Cost Optimization of DNAPL Remediation at Dover Air Force Base Site
Ground Water Monitoring and Remediation
This study investigates stochastic optimization of dense nonaqueous phase liquid (DNAPL) remediation design at Dover Air Force Base Area 5 using emulsified vegetable oil (EVO) injection. The Stochastic Cost Optimization Toolkit (SCOToolkit) is used for the study, which couples semianalytical DNAPL source depletion and transport models with parameter estimation, error propagation, and stochastic optimization modules that can consider multiple sources and remediation strategies. Model parameters are calibrated to field data conditions on prior estimates of parameters and their uncertainty. Monte Carlo simulations are then performed to identify optimal remediation decisions that minimize the expected net present value (NPV) cleanup cost while maintaining concentrations at compliance wells under the maximum contaminant level (MCL). The results show that annual operating costs could be reduced by approximately 50% by implementing the identified optimal remediation strategy. We also show that recalibration and reoptimization after 50 years using additional monitoring data could lead to a further 60% reduction in annual operating cost increases the reliability of the proposed remediation actions. © 2012, The Author(s). Ground Water Monitoring & Remediation © 2012, National Ground Water Association.
Lee, Jonghyun; Liu, Xiaoyi; Kitanidis, Peter K.; Kim, Ungtae; Parker, Jack; Bloom, Aleisa; and Lyon, Robert, "Cost Optimization of DNAPL Remediation at Dover Air Force Base Site" (2012). Civil and Environmental Engineering Faculty Publications. 201.
This research was funded by the U.S. Department of Defense Strategic Environmental Research and Devel-opment Program (SERDP) Environmental Res toration Focus Area managed by Andrea Leeson under project ER-1611 entitled “Practical Cost Optimization of Characterization and Remediation Decisions at DNAPL sites with Consideration of Prediction Uncertainty.”