Development of Artificial Neural Network Models for Biogas Production from Co-Digestion of Leachate and Pineapple Peel

Authors

  • Souwalak Jaroenpoj Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia
  • Qiming Jimmy Yu Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia
  • James Ness Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia

DOI:

https://doi.org/10.15377/2410-3624.2014.01.02.2

Keywords:

Artificial neural network models, biogas, anaerobic co-digestion, leachate, pineapple peel.

Abstract

The processes of anaerobic digestion and co-digestion are complicated and the development of computational models that are capable of simulation and prediction of anaerobic digester performances can assist in the operation of the anaerobic digestion processes and the optimization for methane production. The artificial neural network approach is considered to be an appropriate and uncomplicated modelling approach for anaerobic digestion applications. This study developed neural network models to predict the outcomes of anaerobic co-digestion of leachate with pineapple peel using experimental data. The multilayered feed forward neural network model proposed was capable of predicting the outcomes of biogas production from the anaerobic co-digestion processes with a mean squared error for validation of 2.67 x 10-2 and a R value for validation of 0.9942. The approach was found to be effective, flexible and versatile in coping with the non-linear relationships using available information.

Author Biographies

  • Souwalak Jaroenpoj, Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia
    Griffith School of Engineering
  • Qiming Jimmy Yu, Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia
    Griffith School of Engineering
  • James Ness, Griffith University, Nathan Campus, Brisbane, Queensland 4111, Australia

    Griffith School of Engineering

References

Hanrahan G. Artificial neural networks in biological and environmental analysis: CRC Press 2011. http://dx.doi.org/10.1201/b10515 DOI: https://doi.org/10.1201/b10515

Harmand J, Pons MN, Dagot C. 2012. Available from: http://apps.ensic.inpl-nancy.fr/COSTWWTP/Work_Group/ Wg1/Magdeburg/Harma nd_pres.pdf.

Pons M-N, Van Impe J. Computer Applications in Biotechnology 2004: Elsevier; 2005. http://dx.doi.org/10.1117/3.633187

Yu L, Wensel PC, Ma J, Chen S. Mathematical Modeling in Anaerobic Digestion (AD). J Bioremed Biodeg S 2013; 4: 2. DOI: https://doi.org/10.4172/2155-6199.S4-003

Kriesel D. A brief introduction to neural networks. Retrieved August 2007; 15: 2011.

Priddy KL, Keller PE. Artificial neural networks: an introduction: SPIE Press 2005. DOI: https://doi.org/10.1117/3.633187

Rabunal JR, Dorado J. Artificial neural networks in real-life applications: IGI Global 2006. http://dx.doi.org/10.4018/978-1-59140-902-1 DOI: https://doi.org/10.4018/978-1-59140-902-1

Yegnanarayana B. Artificial neural networks: PHI Learning Pvt Ltd 2009.

Rajasekaran S, Pai GV. Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications (With Cd): PHI Learning Pvt Ltd 2003.

Braspenning PJ, Thuijsman F, Weijters AJMM. Artificial neural networks: an introduction to ANN theory and practice Springer 1995. DOI: https://doi.org/10.1007/BFb0027019

Sivanandam S, Deepa S. Introduction to neural networks using Matlab 6.0: Tata McGraw-Hill Education 2006.

Parthiban R, Parthiban L. Back propagation Neural network modeling approach in the anaerobic digestion of wastewater treatment. International Journal of Environmental Sciences 2012; 2(4): 1944-51.

Strik DP, Domnanovich AM, Zani L, Braun R, Holubar P. Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox. Environmental Modelling Software. 2005; 20(6): 803-10. http://dx.doi.org/10.1016/j.envsoft.2004.09.006 DOI: https://doi.org/10.1016/j.envsoft.2004.09.006

Abu Qdais H, Bani Hani K, Shatnawi N. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources. Conservation and Recycling. 2010; 54(6):359-63. http://dx.doi.org/10.1016/j.resconrec.2009.08.012 DOI: https://doi.org/10.1016/j.resconrec.2009.08.012

Holubar P, Zani L, Hager M, Frschl W, Radak Z, Braun R. Advanced controlling of anaerobic digestion by means of hierarchical neural networks. Water Research 2002; 36(10): 2582-8. http://dx.doi.org/10.1016/S0043-1354(01)00487-0 DOI: https://doi.org/10.1016/S0043-1354(01)00487-0

Ozkaya B, Demir A, Bilgili MS. Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors. Environmental Modelling and Software 2007; 22(6): 815-22. http://dx.doi.org/10.1016/j.envsoft.2006.03.004 DOI: https://doi.org/10.1016/j.envsoft.2006.03.004

MathWorks. Neural Network Toolbox 2013. Available from: http://www.mathworks.com/help/nnet/ index.html.

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Published

2015-01-10

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How to Cite

1.
Development of Artificial Neural Network Models for Biogas Production from Co-Digestion of Leachate and Pineapple Peel. Glob. Environ. Eng. [Internet]. 2015 Jan. 10 [cited 2026 Feb. 12];1(2):42-7. Available from: https://www.avantipublishers.com/index.php/tgevnie/article/view/217

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