Genetic Algorithm Optimization for Multi-Biogas Mass Transfer in Hydrophobic Polymer Biocell

DOI: 10.4236/jsbs.2015.53007   PDF   HTML   XML   3,755 Downloads   4,238 Views  


In this work, a new municipal biocell with new operation and waste management is proposed. The proposed system is biocell that is built gradually when the waste is being disposed. Different from conventional disposal at landfills, waste is put between “hydrophobic bricks” that are perforated permeable containments filled up with porous dumping material such as styrofoam. Genetic algorithm is used to optimize a transfer function that represents input of biogas percentages and output solutions for daily mass transfer rates for biogas mixture from which mass and volume of biogas within the biocell time of service are calculated. Transfer function is obtained by fitting dynamic input-output data to the input-output solutions. Input-output data are encoded to chromosomes (1, 0 digits). These chromosomes are subjected to genetic processes as crossover and mutations. Then a process of evaluation takes place. The evaluation process entails an objective function that evaluates the squared difference between experimental and calculated values. After the chromosomes are being evaluated, they are either selected for more iteration or decoded to the solutions. The decoding process is performed on optimal chromosomes to obtain optimal solutions and required optimal transfer function. Consequently, the mass and volume of biogas within the landfill time of service are determined for any ratio of CH4:CO2 in biocell.

Share and Cite:

Qasaimeh, A. , Masoud, T. and Sharie, H. (2015) Genetic Algorithm Optimization for Multi-Biogas Mass Transfer in Hydrophobic Polymer Biocell. Journal of Sustainable Bioenergy Systems, 5, 73-81. doi: 10.4236/jsbs.2015.53007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Manna, L., Zanetti, M. and Genon, G. (1999) Modeling Biogas Production at Landfill Site. Resources Conservation and Recycling, 26, 1-14.
[2] Kightley, D., Nedwell, D.B. and Cooper, M. (1995) Capacity for Methane Oxidation in Landfill Cover Soils Measured in Laboratory-Scale Microcosms. Applied and Environmental Microbiology, 61, 592-601.
[3] Czepiel, P.M., Mosher, B., Crill, P.M. and Harris, R.C. (1996) Quantifying the Effect of Oxidation on Landfill Methane Emissions. Journal of Geophysical Research, 101, 16721-16729.
[4] Gardner, N., Manley, B. and Pearson, J. (1993) Gas Emission from Landfills and Their Contributions to Global Warming. Applied Energy, 44, 165-174.
[5] Pacey, J. (1986) Factors Influencing Landfill Gas Production. Proceeding of Joint UK/US Engineering Conference, Solihull, 28-31 October 1986, 51-59.
[6] Nozhevnikova, A., Lifshitz, A.B., Lebedev, V.S. and Zavarzin, G.A. (1993) Emission of Methane into the Atmosphere from Landfills in the Former USSR. Chemosphere, 26, 401-417.
[7] Nastev, M., Therrien, Lefebvre, R. and Gélinas, P. (2001) Gas Production and Migration in Landfills and Geological Materials. Journal of Contaminant Hydrology, 52, 187-211.
[8] Shekdar, A.V. (1997) A Strategy for the Development of Landfill Gas Technology in India. Waste Management & Research, 15, 255-266.
[9] Richards, K.M. and Aitchinson, E.M. (1990) Landfill Gas: Energy and Environmental Themes. Proceedings of the International Conference on Landfill Gas: Energy and Environment 90, Bournemouth, October 1990, 21-44.
[10] Ahmad, Q., Maria, E. and Iwona, J. (2012) Investigation of Biogas Transport in Hydrophobic Permeable Medium for Biocells. Journal of Solid Waste Technology & Management, 38, 157-168.
[11] Qasaimeh, A.R. (2006) Intelligent Novel MSW Management System for Biogas Control in Landfill. PhD Thesis, Concordia University, Montreal.
[12] Holland, J. (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
[13] Rechenberg, I. (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen evolution [Evolution Strategy: Optimization of Technical Systems According to the Principles of Biological Evolution]. Frommann-Holzboog Verlag, Stuttgart.
[14] Baron, L. (1998) Genetic Algorithm for Line Extraction. Rapport Technique EPM/RT-98/06, école Polytechnique de Montréal.
[15] Balazinski, M., Achiche, S. and Baron, L. (2000) Influences of Optimization and Selection Criteria on Genetically- Generated Fuzzy Knowledge Bases. (ICAMT2000) International Conference on Advanced Manufacturing Technology, Johor Bahru, Malaysia, August 2000, 159-164.
[16] Ronald, S.P. (1994) Preserving Diversity in Routing Genetic Algorithms: Comparisons with Hash Tagging. Technical Report, Department of Computer and Information Science, The University of South Australia, Australia.
[17] Qasaimeh, A., Elektorowicz, M. and Balazinski, M. (2012) GA-Fuzzy Decision Support System for Mercury Removal in Natural Waters. Computational Water, Energy, and Environmental Engineering, 1, 1-7.
[18] Qasaimeh, A., Abdallah, M. and Bani Hani, F. (2012) Adaptive Neuro-Fuzzy Logic System for Heavy Metal Sorption in Aquatic Environments. Journal of Water Resource and Protection, 4, 277-284.
[19] Martin, A. (2002) Genetic Optimization.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.