ARTIFICIAL NEURAL NETWORK MODEL FOR BIOSORPTION OF METHYLENE BLUE BY DEAD LEAVES OF POSIDONIA OCEANICA (L.) DELILE


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KALAYCI DEMİR G., Dural M. U., ALYÜRÜK H., ÇAVAŞ L.

NEURAL NETWORK WORLD, vol.22, no.5, pp.479-494, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 5
  • Publication Date: 2012
  • Doi Number: 10.14311/nnw.2012.22.029
  • Journal Name: NEURAL NETWORK WORLD
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.479-494
  • Keywords: Biosorption, modeling, artificial neural network, Posidonia oceanica, AQUEOUS-SOLUTION, INDUSTRY WASTE, RICE HUSK, REMOVAL, ADSORPTION, EQUILIBRIUM, DYE, WATER, SORPTION, MEADOWS
  • Dokuz Eylül University Affiliated: Yes

Abstract

In the present study, an alternative promising evaluation method was recommended for dead leaves of Posidonia oceanica (L.) Delile as an adsorbent for biosorption of Methylene Blue (MB). The data from batch experiments were modeled by using Artificial Neural Network (ANN). The optimal operation conditions for biosorption of MB by P. occanica dead leaves were found for pH, adsorbent dosage, temperature and initial dye concentration as 6, 0.3 g, 303 K and 50 mg/L, respectively. The adsorption reached equilibrium after 30 minutes. According to the results of sensitivity analysis, relative importance of temperature, dye concentration, pH, adsorbent dosage and process time on the biosorption of MB were 33%, 27%, 21%, 10% and 8%, respectively. Minimum mean square error (MSE) was found as 0.0169 by ANN modeling. The present study reveals a novel strategy for adsorption studies to utilize the highly accumulated biomass of dead leaves of P. oceanica in Turkish coastlines instead of burning these dead leaves.