Abstract
Large amount of greenhouse gases (CO2 and CH4) are generated through the disposal of municipal solid waste in landfill. Methane is an increasing concern of greenhouse gas. Complex processes taking place within the landfill leads to formation of gas which has to be managed. In order to manage this landfill gas (LFG), it is necessary to estimate it on daily basis. Conventional hard computing techniques as well as modern soft computing techniques have been used to model LFG estimation. The present study uses soft computing method of linear genetic programming (LGP), to estimate the landfill gas emission for Pune city (India). Data from the simulated laboratory-scale landfill have been used, and the temporal models are developed. Landfill gas is estimated using previous values of the gas recorded. The performance of the models has been analyzed using correlation coefficient (r) and root mean square error (RMSE). It is found that the model results are in good agreement with the actual values.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adak, A., Mazumder, D., Bandyopadhyay, P.: Simulation of a process design model for anaerobic digestion of municipal solid wastes. Int. J. Civil Env. Eng. 3(3), 177–182 (2011)
Ubavin, D., Madous, N., Milovanovic, D., Stege, G.A., Leatherwood, C.: Preliminary estimate of methane production at Belgrade MSW landfill “Vinca”. In: The 6th PSU-UNS International Conference on Engineering and Technology, pp. T7–2.5 (2013)
Abushamala, M.F.M., Basri, N.E.A., Younges, M.K.: Landfill Methane oxidation: predictive model development. J. Appl. Sci. 15(2), 283–288 (2015)
Abdallah, M., Warith, M., Narbaitz, R., Petriu, E., Kennedy, K.: Combining fuzzy logic and neural networks in modeling landfill gas production. World Acad. Sci. Eng. Technol 5, 445–451 (2011)
Rada, E.C., Ragazzi, M., Stefani, P., Schiavon, M., Torretta, V.: Modelling the potential biogas productivity range from a MSW landfill for its sustainable exploitation. Sustanaility, 482–495 (2015) (MDPI Publications, Switzerland)
Tikhe, S.S., Khare, K.C., Londhe, S.N.: Forecasting criteria air pollutants using data driven approaches: an Indian case study. IOSR J. Env. Sci. Toxicol. Food Technol. 3(5), 01–08 (2013)
Abushammala, M.F.M., Basri, N.E.A., Kadhum, A.A.H.: Review on landfill gas emission to the atmosphere. Eur. J. Sci. Res. 30(3), 427–436 (2009)
Mali, S.T., Khare, K.C., Biradar, A.H.: Estimation of methane gas emission from municipal solid waste landfill: Pune city, India. EVRJ 6, 1–11 (2012)
Londhe, S.N., Dixit, P.R.: Genetic programming: a novel computing approach in modeling water flows, pp. 1080–1089. Intech Publication (2012)
Bose, N.K., Liang, P.: Neural network fundamentals with graphs, algorithms and applications. Tata McGraw Hill Publishing Company Limited, New Delhi, India (1996)
Jang, J.S.R., Gulley, N.: Fuzzy logic toolbox for use with Matlab. The Math Work Inc., Massachusettes, USA (1995)
Koza, J.R.: Genetic Programming. MIT Press (1992)
Mali, S.T.: Anaerobic bioreactor landfill for bio-stabilization and green renewable energy generation from municipal solid waste: a case study. Ph.D. Thesis, University of Pune, India (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tikhe, K.S., Balapgol, B.S., Mali, S.T. (2019). Estimation of Landfill Gas Using Genetic Programming. In: Kalamdhad, A., Singh, J., Dhamodharan, K. (eds) Advances in Waste Management . Springer, Singapore. https://doi.org/10.1007/978-981-13-0215-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-0215-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0214-5
Online ISBN: 978-981-13-0215-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)