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首页> 外文期刊>RSC Advances >Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO(OH)-NWs-AC using response surface methodology and an artificial neural network
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Optimization of the process parameters for the adsorption of ternary dyes by Ni doped FeO(OH)-NWs-AC using response surface methodology and an artificial neural network

机译：Ni掺杂Feo（OH）-NWS-AC使用响应表面方法和人工神经网络，优化用于吸附三元染料的过程参数

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The present study deals with the simultaneous removal of chrysoidine G (CG), rhodamine B (RB) and disulfine blue (DB) by Ni doped ferric oxyhydroxide FeO(OH) nanowires on activated carbon (Ni doped FeO(OH)-NWs-AC). The adsorbent was characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) and scanning electron microscopy (SEM). Derivative spectrophotometry was used for investigation of simultaneous dye adsorption by an artificial neural network (ANN) and response surface methodology (RSM) to analyse and model their adsorption behavior. Using the ANN analysis, the optimal configuration of the ANN model for modeling of the adsorption process was found to be (6:(4-6):3). The effect of adsorption parameters such as initial pH, adsorbent mass, sonication time and initial CG, RB and DB concentration was studied using central composite design (CCD), while design results were also utilized as a training set for the ANN. After predicting the model using RSM and ANN, the two methodologies were statistically compared by their coefficient of determination, root mean square error, absolute average deviation and mean absolute error based on the validation data set. Results suggest that ANN has better prediction performance as compared to RSM. It was also found that response surface methodology (RSM) predicts the suitability of output parameters. The adsorption mechanism and process rates were investigated by analyzing time dependency data using various conventional kinetic models such as pseudo-first-order and second order, intra-particle diffusion and Elovich models and the best fit was obtained by a pseudo-secondorder kinetic model with good agreement between the equilibrium and expected adsorption data. The experimental results revealed that dye adsorption was highly linear and followed the Langmuir isotherm model with maximum adsorption capacities of 187.420 (CG), 210.170 (RB) and 235.650 mg g(-1) (DB).
机译：本研究涉及通过Ni掺杂的铁羟基氧化物FeO（OH）纳米线在活性炭（Ni掺杂Feo（OH）-NWS-AC上的氯羟基氧化物Feo（OH）纳米线的同时除去蛹G（Cg），罗丹明B（Rb）和二磺胺蓝（DB）。 ）。使用X射线衍射（XRD），场发射扫描电子显微镜（Fe-SEM）和扫描电子显微镜（SEM）的吸附剂表征。衍生分光光度法用于通过人工神经网络（ANN）和响应面方法（RSM）来分析和模拟其吸附行为的同时染料。使用ANN分析，发现了用于建模吸附过程的ANN模型的最佳配置是（6：（4-6）：3）。使用中央复合设计（CCD）研究了吸附参数如初始pH，吸附物质，超声处理时间和初始CG，RB和DB浓度，而设计结果也被用作ANN的训练。在使用RSM和ANN预测模型之后，通过其确定系数，根均方误差，基于验证数据集的绝对平均偏差和平均绝对误差进行统计数据。结果表明，与RSM相比，ANN具有更好的预测性能。还发现响应面方法（RSM）预测输出参数的适用性。通过使用诸如伪第一阶和二阶的各种传统的动力学模型分析时间依赖性数据来研究吸附机制和处理速率，颗粒内扩散和ELOVICH模型，并通过伪二次交叉动力学模型获得最佳拟合均衡和预期吸附数据之间的良好一致性。实验结果表明，染料吸附是高度线性的，然后是Langmuir等温模型，最大吸附容量为187.420（CG），210.170（RB）和235.650mg（-1）（DB）。

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