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

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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.

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Abbreviations

AAS:

Absolute average sensitivity

AI:

Artificial intelligence

ANN:

Artificial neural network

BPNN:

Back propagation neural network

BEP:

Back error propagation

MLP:

Multi layered perceptron

MC:

Methane Content, %

R:

Correlation coefficient

Wij :

Connection weights between layers

θij :

Bias terms

X1, X2:

Inputs to the neural network model

Y1:

Output from the neural network model

N Tr :

Number of data points in the training data set

N Te :

Number of data points in the test data set

N :

Number of cases analyzed

η :

Learning rate

α :

Momentum term

T c :

Training cycle

N I :

Number of neurons in the input layer

N O :

Number of neurons in the output layer

N H :

Number of neurons in the hidden layer

MCexp:

Methane content, %

MCpred:

Methane content, %

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Acknowledgments

The authors would acknowledge the University of Ulsan, South Korea, KICOX (Grant no. 2005-B029-01), South Korea and GMR Institute of Technology, Rajam (Autonomous), India for their support in this field of experimental and modeling research.

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Authors and Affiliations

  1. Chemical Engineering Department, GMR Institute of Technology, Rajam, Srikakulam, 532127, Andhra Pradesh, India

    Shishir Kumar Behera

  2. Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, 560059, India

    Saroj Kumar Meher

  3. Center for Clean Technology and Resource Recycling, University of Ulsan, Ulsan, 680749, South Korea

    Hung-Suck Park

Authors
  1. Shishir Kumar Behera
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  2. Saroj Kumar Meher
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  3. Hung-Suck Park
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Correspondence to Hung-Suck Park.

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Behera, S.K., Meher, S.K. & Park, HS. Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste. Clean Techn Environ Policy 17, 443–453 (2015). https://doi.org/10.1007/s10098-014-0798-4

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  • DOI: https://doi.org/10.1007/s10098-014-0798-4

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