Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization

Abstract

A methodology is developed for optimal remediation of groundwater aquifers under hydraulic conductivity uncertainty. A multi-objective management method based on a pump-and-treat remediation technology, is proposed. The pumping rates and well locations are the decision variables and two objectives are chosen: minimization of contaminated groundwater in the aquifer and minimization of remediation cost. A Monte Carlo simulation method is used to cope with hydraulic conductivity uncertainty. A number of equally probable realizations of hydraulic conductivity are created and a Pareto front is obtained using a modified multi-objective Genetic Algorithm. A penalty function is utilized to maintain the algebraic sum of pumping and recharging rates equal to zero. Since Monte Carlo simulations are CPU time consuming, a method is proposed to identify the few significant realizations which have an effect on the optimal solution (critical realizations). A Pareto front with an assigned probability is derived, so that the decision maker can make decisions with specified reliability. In a case study with 100 realizations, only 11 realizations were found critical and need be considered. The remaining 89 realizations consistently obtain low ranks for all designs considered and do not affect decisions at 95% reliability level. Thus these realizations need not be considered which implies a 89% savings in computer time. The designs obtained using the critical realizations, retain a similar reliability for new realizations not considered in the design process.