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Mondal A.,Indian Institute of Technology Bombay | Eldho T.I.,Indian Institute of Technology Bombay | Rao V.V.S.G.,National Geographical Research Institute
Journal of Hydrologic Engineering | Year: 2010

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. © 2010 ASCE. Source

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