GA-Fuzzy Decision Support System for Mercury Removal in Natural Waters

DOI: 10.4236/cweee.2012.11001   PDF   HTML     3,646 Downloads   8,423 Views   Citations


The idea of this research is to apply sustainability and augment efficiency of the aquatic systems by intelligent tools. This paper exploits fuzzy logic approach as a flexible methodology for providing supplementary information about mercury removal in natural waters. Fuzzy logic generates information on Hg behaviour in water according to its uptake by bio-species and adsorption by sediments. Fuzzy Decision Support System (FDSS) comprises knowledge base (i.e. premises and conclusions), fuzzy sets, and fuzzy rules. Knowledge base and rules are being built manually and by algo- rithm. GA-FDSS incorporates genetic algorithm GA to build optimal approximation for knowledge base, fuzzy sets, and rules. The role of integrating GA with FDSS is to train knowledge base and rules automatically from available data, hence FDSS models and predicts conclusion acquired. The findings of this research show more than 95% correlation between observed data and soft computed data. The optimal biological uptake occurs at pH of 5.5. The optimal sedi-ment adsorption occurs at pH of 8. The final mercury concentration calculated in natural waters is about 7 ? 10–8 mole/L. The results show that the removal efficiency of mercury by natural waters approaches 97%. Consequently the obtained fuzzy logic informative hierarchy is proficient to manage metals removal by aquatic systems.

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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. doi: 10.4236/cweee.2012.11001.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. D. Allison, D. S. Brown and K. J. Novo-Gradac, “MINTEQA2/PRODEFA2, a Geochemical Assessments Model for Environmental Systems: Version 3.0 User’s Manual,” Environmental Research Laboratory, Athens, 1991.
[2] M. Kantrowitz, E. Horstkotte and C. Joslyn, “What is a fuzzy logic?” 1993.
[3] M. Balazinski and K. Jemielniak, “Tool Conditions Monitoring Using Fuzzy Decision Support Systems,” 5th International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw, 20-21 August 1998, pp. 115- 122.
[4] L. Baron, “Genetic Algorithm for Line Extraction,” école Polytechnique de Montréal, Montreal, 1998.
[5] A. El-Agroudy and M. Elektorowicz, “Kinetics of Inor-ganic Mercury Removal from Surface Water by Water Hyacinths and Reeds,” 34 CCSWPR, Burlington, 1999.
[6] A. El-Agroudy, “Investigation of Constructed Wetland Capability to Remove Mercury from Contaminated Wa- ters,” Ph.D. Thesis, Concordia University, Montreal, 1999.
[7] E. Maria, B. Marek and Q. Ahmad, “Application of the AI to Estimate the Constructed Wetland Response to Heavy Metal Removal,” ASCE/CSCE Conference on En-vironmental Engineering, Niagara Falls, 25-28 July 2002, p. 151.
[8] R. J. Hunter, “Introduction to Modern Colloid Science,” Oxford University Press Inc., New York, 1993.
[9] A. Qasaimeh, “Application of the Artificial Intelligence to the Design of Constructed Wetlands for Heavy Metal Removal,” Master thesis, Concordia University, Montreal, 2003.
[10] P. Stephen and J. Curtis, “Physical and Chemical Char-acteristics of Freshwater Wetland Soils,” In: D. A. Ham-mer, Ed., Constructed Wetlands for Wastewater Treat-ment: Municipal, Industrial, and Agricultural, Lewis Pub- lishers, Chelsea, 1989, pp. 41-72.
[11] K. H. Tan, “Principles of Soil Chemistry,” Marcel Dekker Inc., New York, 1982.
[12] R. N. Yong, A. M. O. Mohamed and B. P. Warketin, “Principles of Contaminant Transport in Soils,” Elsevier, Amsterdam, 1992.
[13] T. D. Reynolds, “Unit Operations and Processes in Envi-ronmental Engineering,” Brooks/Cole Engineering Divi-sion, Monterey, 1982.
[14] M. Elektorowicz and A. Qasaimeh, “Fuzzy Modeling Estimation of Mercury Removal by Wetland Compo-nents,” Processing NAFIPS ’04. IEEE Annual Meeting of the Fuzzy Information, Chicago, 27-30 June 2004, pp. 37-40.
[15] M. Balazinski, S. Achiche and L. Baron, “Influences of Optimization and Selection Criteria on Genetically-Gene- rated Fuzzy Knowledge Bases,” International Conference on Advanced Manufacturing Technology, Johor Bahru,29-30 November 2000, pp. 159-164,.
[16] L. Baron, S. Achiche and M. Balazinski, “Fuzzy Decision Support System Knowledge Base Generation Using a Genetic Algorithm,” International Journal of Approxi-mate Reasoning, Vol. 28, No. 2-3, 2001, pp. 125-148. doi:10.1016/S0888-613X(01)00047-0
[17] E. Maria, B. Marek and Q. Ahmad, “Assessment of the Capacity of Metal Sorption to Sediments of Natural Sys-tems Using Fuzzy Knowledge,” CSCE Canadian Society for Civil Engineering 31st Annual Conference, Moncton, 4-7 June 2003.
[18] G. Mitsuo and C. Runwei, “Genetic Algorithms and En-gineering Design,” Wiley, New York, 1997.
[19] J. Holland, “Adaptation in Natural and Artificial Sys- tems,” The University of Michigan Press, Ann Arbor, 1975.
[20] I. Rechenberg, “Evolutionsstrategie: Optimierung Tech- nischer Systeme Nach Prinzipien der Biologischen Evo- lution,” Frommann-Holzboog Verlag, Stuttgart, 1973.
[21] S. P. Ronald, “Preserving Diversity in Routing Genetic Algorithms: Comparisons with Hash Tagging,” Univer-sity of South Australia, Adelaide, 1994.
[22] D. Inthorn, H. Nagase, Y. Isaji, K. Hirata and K. Miya-moto, “Removal of Cadmium from Aqueous Solution by the Filamentous Cyanobacterium,” Journal of Fermenta-tion and Bioengineering, Vol. 82, No. 6, 1996, pp. 580- 584. doi:10.1016/S0922-338X(97)81256-1
[23] A. Bonde, “Fuzzy Logic Basics,” 2000.
[24] K. Vijayaraghavan and Y.-S. Yun, “Bacterial Biosorbents and Biosorption,” Biotechnology Advances, Vol. 26, No. 2, 2008, pp. 266-291. doi:10.1016/j.biotechadv.2008.02.002
[25] M. H. Jnr, “Effects of Temperature on the Sorption of Pb2+ and Cd2+ from Aqueous Solution by Caladium Bi- colour (Wild Cocoyam) Biomass,” Electronic Journal of Biotechnology, Vol. 8, No. 2, 2005, pp. 162-169.
[26] J. C. Igwe and A. A. Abia, “Maize Cob and Husk as Ad-sorbents for Removal of Cd, Pb and Zn Ions from Wastewater,” Physical Sciences, Vol. 2, 2003, pp. 83-94.
[27] A. S. Sheoran and V. Sheoran, “Heavy Metal Removal Mechanism of Acid Mine Drainage in Wetlands: A Criti-cal Review,” Minerals Engineering, Vol. 19, No. 2, 2006, pp. 105-116. doi:10.1016/j.mineng.2005.08.006
[28] K. Haarstad, H. J. Bavor and T. Maehlum, “Organic and Metallic Pollutants in Water Treatment and Natural Wet-lands: A Review,” Water Science and Technology, Vol. 65, No. 1, 2012, pp. 76-99.

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