화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.34, 186-197, February, 2016
DOI10.1016/j.jiec.2015.11.010  Export Citation
Modeling of quaternary dyes adsorption onto ZnO-NR-AC artificial neural network: Analysis by derivative spectrophotometry
E. Alipanahpour Dil1, M. Ghaedi1, †, A.M. Ghaedi2, A. Asfaram1, A. Goudarzi3, S. Hajati4, M. Soylak5, Shilpi Agarwal6, 7, and Vinod Kumar Gupta6, 7, †
  • 1Chemistry Department, Yasouj University, Yasouj 75914-35, Iran
  • 2Chemistry Department, Gachsaran Branch, Islamic Azad University, Gachsaran 75818-63876, Iran
  • 3Department of Polymer Engineering, Golestan University, Gorgan 49188-88369, Iran
  • 4Department of Physics, Yasouj University, Yasouj 75918-74831, Iran
  • 5Department of Chemistry, Erciyes University, 38039 Kayseri, Turkey
  • 6Department of Chemistry, Indian Institute of Technology, Roorkee 247667, India
  • 7Department of Applied Chemistry, University of Johannesburg, Johannesburg, South Africa
E-mail:,
The novel adsorbent i.e. ZnO-NR-AC was synthesized and used for the rapid removal of the quaternary dyes from the aqueous solution. The ANN model was used for the optimization and modeling of sonication time, amount of sorbent and dyes concentrations to study their simultaneous adsorption based on achievement of minimum mean squared error as criterion. The optimized parameters was found to be 4 min sonication time, 0.022 g of ZnO-NR-AC; MB, EY, CV and AO concentrations were 8.0, 9.7, 8.0 and 10.6 mg L-1possible to achieve the removal percentage of 99.89, 99.2, 99.68 and 99.45% for MB, EY, CV and AO, respectively. The analysis of variance (ANOVA) support the high suitability of achieved equation for the efficient prediction of understudy adsorption system behavior that proofed by the presence of good agreement among the predicted and experimental data. The Langmuir isotherm model with maximum adsorption capacities were 89.29, 93.46, 87.52 and 88.5 mg g-1 correspond to MB, CV, EY and AO, respectively.
Keywords:Artificial neural network;Isotherms;Kinetics;Response surface methodology;ZnO-NRs-AC
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