graphical abstractDisplay OmittedAbstractThe present work focused on the performance of Polyaniline/SiO2nanocomposite for removing Amido Black 10B dye from aqueous solution. The effect of different variables, such as adsorption time, the mass of adsorbent, solution pH and initial dye concentration was studied and also was optimized by an Artificial Neural Network (ANN) method. Lagergren, pseudo-second order, Intra-particle Diffusion, Elovich and Boyd models were tested to track the kinetics of the adsorption process. The experimental data were fitted to different two-parameter, and three-parameter isotherm models, namely, Langmuir, Freundlich, Temkin, D-R, Hill, Sips and Redlich-Peterson models, and their validity was examined. The results showed that the dye adsorption process was well described by Redlich-Peterson isotherm model. Thermodynamic studies revealed that the adsorption of Amido Black 10B onto Polyaniline/SiO2nanocomposite was endothermic. The comparison of the adsorption efficiencies obtained by the ANN model and the experimental data evidenced that the ANN model could estimate the behavior of the Amido Black 10B dye adsorption process under various conditions.]]>
机译:<![cdata [ 图形摘要 显示省略 抽象 本作本作的重点是Polyaniline / SiO 2 纳米复合材料,用于去除amido黑色10b染料水溶液。研究了不同变量,例如吸附时间,吸附剂的质量,溶液pH和初始染料浓度的影响,并通过人工神经网络(ANN)方法进行了优化。测试了Laggren,伪二次订单,粒子内扩散,ELOVICH和BOYD模型,以跟踪吸附过程的动力学。实验数据适用于不同的双参数,以及三个参数等温线模型,即Langmuir,Freundlich,Temkin,D-R,Hill,Sips和Redlich-Peterson模型以及他们的有效性。结果表明,Redlich-Peterson等温模型很好地描述了染料吸附过程。热力学研究表明,Amido黑10b的吸附到聚苯胺/ SiO中:Im Loc =“post”> 2 纳米复合材料是吸热的。通过ANN模型获得的吸附效率和实验数据的比较证明了ANN模型可以在各种条件下估算amido黑色10B染料吸附过程的行为。 ]]>

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