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首页> 外文期刊>Environmental progress >Artificial Neural Network and Bees Algorithm for Removal of Eosin B Using Cobalt Oxide Nanoparticle-Activated Carbon: Isotherm and Kinetics Study
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Artificial Neural Network and Bees Algorithm for Removal of Eosin B Using Cobalt Oxide Nanoparticle-Activated Carbon: Isotherm and Kinetics Study

机译：氧化钴纳米粒子活化炭去除曙红B的人工神经网络和蜜蜂算法：等温线和动力学研究

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摘要

The objective of this work is the study of adsorption of Eosin B by cobalt oxide nanoparticle loaded on activated carbon (Co_2O_3-NP-AC). This new material with high efficiency in a routine manner was synthesized in our laboratory, and its surface properties such as surface area, pore volume, and functional groups were characterized with different techniques such X-ray diffraction, Brunauer, Emmett, and Teller, and scanning electron microscopy analysis. The effect of solution pH, adsorbent dosage (0.005-0.02 g), contact time (0.5-30 min), and initial concentration of dye (30-80 mg L~(-1)) on the adsorption process was investigated. Thus, Langmuir, Freundlich, Tempkin, and D-R isothermal models are applied for fitting the experimental data, and the data well presented by Langmuir model with a maximum adsorption capacity of 588.2 mg g~(-1) at 25℃. Kinetic studies at various adsorbent dosage and initial Eosin B concentration show that maximum Eosin B removal was achieved within 18 min of the start of every experiment at most conditions. The combination of pseudo-second-order rate equation and intraparticle diffusion model (with removal more than 99%) is usable to explain the experimental data of adsorption process at all conditions. The influences of parameters including initial dye concentration, adsorbent dosage (g), and contact time on Eosin B adsorption onto cobalt oxide nanoparticles loaded on AC were investigated by multiple linear regression (MLR) and artificial neural network (ANN), and the influences of variables were optimized using Bees Algorithm. Comparison of the results obtained using introduced models showed the ANN model is better than the MLR model for prediction of Eosin B removal using cobalt oxide nanoparticles loaded on AC. Using the optimal ANN model, the coefficients of determination (R~2) were 0.9965 and 0.9936; mean squared error values were 0.00015 and 0.00029 for training and testing data, respectively.
机译：这项工作的目的是研究负载在活性炭（Co_2O_3-NP-AC）上的氧化钴纳米颗粒对曙红B的吸附。在我们的实验室中以常规方式高效合成了这种新材料，并使用不同的技术（例如X射线衍射，Brunauer，Emmett和Teller）表征了其表面性质（例如表面积，孔体积和官能团），并且扫描电子显微镜分析。研究了溶液pH，吸附剂用量（0.005-0.02 g），接触时间（0.5-30 min）和染料初始浓度（30-80 mg L〜（-1））对吸附过程的影响。因此，应用Langmuir，Freundlich，Tempkin和D-R等温模型拟合实验数据，Langmuir模型所提供的数据在25℃下的最大吸附容量为588.2 mg g〜（-1）。在各种吸附剂剂量和初始曙红B浓度下进行的动力学研究表明，在大多数条件下，每次实验开始后18分钟内即可达到最大曙红B去除率。伪二级速率方程和颗粒内扩散模型（去除率超过99％）的结合可用于解释在所有条件下的吸附过程的实验数据。通过多元线性回归（MLR）和人工神经网络（ANN）研究了初始染料浓度，吸附剂量（g）和接触时间等参数对曙红B吸附在负载AC的氧化钴纳米颗粒上的影响。使用Bees算法优化变量。使用引入的模型获得的结果的比较表明，对于使用负载在AC上的氧化钴纳米颗粒预测曙红B的去除，ANN模型优于MLR模型。使用最佳人工神经网络模型，确定系数（R〜2）为0.9965和0.9936；训练和测试数据的均方误差值分别为0.00015和0.00029。

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  • 来源
    《Environmental progress》 |2015年第1期|155-168|共14页
  • 作者

    Mehrorangm Ghaedi; Amin Ansari; Payman Assefi Nejad; Abdolmohammad Ghaedi; Azam Vafaei; Mohammad Hossein Habibi;

  • 作者单位

    Chemistry Department, Yasouj University Yasouj, 75914-35, Iran;

    Department of Chemistry, Science and Research Branch, Islamic Azad University, Fars, Iran;

    Department of Chemistry, Science and Research Branch, Islamic Azad University, Fars, Iran;

    Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran;

    Department of Chemistry, Gachsaran Branch, Islamic Azad University, P.O. Box 75818-63876, Gachsaran, Iran;

    Nanotechnology Laboratory, Department of Chemistry, University of Isfahan, Isfahan, 81746-73441 I.R. Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cobalt Oxide Nanoparticles; Eosin B; Artificial Neural Network; Bees Algorithm; Kinetics; Isotherm;

    机译：氧化钴纳米粒子;曙红B;人工神经网络;蜜蜂算法;动力学;等温线;

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