Efficient Groundwater Remediation System Design Subject to Uncertainty Using Robust Optimization

Abstract

Many groundwater remediation designs for contaminant plume containment are developed using mathematically based groundwater flow models. These mathematical models are most effective as predictive tools when the parameters that govern groundwater flow are known with a high degree of certainty. The hydraulic conductivity of an aquifer, however, is uncertain, and so remediation designs developed using models employing one realization of the hydraulic conductivity field have an associated risk of failure of plume containment. To account for model uncertainty attributable to hydraulic conductivity in determining an optimal groundwater remediation design for plume containment, a method of optimization called robust optimization is utilized. This method of optimization is a multiscenario approach whereby multiple hydraulic conductivity fields are examined simultaneously. By examining these fields simultaneously, the variability of the uncertainty is included in the model. To increase the efficiency of the robust optimization approach, a sampling technique is developed that allows the modeler to determine the minimum number of field realizations necessary to achieve a reliable remediation design.