Multiobjective Groundwater Remediation System Design Using Coupled Finite-Element Model and Nondominated Sorting Genetic Algorithm II
This article has a reply.
VIEW THE REPLYThis article has a reply.
VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 15, Issue 5
Abstract
The optimal design of a groundwater remediation system using the pump-and-treat method is a complex task involving modeling physical phenomena such as groundwater flow and contaminant transport and optimizing several goals of concern, while satisfying bounds on certain parameters. Simulation tools such as the finite-element method (FEM) coupled with optimization tools such as the genetic algorithm (GA) has been found to be an efficient and easy to use methodology to solve such arduous problems. In this study, a simulation model using the FEM for groundwater flow and contaminant transport has been developed and coupled with a multiobjective optimization model based on the nondominated sorting genetic algorithm II (NSGA II). The model is used to minimize the cost optimization function as well as the time period for the remediation of the aquifer subject to bounds on pumping rates, groundwater heads, and concentration levels of the contaminant. The coupled FEM-NSGA II model has been applied for the remediation of a real field aquifer near Vadodara, India for a combination of flushing and pumping to demonstrate the effectiveness of the proposed multiobjective technique for achieving the optimal pumping policy. Total dissolved solids are considered to be the main pollutant. Three alternative remediation design scenarios with different pumping well locations have been compared and the best remediation design scenario has been identified to be the one that gives the best Pareto-optimal front. The FEM-NSGA II in general evolves a set of well-spread and consistent Pareto-optimal solutions, which represent the best designs that identify the trade-off between the expected cost and the time period of remediation for all the remediation design scenarios while satisfying the constraints, thus, giving the decision maker a wide set of choices.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers are thankful to Dr. S. M. V. Sharief, former Research Scholar, Department of Civil Engineering, IIT Bombay for his help. Furthermore, the writers are grateful to the anonymous reviewers and the editors for their thoughtful and constructive comments leading to the improved paper.
References
Bear, J. (1979). Hydraulics of groundwater, McGraw-Hill, New York.
Beckford, O., and Hilton, A. B. C. (2004). “Comparing formulations for multi-objective groundwater remediation design under uncertainty.” Proc., ASCE EWRI 2004 World Water and Environmental Resources Congress, ASCE, Salt Lake City.
Beckford, O., Hilton, A. B. C., and Liu, X. (2003). “Development of an enhanced multiobjective robust genetic algorithm for groundwater remediation design under uncertainty.” Proc., ASCE EWRI 2003 World Water and Environmental Resources Congress, ASCE, Philadelphia.
Carlos, A. C. C. (2006). “Evolutionary multi-objective optimization: A historical view of the field.” IEEE Computational Intelligence Magazine, 1(1), 28–36.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, London.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
Erickson, M., Mayer, A., and Horn, J. (2002). “Multi-objective optimal design of groundwater remediation systems: Application of the niched Pareto genetic algorithm (NPGA).” Adv. Water Resour., 25, 51–65.
Gunduz, O., and Aral, M. M. (2005). “A Dirac- function notation for source/sink terms in groundwater flow.” J. Hydrologic Eng., 10(5), 420–427.
He, X., and Ren, L. (2006). “A modified multiscale finite-element method for well-driven flow problems in heterogeneous porous media.” J. Hydrol., 329, 674–684.
Hossain, M. A., and Yonge, D. R. (1998). “Modeling contaminant transport in groundwater: An optimized finite-element method.” Appl. Math. Comput., 96, 89–100.
Javadi, A. A., and Al-Najjar, M. M. (2007). “Finite-element modeling of contaminant transport in soils including the effect of chemical reactions.” J. Hazard. Mater., 143, 690–701.
Kollat, J. B., and Reed, P. M. (2006). “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design.” Adv. Water Resour., 29, 792–807.
McKinney, D. C., and Lin, M. -D. (1994). “Genetic algorithm solution of groundwater management models.” Water Resour. Res., 30(6), 1897–1906.
Morshed, J., and Kaluarachchi, J. J. (2000). “Enhancements to genetic algorithm for optimal ground-water management.” J. Hydrologic Eng., 5(1), 67–73.
NGRI. (2001). “Environmental impact assessment of industrial effluents on groundwater regime around Gujarat refinery and its environs.” Tech. Rep. No. NGRI-2001-GW-303, National Geophysical Research Institute (NGRI), Hyderabad, India.
Pinder, G. F. (1973). “A Galerkin finite-element simulation of groundwater contamination on Long Island, New York.” Water Resour. Res., 9(6), 1657–1669.
Pinder, G. F., and Gray, W. G. (1977). Finite-element simulation in surface and subsurface hydrology, Academic, New York.
Reed, P., and Minsker, B. S. (2004). “Striking the balance: Long-term groundwater monitoring design for conflicting objectives.” J. Water Resour. Plan. Manage., 130(2), 140–149.
Reed, P., Minsker, B. S., and Goldberg, D. E. (2001). “A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data.” J. Hydroinform., 3(2), 71–90.
Reed, P., Minsker, B. S., and Goldberg, D. E. (2003). “Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm-II.” Water Resour. Res., 39(7), 1196.
Ritzel, B. J., Ranjithan, S., and Eheart, J. W. (1994). “Using genetic algorithm to solve a multiple objective groundwater pollution containment problem.” Water Resour. Res., 30(5), 1589–1603.
Shalabey, M. E. E., Kashyap, D., and Sharma, A. (2006). “Numerical model of saltwater transport toward a pumping well.” J. Hydrologic Eng., 11(4), 306–318.
Sharief, S. M. (2007). “Groundwater remediation strategies using FEM-GA simulation optimization models.” Ph.D. thesis, Indian Institute of Technology Bombay, India.
Sheng, D., and Smith, D. W. (2002). “2D finite-element analysis of multicomponent contaminant transport through soils.” Int. J. Geomech., 2(1), 113–134.
Singh, A., and Minsker, B. (2004). “Uncertainty based multi-objective optimization of groundwater remediation at the Umatilla chemical depot.” Proc., ASCE EWRI 2004 World Water and Environmental Resources Congress, ASCE, Salt Lake City.
Singh, A., and Minsker, B. S. (2008). “Uncertainty-based multiobjective optimization of groundwater remediation design.” Water Resour. Res., 44(2), W02404.
Singh, A., Minsker, B. S., and Valochi, A. J. (2008). “An interactive multi-objective optimization framework for groundwater inverse monitoring.” Adv. Water Resour., 31(10), 1269–1283.
Srinivas, N., and Deb, K. (1994). “Multi-objective optimization using non-dominated sorting in genetic algorithm.” Evol. Comput., 2(3), 221–248.
Wang, H., and Anderson, M. P. (1982). Introduction to groundwater modeling finite-difference and finite-element methods, W. H. Freeman, New York.
Wang, M., and Zheng, C. (1997). “Optimal remediation policy selection under general conditions.” Water Resour. Res., 35(5), 757–764.
Yoon, J. H., and Shoemaker, C. A. (2001). “Improved real-coded GA for groundwater bioremediation.” J. Comput. Civ. Eng., 15(3), 224–231.
Yu, F. X., and Singh, V. P. (1995). “Improved finite-element method for solute transport.” J. Hydraul. Eng., 121(2), 145–158.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 15 • Issue 5 • May 2010
Pages: 350 - 359
Copyright
© 2010 ASCE.
History
Received: Apr 30, 2009
Accepted: Oct 2, 2009
Published online: Apr 15, 2010
Published in print: May 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.