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

Content Provider	Springer Nature Link
Author	Behera, Shishir Kumar Meher, Saroj Kumar Park, Hung Suck
Copyright Year	2014
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.
Starting Page	443
Ending Page	453
Page Count	11
File Format	PDF
ISSN	1618954X
Journal	Clean Technologies and Environmental Policy
Volume Number	17
Issue Number	2
e-ISSN	16189558
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-06-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks (ANNs) Food waste leachate Landfill Landfill gas Methane recovery Performance prediction Sustainable Development Industrial Chemistry/Chemical Engineering Industrial and Production Engineering Environmental Engineering/Biotechnology Environmental Economics
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Economics and Econometrics Business, Management and Accounting Management, Monitoring, Policy and Law

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