Fast and effective methylene blue adsorption onto graphene oxide/amberlite nanocomposite: Evaluation and comparison of optimization techniques

CİĞEROĞLU Z., KÜÇÜKYILDIZ G., Haşimoğlu A., Taktak F., Açıksöz N.

Korean Journal of Chemical Engineering, cilt.37, sa.11, ss.1975-1984, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 11
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11814-020-0600-8
  • Dergi Adı: Korean Journal of Chemical Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core
  • Sayfa Sayıları: ss.1975-1984
  • Anahtar Kelimeler: Adsorption, Amberlite Resin, Artificial Neural Network, Graphene Oxide, Random Forest Model, Response Surface Methodology
  • Uşak Üniversitesi Adresli: Evet

Özet

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.