• 文献标题:   Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks
  • 文献类型:   Article
  • 作  者:   RATNAM MV, MURUGESAN MP, KOMARABATHINA S, SAMRAJ S, ABDULKADIR M, KALIFA MA
  • 作者关键词:  
  • 出版物名称:   INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING
  • ISSN:   1687-806X EI 1687-8078
  • 通讯作者地址:  
  • 被引频次:   1
  • DOI:   10.1155/2022/5759394
  • 出版年:   2022

▎ 摘  要

To mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV-irradiated nano-graphene oxide (UV/n-GO) was developed and studied for methylene blue (MB) adsorption. Furthermore, the batch adsorption studies were modelled using response surface modelling (RSM) and artificial neural networks (ANNs). Investigations employing FTIR, XRD, and SEM were carried out to characterize the adsorbent. The best MB removal of 95.81% was obtained at a pH of 6, a dose of 0.4 g/L, an MB concentration of 25 mg/L, and a period of 40 min. This was accomplished with a desirability score of 0.853. A three-layer backpropagation network with an ideal structure of 4-4-1 was used to create an ANN model. The R-2 and MSE values determined by comparing the modelled data with the experimental data were 0.9572 and 0.00012, respectively. The % MB removal predicted by ANN was 94.76%. The kinetics of adsorption corresponded well with the pseudo-second-order model (R-2 > 0.97). According to correlation coefficients, the order of adsorption isotherm models is Redlich-Peterson > Temkin > Langmuir > Freundlich. Thermodynamic investigations show that MB adsorption was both spontaneous and endothermic.