Jang Ho-Young, Kang Jin-Kyu, Park Jeong-Ann, Lee Seung-Chan, Kim Song-Bae
Environmental Functional Materials and Water Treatment Laboratory, Department of Rural Systems Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
Environ Pollut. 2020 Dec;267:115583. doi: 10.1016/j.envpol.2020.115583. Epub 2020 Sep 4.
In this study, a metal organic framework MIL-100(Fe) was synthesized for rhodamine B (RB) removal from aqueous solutions. An experimental design was conducted using a central composite design (CCD) method to obtain the RB adsorption data (n = 30) from batch experiments. In the CCD approach, solution pH, adsorbent dose, and initial RB concentration were included as input variables, whereas RB removal rate was employed as an output variable. Response surface methodology (RSM) and artificial neural network (ANN) modeling were performed using the adsorption data. In RSM modeling, the cubic regression model was developed, which was adequate to describe the RB adsorption according to analysis of variance. Meanwhile, the ANN model with the topology of 3:8:1 (three input variables, eight neurons in one hidden layer, and one output variable) was developed. In order to further compare the performance between the RSM and ANN models, additional adsorption data (n = 8) were produced under experimental conditions, which were randomly selected in the range of the input variables employed in the CCD matrix. The analysis showed that the ANN model (R = 0.821) had better predictability than the RSM model (R = 0.733) for the RB removal rate. Based on the ANN model, the optimum RB removal rate (>99.9%) was predicted at pH 5.3, adsorbent dose 2.0 g L, and initial RB concentration 73 mg L. In addition, pH was determined to be the most important input variable affecting the RB removal rate. This study demonstrated that the ANN model could be successfully employed to model and optimize RB adsorption to the MIL-100(Fe).
在本研究中，合成了一种金属有机骨架MIL-100(Fe)用于从水溶液中去除罗丹明B（RB）。采用中心复合设计（CCD）方法进行实验设计，以从批量实验中获得RB吸附数据（n = 30）。在CCD方法中，将溶液pH值、吸附剂剂量和初始RB浓度作为输入变量，而将RB去除率作为输出变量。使用吸附数据进行响应面法（RSM）和人工神经网络（ANN）建模。在RSM建模中，开发了立方回归模型，根据方差分析，该模型足以描述RB吸附。同时，开发了拓扑结构为3:8:1（三个输入变量、一个隐藏层中的八个神经元和一个输出变量）的ANN模型。为了进一步比较RSM和ANN模型之间的性能，在实验条件下生成了额外的吸附数据（n = 8），这些数据是在CCD矩阵中使用的输入变量范围内随机选择的。分析表明，对于RB去除率，ANN模型（R = 0.821）比RSM模型（R = 0.733）具有更好的预测能力。基于ANN模型，预测在pH 5.3、吸附剂剂量2.0 g/L和初始RB浓度73 mg/L时，RB去除率最佳（>99.9%）。此外，确定pH是影响RB去除率的最重要输入变量。本研究表明，ANN模型可成功用于模拟和优化RB对MIL-100(Fe)的吸附。
