首页    期刊浏览 2024年09月19日 星期四
登录注册

文章基本信息

  • 标题:Modeling of adsorption of Methylene Blue dye on Ho-CaWO 4 nanoparticles using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques
  • 本地全文:下载
  • 作者:Chinenye Adaobi Igwegbe ; Leili Mohmmadi ; Shahin Ahmadi
  • 期刊名称:MethodsX
  • 印刷版ISSN:2215-0161
  • 电子版ISSN:2215-0161
  • 出版年度:2019
  • 卷号:6
  • 页码:1779-1797
  • DOI:10.1016/j.mex.2019.07.016
  • 语种:English
  • 出版社:Elsevier
  • 摘要:Graphical abstractDisplay OmittedAbstractThe aim of this study is to evaluate the applicability of Ho-CaWO4nanoparticles prepared using the hydrothermal method for the removal of Methylene Blue (MB) from aqueous solution using adsorption process. The effects of contact time, Ho-CaWO4nanoparticles dose and initial MB concentration on the removal of MB were studied using the central composite design (CCD) method. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling techniques were applied to model the process and their performance and predictive capabilities of the response (removal efficiency) was also examined. The adsorption process was optimized using the RSM and the optimum conditions were determined. The process was also modelled using the adsorption isotherm and kinetic models. The ANN and RSM model showed adequate prediction of the response, with absolute average deviation (AAD) of 0.001 and 0.320 and root mean squared error (RMSE) of 0.119 and 0.993, respectively. The RSM model was found to be more acceptable since it has the lowest RMSE and AAD compared to the ANN model. Optimum MB removal of 71.17% was obtained at pH of 2.03, contact time of 15.16 min, Ho-CaWO4nanoparticles dose of 1.91 g/L, and MB concentration of 100.65 mg/L. Maximum adsorption capacity (qm) of 103.09 mg/g was obtained. The experimental data of MB adsorption on Ho-CaWO4nanoparticles followed the Freundlich isotherm and pseudo-second-order kinetic models than the other models. It could be concluded that the prepared Ho-CaWO4nanoparticles can be used efficiently for the removal of MB and also, the process can be optimized to maximize the removal of MB.•Synthesis and characterization of Ho-CaWO4nanoparticles.•Modelling and optimization of Methylene Blue removal onto Ho-CaWO4using Response Surface Methodology (RSM) and Artificial neural network (ANN).•Evaluation of the isotherm and kinetic parameters of the adsorption process.
  • 关键词:Methylene Blue;Nanoparticles;Artificial Neural Network;Adsorption;Central composite design;Response Surface Methodology
国家哲学社会科学文献中心版权所有