Remediation of heterogeneous aquifers based on multiobjective optimization and adaptive determination of critical realizations

A method for optimal remediation of heterogeneous aquifers based on stochastic simulation with adaptive determination of critical realizations is developed. Multiple hydraulic conductivity realizations are generated using the turning bands method while Monte Carlo simulation is incorporated into a multiobjective genetic optimization algorithm. In order to reduce the computational burden of Monte Carlo simulations, an adaptive procedure is developed for identifying critical realizations'' which are the ones that have an effect on the optimal solution depending on desired reliability level. The adaptive procedure is embedded into the genetic optimization algorithm where noncritical realizations are eliminated step by step during the advancement of optimization. When the number of remaining realizations reaches a minimum, some new realizations are added. The specific realizations which are removed or added in the critical set are selected by an automatic procedure controlled by a similarity criterion based on rankings of realizations. This is a significant improvement over the nonadaptive methodology for identification of critical realizations and does not require using a number of initial designs and a safety threshold. The methodology is applied in a remediation problem with two objectives (reduction of contaminant mass and cleanup cost). The applications indicate significant savings in computer time without loss of performance.