Artifical Neural Network (ANN) Modeling and Analysis of Radioactive Gallium-67 Adsorption from Aqueous Solution with Waste Acorns of Quercus ithaburensis

Eroglu H., AKTAN M., AKKAYA G.

JOURNAL OF CHEMICAL AND ENGINEERING DATA, cilt.56, sa.5, ss.1910-1917, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56 Sayı: 5
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1021/je100929z
  • Dergi Adı: JOURNAL OF CHEMICAL AND ENGINEERING DATA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1910-1917
  • Atatürk Üniversitesi Adresli: Evet

Özet

The adsorption of gallium-67, which is used in nuclear medicine, was investigated in this study by using waste acorns of Quercus ithaburensis (WAQI). The experimental parameters were determined to be as follows: temperature, (283 to 313) K; pH, (2.0 to 10.0); stirring speed, (300 to 720) rpm; particle size, (0.15 to 1.40) mm; and adsorbent dose, (1.0 to 15.0) g . L-1. The most effective parameters were pH, temperature, particle size, and adsorbent ratio. The WAQI adsorption mechanism was analyzed by Fourier transform infrared (FTIR) spectra. Adsorption kinetics were studied, and it was seen that WAQI is an excellent Ga-67 adsorbent. An artificial neural network (ANN) was constructed that closely estimates the adsorption amount against temperature, adsorbent ratio, and processing time. The R-2 value of the ANN model was 0.997.