화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.37, No.11, 1975-1984, November, 2020
DOI10.1007/s11814-020-0600-8  Export Citation
Fast and effective methylene blue adsorption onto graphene oxide/amberlite nanocomposite: Evaluation and comparison of optimization techniques
Zeynep Cigeroglu†, Gürkan Küçükyıldız1, Aydın Haşimoğlu2, Fulya Taktak, and Nazlıcan Açıksöz
  • Engineering Faculty, Department of Chemical Engineering, Usak University, 64200 Usak, Turkey, Korea
  • 1Engineering Faculty, Department of Electrical and Electronics Engineering, Uşak University, 64200 Uşak, Turkey
  • 2Material Science and Engineering Department, Engineering Faculty, Gebze Technical University, 41400 Gebze, Kocaeli, Turkey
E-mail:
Since graphene is a miracle material of the 21st century, a considerable number of researchers have studied the oxidation of graphite to synthesize graphene oxide and its applications. In this study, polymeric resin (amberlite XAD7HP) supported graphene oxide (GO) nanocomposite was synthesized successfully. Analytical methods, namely Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were utilized to characterize the new structure. Methylene blue (MB) solution was selected as a model discharged textile wastewater for adsorption application of synthesized nanocomposite. The adsorption data were modelled by response surface methodology (RSM), random forest (RF) and artificial neural networks (ANN) methods. The optimal condition parameters, which maximize the adsorption uptake capability, were determined by the genetic algorithm. Statistical errors and correlation coefficient values of each developed model were calculated independently to compare models’ performance. According to the results, the developed RF model outperformed the other models. On the other hand, the ANN model had the lowest correlation coefficient value among the models.
Keywords:Adsorption;Amberlite Resin;Graphene Oxide;Response Surface Methodology;Random Forest Model;Artificial Neural Network
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