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首页> 外文期刊>RSC Advances >Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye
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Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye

机译：统计实验设计，最小二乘支持向量机（LS-SVM）和人工神经网络（ANN）用于培养亚甲基蓝染料的促进吸附的方法

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摘要

This study is based on the usage of a composite of zinc sulfide nanoparticles with activated carbon (ZnS-NPs-AC) for the adsorption of methylene blue (MB) from aqueous solutions. The properties of ZnS-NPs-AC were identified by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS) and Fourier transformation infrared spectroscopy (FTIR). Response surface methodology (RSM), an artificial neural network (ANN) and the least squares-support vector machine (LS-SVM) were used for the optimization and/or modeling of pH, ZnS-NPs-AC mass, MB concentration and sonication time to develop respective predictive equations for the simulation of the efficiency of MB adsorption. The obtained results using LS-SVM and ANN exhibit two nonlinear approaches (LS-SVM and ANN models) which show better performances in comparison to central composite design (CCD) for the prediction of MB adsorption. The root mean square error (RMSE) values corresponding to the validation set for MB were 0.00013, 0.00071 and 0.00117, while the respective coefficient of determination (R-2) values were 0.9996, 0.9983 and 0.9978 for the LS-SVM, ANN and CCD models, respectively. In the training set, the RMSE values of 0.00011, 0.00065 and 0.00110 and the R-2 values of 0.9997, 0.9984 and 0.9980 were obtained using the LS-SVM, ANN and multiple linear regression (MLR) models, respectively. The significant factors were optimized using CCD combined with desirability function (DF) and genetic algorithm (GA) approaches. The obtained optimum point was located in the valid region, experimental confirmation tests were conducted and good agreement was found between the predicted and experimental data. The optimum conditions for searching for the optimum point were set as pH 7.0, 0.015 g ZnS-NPs-AC, 20 mg L-1 MB and 3 min sonication, while at this point, the removal percentages were 98.02% and 98.12% by the DF and GA approaches, respectively. The adsorption equilibrium data in all conditions according to the optimum point are represented by the Langmuir model with a maximum monolayer adsorption capacity of 243.90 mg g(-1) while, in all situations, the kinetics and rate of MB adsorption follow the pseudo-second-order kinetic model. Moreover, ZnS-NPs-AC was efficiently regenerated using methanol and, over five cycles, the removal percentage did not change significantly.
机译：本研究基于硫化锌纳米颗粒复合物与活性炭（ZnS-NPS-AC）的用途，用于吸附来自水溶液的亚甲基蓝（Mb）。通过X射线衍射（XRD），场发射扫描电子显微镜（Fe-SEM），能量分散X射线光谱（EDS）和傅立叶变换红外光谱（FTIR）鉴定ZnS-NPS-AC的性质。响应表面方法（RSM），人工神经网络（ANN）和最小二乘 - 支持向量机（LS-SVM）用于PH，ZnS-NPS-AC质量，MB浓度和超声的优化和/或建模时间来开发各个预测方程，用于模拟MB吸附效率。使用LS-SVM和ANN的得到的结果表现出两种非线性方法（LS-SVM和ANN模型），其与中央复合设计（CCD）相比，用于预测MB吸附的中央复合设计更好的性能。对应于MB的验证集的根均方误差（RMSE）值为0.00013,0.00071和0.00117，而LS-SVM，ANN和CCD的相应系数（R-2）值为0.9996,0.9983和0.9978模型分别。在训练集中，使用LS-SVM，ANN和多元线性回归（MLR）模型，获得0.00011,0.00065和0.00110的RMSE值和0.997,0.9984和0.9980的R-2值。使用CCD结合可取性功能（DF）和遗传算法（GA）方法来优化显着因素。所得最佳点位于有效区域中，进行了实验证据测试，在预测和实验数据之间发现了良好的一致性。搜索最佳点的最佳条件被设定为pH 7.0,0.015g ZNS-NPS-AC，20mg L-1 MB和3分钟的超声，而在此时，去除率为98.02％和98.12％ DF和GA接近。根据最佳点的所有条件中的吸附平衡数据由Langmuir模型表示，最大单层吸附容量为243.90mg（-1），而在所有情况下，MB吸附的动力学和速率遵循伪秒-Order动力学模型。此外，使用甲醇有效地再生ZnS-NPS-AC，超过五个循环，除去百分比不会显着变化。

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  • 来源
    《RSC Advances》 |2016年第46期|共15页
  • 作者

    Asfaram A.; Ghaedi M.; Azqhandi M. H. Ahmadi; Goudarzi A.; Dastkhoon M.;

  • 作者单位

    Univ Yasuj Dept Chem Yasuj 7591874831 Iran;

    Univ Yasuj Dept Chem Yasuj 7591874831 Iran;

    Univ Yasuj Fac Gas &

    Petr Gachsaran Appl Chem Dept Gachsaran 7591874831 Iran;

    Golestan Univ Dept Polymer Engn Gorgan 4918888369 Iran;

    Univ Yasuj Dept Chem Yasuj 7591874831 Iran;

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  • 正文语种 eng
  • 中图分类 化学;
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