Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste

Abstract

Field-scale investigation for a period of more than four months was conducted to evaluate the performance of a landfill for biogas extraction upon the injection of food waste leachate (FWL), a liquid organic waste generated from the food waste recycling facilities in Korea. The target was set at recovering about 50–60 % methane from the landfill gas (LFG) at extraction rates varying between 10 and 30 m3/h. An application of the artificial neural network (ANN) was presented in this paper to predict the performance parameter namely methane percentage (%). The input parameters to the network were LFG extraction rate (m3/h) and landfill leachate: FWL ratio, respectively, which were obtained from the field-scale investigation. Four different back error propagation learning algorithms were used to train the ANN for a comparative analysis, and the best among them was selected. To substantiate our claim, performance of the network was analyzed for different set of training and test data points. Predictions were attained by appropriately selecting the network parameters and, adequately training the network with 130 set of data points. The accuracy of back propagation neural network (BPNN)-based model predictions was evaluated by calculating the correlation coefficient (R) and mean absolute percentage error values. The results from this predictive modeling work showed that BPNNs were able to predict the methane percentage of the LFG in an acceptable range.