[Neural network modeling and prediction of methane fraction in biogas from landfill bioreactors]

A number of different technologies have recently been studied to determine the best use of biogas, however, to choose optimize technologies of using biogas for energy recovery it is necessary to monitor and predict the methane percentage of biogas. In this study, a method is proposed for predicting the methane fraction in landfill gas originating from Labscale landfill bioreactors, based on neural network. In this study, two different systems were applied, to predict the methane fraction in landfill gas as a final product of anaerobic digestion, we used the leachate specifications as input parameters. 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. There is very good agreement in the trends between forecasted and measured data. R values are 0.999 and 0.997, and the obtained Root mean square error values are 1.098 and 2.387 for training and test data, respectively The proposed method can significantly predict the methane fraction in landfill gas originating and, consequently, neural network can be use to optimize the dimensions of a plant using biogas for energy [i.e. heat and/or electricity] recovery and monitoring system