A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater


Turkdogan F. İ., Yetilmezsoy K.

JOURNAL OF HAZARDOUS MATERIALS, cilt.182, ss.460-471, 2010 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 182
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.jhazmat.2010.06.054
  • Dergi Adı: JOURNAL OF HAZARDOUS MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.460-471
  • Anahtar Kelimeler: Molasses wastewater, Up-flow anaerobic sludge blanket, Fuzzy-logic, Non-linear regression, Modeling, ARTIFICIAL NEURAL-NETWORK, MUNICIPAL SOLID-WASTE, ANAEROBIC-DIGESTION, PERFORMANCE EVALUATION, DISSOLVED-OXYGEN, SLUDGE BLANKET, COLOR REMOVAL, SOIL-EROSION, POULTRY, MANURE
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R-V), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (+/-3)% and an average volumetric TCOD removal rate of 6.87 (+/-3.93) kg TCODremoved/m(3)-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98. (C) 2010 Elsevier B.V. All rights reserved.