Cost Optimization of DNAPL Remediation at Dover Air Force Base Site

Abstract

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.

Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance

“…The BNN upscaling framework presented in this work could be used to provide uncertainty quantification that appropriately represents both input parameter uncertainty by design, as well as uncertainty due to the upscaling model itself. Using the BNN models to predict DNAPL mass discharge observed in laboratory settings, we found that experimentally observed mass-discharge data points were within the BNN-estimated 95% confidence interval, providing evidence that usage of this upscaling methodology with an appropriately trained BNN can be used in probabilistic risk assessments of DNAPL field sites and associated decision-making frameworks (e.g., Lee et al, 2012). The BNN upscaling framework lends itself to traditional methods of improving accuracy, like Water Resources Research 10.1029/2023WR036864 parameter calibration, as well as methods involving multiple phases of NN model trainings as more information about a field site becomes available.…”

Data Analysis and Modeling to Optimize Thermal Treatment Cost and Performance

“…Owing to the large number of factors, uncertainty in true values of many properties, and complexity of interactions, ad hoc design approaches are likely to be suboptimal in terms of performance and/or cost. We wish to evaluate potential performance improvement and cost reductions for thermal treatment associated with various monitoring strategies by the application of optimization methods using the Stochastic Cost Optimization Toolkit (SCOToolkit) program (Parker et al ; Kim et al ; Lee et al ), which performs optimization analyses to determine design parameters that minimize expected (i.e., probability‐weighted) total cost to meet specified remediation criteria taking into consideration uncertainty in measurements and model predictions. The program is capable of coupling effects of various source mass reduction technologies to downgradient dissolved plume attenuation.…”