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Application of ANN and EA for description of metal ions sorption on chitosan foamed structure—Equilibrium and dynamics of packed column

机译:ANN和EA在描述金属离子在壳聚糖泡沫结构上的吸附—填充柱的平衡和动力学

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In this study, a multi-component sorption equilibria calculation, with application of artificial neural network (ANN) and identification of adsorption dynamics model using evolutionary algorithm (EA), is presented. Equilibrium experiments were carried out to estimate sorptivity of a new form of a chitosan foamed structure and its selectivity towards Cu( II), Zn(ll) and Cr( VI) ions. In the case of single ions, it was found that in the whole range of concentrations, experimental data were well described by the Langmuir-Freundlich equation. In the case of a multi-component mixture the application of a neural MLP network was proposed. Calculations with the use of MLP enabled description of sorption isotherms for when one, two and three ions were present at the same time in the solution. The network also enabled an analysis of sorption of the selected ion, taking into account the effect of its concentration on the sorption of other ions. This assessment would not be possible in an experimental way only. A universal mathematical model of adsorption in a packed column is proposed in this paper. The model includes mass balances for fluid and adsorbent as well as a sorption kinetics. The effect of these, is a system of two partial differential equations. Additionally, the distance and time are composed in one relevantly defined variable. The proposed transformations convert the system of partial differential equations to a system of ordinary equations, which enables analytical solution of the equations system. Also, calculation of a concentration distribution within the solution and adsorbent, dependent on the distance from inlet, and process duration, is achieved. The data obtained in the measurements for Cu(ll), Ni(ll) and Zn(II) ions, were then compared with those obtained from the model using EA for identification of model coefficients.
机译:在这项研究中,提出了一种多组分吸附平衡计算方法,其中应用了人工神经网络(ANN)并使用进化算法(EA)识别了吸附动力学模型。进行了平衡实验,以评估一种新型壳聚糖泡沫结构的吸附性及其对Cu(II),Zn(II)和Cr(VI)离子的选择性。对于单离子,发现在整个浓度范围内,Langmuir-Freundlich方程可以很好地描述实验数据。在多组分混合物的情况下,提出了神经MLP网络的应用。使用MLP进行计算可以描述溶液中同时存在一个,两个和三个离子时的吸附等温线。该网络还考虑到其浓度对其他离子吸附的影响,从而能够分析所选离子的吸附。仅以实验方式无法进行此评估。提出了填充柱吸附的通用数学模型。该模型包括流体和吸附剂的质量平衡以及吸附动力学。这些的影响是一个由两个偏微分方程组成的系统。另外,距离和时间由一个相关定义的变量组成。提出的变换将偏微分方程组系统转换为普通方程组,从而可以对方程组进行解析。而且,实现了取决于与入口的距离和过程持续时间的溶液和吸附剂内的浓度分布的计算。然后将在测量中获得的有关Cu(II),Ni(II)和Zn(II)离子的数据与使用EA识别模型系数而从模型获得的数据进行比较。

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