...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon
【24h】

A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon

机译：一种杂交人工神经网络和粒子群优化，用于使用碳活性炭锌的水溶液去除危险染料辉煌绿色的预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BC removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN PSO model the coefficient of determination (R-2) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively. (C) 2014 Elsevier B.V. All rights reserved.
机译：在本研究中，硫化锌纳米粒子在活性炭（ZnS-NP-Ac）上仅在超声的情况下合成，并使用不同的技术（例如SEM和BET分析）表征。然后，该材料用于去除亮绿色（BG）。对BC去除百分比朝向包括pH，吸附剂剂量，初始染料浓度和接触时间的各种参数的依赖性进行检查和优化。通过在各种时间分析到诸如伪一阶和二阶，ELOVICH和颗粒帧内扩散模型的常规动力学模型来确定吸附机理和吸附速率。根据常规标准进行比较，例如吸附容量和相关系数的相对误差，确认伪二阶动力学模型以解释数据的可用性。 Langmuir模型可以有效地解释了吸附系统的行为，提供了与ZnS-NP-AC相互作用的完整信息。一种多元线性回归（MLR）和人工神经网络和部分群优化（ANN PSO）模型的混合用于预测ZNS-NP-AC的辉煌绿色吸附。使用所提供的模型获得的结果的比较确认了ANN模型与MLR模型的能力相比，以将BG吸附预测到ZNS-NP-AC。使用最佳ANN PSO模型，测定系数（R-2）为0.9610和0.9506;培训和测试数据集分别为0.0020和0.0022的平均方形误差（MSE）值分别为0.0020和0.0022。 （c）2014 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号