Adsorption of ethidium bromide (EtBr) from aqueous solutions by natural pumice and aluminium-coated pumice

Heibati B., Yetilmezsoy K., Zazouli M. A., Rodriguez-Couto S., Tyagi I., Agarwal S., ...Daha Fazla

JOURNAL OF MOLECULAR LIQUIDS, cilt.213, ss.41-47, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 213
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.molliq.2015.08.063
  • Dergi Adı: JOURNAL OF MOLECULAR LIQUIDS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.41-47
  • Anahtar Kelimeler: Artificial neural network, Ethidum bromide, Isotherms, Kinetics, Pumice stone, WASTE-WATER, TITANIUM-DIOXIDE, CARBON NANOTUBES, BOTTOM ASH, REMOVAL, DYE, DEGRADATION, ADSORBENT, SORPTION, PHOTODEGRADATION
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

In the present paper, the removal of ethidium bromide (EtBr) from aqueous solutions in a batch system using natural (NP) and aluminium-coated pumice (ACP) as alternative low-cost adsorbents was investigated. The maximum adsorption capacity, q(m) (mg/g) was 58.82 and 76.92 mg/g for NP and ACP, respectively, operating at an initial pH of 8, an adsorbent dose of 8 g/L, a contact time of 210 min and an initial EtBr concentration of 30 mg/L. The equilibrium data of both adsorbents fitted the Freundlich isotherm model, indicating the heterogeneity of the adsorbent surface. In addition, the adsorption rate of both adsorbents was well described by the pseudo-second-order kinetics model. This indicated chemisorption was the rate -controlling step of the adsorption process which occurred by ion exchange. Within the performed study, a three-layer artificial neural network (ANN) model was also developed to predict the efficiency of EtBr removal. Computational results clearly demonstrated that the ANN model was able to predict the combined effect of initial pH, adsorbent dose, contact time and initial EtBr concentration on the adsorption efficiency with a very high determination coefficient (R-2 = 0.998) and a low relative error (RE = 0.037). (C) 2015 Elsevier B.V. All rights reserved.