El Bardiji Naoual, Ziat Khadija, Naji Ahmed, Saidi Mohamed
Laboratoire Physico-Chimie des Matériaux, Substances Naturelles et Environnement, Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier 90040, Morocco.
Laboratoire de Mathématiques et Applications, Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier 90040, Morocco.
ACS Omega. 2020 Mar 6;5(10):5105-5115. doi: 10.1021/acsomega.9b04088. eCollection 2020 Mar 17.
In this paper, the fractal-like multiexponential (f-mexp) equation was modified by introducing the fractional fractal exponent to each stage of the adsorption process. The new equation was used for the analysis of kinetic adsorption of copper onto treated attapulgite. The modeling results show that the modified f-mexp equation fits properly the kinetic data in comparison with the classical and fractal-like kinetic models tested. The effect of varying the initial concentration of the adsorbate on the kinetic parameters was analyzed. Artificial neural networks were applied for the prediction of adsorption efficiency. Outcomes indicate that the multilayer perceptron neural network can predict the removal of copper from aqueous solutions more accurately under different experimental conditions than the single-layer feedforward neural network. Single-site and multisite occupancy adsorption models were used for the analysis of experimental adsorption equilibrium data of copper onto treated attapulgite. The modeling results show that there is no multisite occupancy effect and that the equilibrium data fit well the Langmuir-Freundlich isotherm.
在本文中,通过在吸附过程的每个阶段引入分数分形指数,对类分形多指数(f-mexp)方程进行了修正。新方程用于分析铜在处理过的凹凸棒石上的动力学吸附。建模结果表明,与所测试的经典和类分形动力学模型相比,修正后的f-mexp方程能很好地拟合动力学数据。分析了改变吸附质初始浓度对动力学参数的影响。应用人工神经网络预测吸附效率。结果表明,在不同实验条件下,多层感知器神经网络比单层前馈神经网络能更准确地预测水溶液中铜的去除率。采用单位点和多位点占据吸附模型分析铜在处理过的凹凸棒石上的实验吸附平衡数据。建模结果表明不存在多位点占据效应,且平衡数据与Langmuir-Freundlich等温线拟合良好。