Author(s)

Ahmad Qasaimeh, Maria Elektorowicz, Marek Balazinski

.
Concordia University.
Jerash University.

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.

GA; Fuzzy Logic; Decision Support System; Heavy Metals; Natural Waters

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.