Also an adaptive nero-fuzzy interference system (ANFIS) was proposed as a prediction model to predict biogas production through kitchen wastes digester.
The selected experimental data were trained by a hybrid learning algorithm. The algorithm combines the forward pass and the backward pass. The developed model was validated and statistically analyzed using scatter diagram through actual and predicted data. The ANFIS predicted data demonstrated reasonable agreement with actual data.
Index Terms—Kitchen waste, sheep manure, batch digestion.
Kobra Salehi is with the Department of Chemical Engineering, Darab Branch, Islamic Azad University, Darab, Iran (e-mail: salehi.salehi@gmail.com). Seyed Masoom Khazraee is with the Department of Chemical Engineering, Darab Branch, Islamic Azad University, Darab, Iran (Corresponding author e-mail: khazraee.masoom@gmail.com).
Fatemeh Sadat Hoseini is with the Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran (e-mail: polytak2000@gmail.com).
Farnoush Khosravanipour Mostafazadeh is with the Environmental Research Centerin Petroleum and Petrochemical Industries, Shiraz University, Shiraz, Iran (e-mail: f.khosravanipour@gmail.com).
Cite:Kobra Salehi, Seyed Masoom Khazraee, Fatemeh Sadat Hoseini, and Farnoush Khosravanipour Mostafazadeh, "Laboratory Biogas Production from Kitchen Wastes and Applying an Adaptive Neuro Fuzzy Inference System as a Prediction Model," International Journal of Environmental Science and Development vol. 5, no. 3, pp. 290-293, 2014.