ESTIMATION OF THE CARBON TO NITROGEN (C:N) RATIO IN COMPOSTABLE SOLID WASTE USING ARTIFICIAL NEURAL NETWORKS

BAYRAM A., Kankal M., ÖZŞAHİN T. Ş., Saka F.

FRESENIUS ENVIRONMENTAL BULLETIN, cilt.20, sa.12A, ss.3250-3257, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 12A
  • Basım Tarihi: 2011
  • Dergi Adı: FRESENIUS ENVIRONMENTAL BULLETIN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.3250-3257
  • Anahtar Kelimeler: Artificial neural network, C:N ratio, Composting, Municipal solid waste, HEATING VALUE, PREDICTION, GENERATION, GUMUSHANE, MSW
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Organic waste constitutes a majority of all municipal solid waste (MSW), a fact which yields some unfavorable results at open dumps, sanitary landfills, and incineration plants. As part of an integrated solid waste management strategy, composting could be applied to mixed collected MSWs or separately collected leaves, food, and yard wastes. The factor most crucial to successful composting is the carbon to nitrogen (C:N) ratio of the waste. This study employs two predictive models to estimate the C:N ratio of compostable MSW, an artificial neural network (ANN) and multiple linear regression (MLR). These models are based on 52 solid waste samples taken from the MSW open dumping area in Gumushane Province, Turkey. To estimate the C:N ratio, seven predictive variables were adopted. The proportions of food and yard (F&Y), and ash and scoria (A&S) waste; the moisture content (MC), the fixed carbon (FC) content, the total amount of organic matter (TOM), high calorific value (HCV), and pH. Forty-two of the samples were used for training, and the remaining ten were used to test the models. The average relative error attained by the best ANN was 6.376 %, while that attained by the MLR model was 11.002 %. The effects of TOM content, F&Y percentage, and A&S percentage on the C:N ratio were investigated by running the ANN model for a range of input variables.