caa a22 4500 606170588 CHVBK 20210128100718.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s10098-014-0798-4 doi (NATIONALLICENCE)springer-10.1007/s10098-014-0798-4 Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste [Elektronische Daten] [Shishir Behera, Saroj Meher, Hung-Suck Park] 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 30m3/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. Springer-Verlag Berlin Heidelberg, 2014 Artificial neural networks (ANNs) nationallicence Food waste leachate nationallicence Landfill nationallicence Landfill gas nationallicence Methane recovery nationallicence Performance prediction nationallicence AAS : Absolute average sensitivity nationallicence AI : Artificial intelligence nationallicence ANN : Artificial neural network nationallicence BPNN : Back propagation neural network nationallicence BEP : Back error propagation nationallicence MLP : Multi layered perceptron nationallicence MC : Methane Content, % nationallicence R : Correlation coefficient nationallicence W ij : Connection weights between layers nationallicence θ ij : Bias terms nationallicence X 1, X 2 : Inputs to the neural network model nationallicence Y 1 : Output from the neural network model nationallicence N Tr : Number of data points in the training data set nationallicence N Te : Number of data points in the test data set nationallicence N : Number of cases analyzed nationallicence η : Learning rate nationallicence α : Momentum term nationallicence T c : Training cycle nationallicence N I : Number of neurons in the input layer nationallicence N O : Number of neurons in the output layer nationallicence N H : Number of neurons in the hidden layer nationallicence MCexp : Methane content, % nationallicence MCpred : Methane content, % nationallicence Behera Shishir Chemical Engineering Department, GMR Institute of Technology, Rajam, 532127, Srikakulam, Andhra Pradesh, India aut Meher Saroj Systems Science and Informatics Unit, Indian Statistical Institute, 560059, Bangalore, India aut Park Hung-Suck Center for Clean Technology and Resource Recycling, University of Ulsan, 680749, Ulsan, South Korea aut Clean Technologies and Environmental Policy Springer Berlin Heidelberg 17/2(2015-02-01), 443-453 1618-954X 17:2<443 2015 17 10098 https://doi.org/10.1007/s10098-014-0798-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10098-014-0798-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Behera Shishir Chemical Engineering Department, GMR Institute of Technology, Rajam, 532127, Srikakulam, Andhra Pradesh, India aut NATIONALLICENCE 700 1- Meher Saroj Systems Science and Informatics Unit, Indian Statistical Institute, 560059, Bangalore, India aut NATIONALLICENCE 700 1- Park Hung-Suck Center for Clean Technology and Resource Recycling, University of Ulsan, 680749, Ulsan, South Korea aut NATIONALLICENCE 773 0- Clean Technologies and Environmental Policy Springer Berlin Heidelberg 17/2(2015-02-01), 443-453 1618-954X 17:2<443 2015 17 10098