Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors

Ozkaya B., Demir A., Bilgili M. S.

ENVIRONMENTAL MODELLING & SOFTWARE, cilt.22, sa.6, ss.815-822, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 6
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.envsoft.2006.03.004
  • Dergi Adı: ENVIRONMENTAL MODELLING & SOFTWARE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.815-822
  • Anahtar Kelimeler: anaerobic digestion, landfill gas, leachate, methane fraction, modeling, neural network, LEACHATE RECIRCULATION, ANAEROBIC-DIGESTION, PERFORMANCE, STABILIZATION, SAMPLES, REFUSE, IMPACT, SITE, GAS
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

In this study we present a neural network model for predicting the methane fraction in landfill gas originating from field-scale landfill bioreactors. Landfill bioreactors were constructed at the Odayeri Sanitary Landfill, Istanbul, Turkey, and operated with (C2) and without (C1) leach-ate recirculation. The refuse height of the test cell was 5 m, with a placement area of 1250 M (25 m x 50 m). We monitored the leachate and landfill gas components for 34 months, after which we modeled the methane fraction in landfill gas from the bioreactors (C1 and C2) using artificial neural networks; leachate components were used as input parameters. To predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used input parameters such as pH, alkalinity, Chemical Oxygen Demand, sulfate, conductivity, chloride and waste temperature. We evaluated the anaerobic conversion efficiencies based on leachate characteristics during different time periods. We determined the optimal architecture of the neural network, and advantages, disadvantages and further developments of the network are discussed. (c) 2006 Elsevier Ltd. All rights reserved.