Combining Fuzzy Logic and Neural Networks in Modeling Landfill Gas Production

Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

Abstract:

Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061565

References:

[1] R. L., Peer, D. L., Darcy, D. L., Campbell, and P. V., Brook, "Development of an empirical model of methane emissions from landfills," U.S. EPA, Air Energy Eng. Res. Lab., NC, no. EPA/600/SR¬92/037, 1992.
[2] A. N., Findikakis, C., Papelis, C. P., Halvadakis, and J. 0., Leckie, "Modeling gas production in managed sanitary landfills," Waste Manage. Res., vol. 6, pp. 115-123, 1988.
[3] K. R., Gurijala, P., Sa, and J. A., Robinson, "Statistical modeling of methane production from landfill samples," Appl. Environ. Micro., vol. 63, no. 10, pp. 3797-3803, 1997.
[4] N. K., Copty, D., Ergene, and T. T., Onay, "Stochastic model for landfill gas transport and energy recovery," J. Environ. Eng., vol. 130, no. 9, pp. 1042-1049, 2004.
[5] A. I., Zacharof, and A. P., Butler, "Stochastic modeling of landfill leachate and biogas production incorporating waste heterogeneity: model formulation and uncertainty analysis," Waste Manage., vol. 24, pp. 453-462, 2004.
[6] S., Rendra, L., Fernandes, and M., Warith, "Fuzzy logic simulation of biodegradation of municipal solid waste in simulated aerobic and anaerobic bioreactors landfill," in Proc. 22nd Int. Conf Solid Waste Technol. Manage., Philadelphia, 2007.
[7] A., Garg, G., Achari, and R. C., Joshi, "A model to estimate the methane generation rate constant in sanitary landfills using fuzzy synthetic evaluation," Waste Manage. Res., vol. 24, pp. 363-375, 2006.
[8] J. S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE T. Syst., vol. 23, no. 3, pp. 665-685, 1993.
[9] J. S. R., Jang, and N., Gulley, Fuzzy Logic Toolbox for Use with MATLAB. Massachusetts, USA: The MathWork Inc., 1995.
[10] S., Rendra, Comparative Study of Biodegradation of Municipal Solid Waste in Simulated Aerobic and Anaerobic Bioreactor Landfills, PhD Thesis, University of Ottawa, Canada, 2007.
[11] P. I. Good, Introduction to Statistics through Resampling Methods and Microsoft Office Excel. New Jersy, USA: John Wiley & Sons, Ltd, 2005.
[12] F. G., Pohland, and B., Al-Yousfi, "Design and operation of landfills for optimum stabilization and biogas production," Water Sci. Technol., vol. 30, no. 12, pp. 117-124, 1994.
[13] L. 0., Tedeschi, "Assessment of the adequacy of mathematical models," Agr. Syst., vol. 89, pp. 225-247, 2006.
[14] H. G., Gauch, J. T. G., Hwang, and G. W., Fick, "Model evaluation by comparison of model-based predictions and measured values," Agron. J., vol. 95, no. 6, pp. 1442-1446, 2003.