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首页> 外文期刊>RSC Advances >Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach
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Random forest model for the ultrasonic-assisted removal of chrysoidine G by copper sulfide nanoparticles loaded on activated carbon; response surface methodology approach

机译：用铜硫化铜纳米粒子加载碳硫含量超声波辅助除去氯苯G的随机森林模型; 响应面方法方法

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摘要

Copper sulfide nanoparticle-loaded activated carbon (CuS-NP-AC) was prepared and used as an adsorbent for the accelerated removal of chrysoidine G (CG) assisted by ultrasound. This nanomaterial was characterized by FE-SEM, BET and XRD. The effects of variables such as initial CG concentration (mg L-1), adsorbent amount (g) and sonication time (s) on CG removal were investigated and optimized by using central composite design (CCD) under response surface methodology (RSM). The Langmuir isotherm was applied to describe well the experimental equilibrium data with high figures of merit. The mass transfer mechanism of time varying adsorption was shown to be described by the second-order equation model. The random forest (RF) model applied to the experimental data was shown to be highly applicable to predict CG adsorption onto CuS-NP-AC. The optimal tuning parameters for the RF model were obtained based on 100 and 2 for n(tree) and m(try), respectively. For the training dataset, the values of MSE and the coefficient of determination (R-2) were found to be 0.0021 and 0.9657, respectively, while they were determined as 0.0069 and 0.8976 for the testing dataset. It was found that a small adsorbent amount (0.03 g) is applicable for efficient removal of CG (RE > 94%) in a short time (360 s) with reasonably high adsorption capacity (89.3 mg g(-1)).
机译：制备硫化铜纳米粒子 - 纳米颗粒加工的活性炭（CUS-NP-AC）并用作通过超声波辅助的加速除去蛹G（CG）的吸附剂。该纳米材料的特征在于Fe-SEM，BET和XRD。研究和优化诸如初始CG浓度（Mg L-1），吸附量（G）和超声处理时间的变异效果，并通过使用中央复合设计（CCD）在响应面法（RSM）下进行了优化。应用Langmuir等温线描述了具有高图数的实验性平衡数据。时间改变吸附的传质机制显示由二阶等式模型描述。应用于实验数据的随机森林（RF）模型被证明是高度适用于将CG吸附预测到CU-NP-AC。基于N（树）和M（尝试）的100和2获得RF模型的最佳调谐参数。对于训练数据集，发现MSE的值和测定系数（R-2）分别为0.0021和0.9657，而它们被确定为0.0069和0.8976，用于测试数据集。发现小吸附量（0.03g）适用于在短时间（360秒）中有效除去Cg（Re> 94％），具有合理的吸附能力（89.3mg g（-1））。

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  • 来源
    《RSC Advances》 |2015年第73期|共9页
  • 作者

    Bagheri A. R.; Ghaedi M.; Hajati S.; Ghaedi A. M.; Goudarzi A.; Asfaram A.;

  • 作者单位

    Univ Yasuj Dept Chem Yasuj 7591874831 Iran;

    Univ Yasuj Dept Chem Yasuj 7591874831 Iran;

    Univ Yasuj Dept Phys Yasuj 7591874831 Iran;

    Islamic Azad Univ Gachsaran Branch Dept Chem Gachsaran 7581863876 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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