Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

YILDIZ S.

KOREAN JOURNAL OF CHEMICAL ENGINEERING, cilt.34, sa.9, ss.2423-2434, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 9
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s11814-017-0157-3
  • Dergi Adı: KOREAN JOURNAL OF CHEMICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2423-2434
  • Anahtar Kelimeler: Artificial Neural Network (ANN), Kinetics and Isotherm Study, Zn(II) Ions, Peanut Shell, HEAVY-METAL REMOVAL, AQUEOUS-SOLUTION, ACTIVATED-SLUDGE, DYE ADSORPTION, COPPER IONS, BIOSORPTION, ZINC, EQUILIBRIUM, ZN2+, KINETICS
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R-2 value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R-2 values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.