International Journal of Health Studies2423-65941220150922Prediction of Methane Fraction in Biogas from Landfill Bioreactors by Neural Network Modeling913ENAllahbakhshJavid11 Dept. of Environmental Health Engineering, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran.. firstname.lastname@example.orgMajidArabameri2AliakbarRoudbari3*2015062820150706Background: Predicting the methane percentage of biogas is necessary for selecting the optimized technologies of using landfill biogas for energy. The aim of this study was to predict of methane fraction in biogas from landfill bioreactors by Artificial Neural Network (ANN) modeling.Methods: In this study, two different systems were applied to predict the methane fraction in landfill gas as a final product of anaerobic digestion, in system I (C1), the leachate generated from a fresh-waste reactor was drained to recirculation tank, and recycled. In System II (C2), the leachate generated from a fresh waste landfill reactor was fed through a well-decomposed refuse landfill reactor, and at the same time, the leachate generated from a well-decomposed refuse landfill reactor recycled to a fresh waste landfill reactor. We monitored the systems for 6 months, after which we modeled the methane fraction in landfill gas from the bioreactors using artificial neural networks. The leachate specifications were used as input parameters. Leachate samples were collected every 7 days from effluent port of each reactor. COD and NH4 were determined according to the Standard Methods (2005). The pH value was measured by a portable digital pH meter (Salemab, Iran). Results: There is very good agreement in the trends between predicted and measured data. R values are 0.991 and 0.993, and the obtained mean square error values are 1.046 and 2.117 for training and test data, respectively. Conclusions: ANN based approaches can be considered as a compromising approach in landfill gas prediction problem and can be used to optimize the dimensions of a plant using biogas for energy (i.e. heat and/or electricity) recovery and monitoring system.