Stochastic Cost Optimization of DNAPL Remediation - Method Description and Sensitivity Study
A modeling approach is described for optimizing the design and operation of groundwater remediation at DNAPL sites that considers uncertainty in site and remediation system characteristics, performance and cost model limitations, and measurement uncertainties that affect predictions of remediation performance and cost. The performance model simulates performance and costs for thermal source zone treatment and enhanced bioremediation with statistical compliance rules and real-time operational system monitoring. An inverse solution is employed to estimate model parameters, parameter covariances, and residual prediction error from site data and a stochastic cost optimization algorithm determines design and operation variables that minimize expected net present value cost over Monte Carlo realizations. The method is implemented in the program SCOToolkit. A series of applications to a hypothetical problem yielded expected cost reductions for site remediation as much as 85% compared to conventional non-optimized approaches, while also increasing the probability of achieving “no further action” status in a specified timeframe by more than 60%. Optimizing monitoring frequency for compliance wells used to make no further action determinations as well as operational monitoring used to make decisions on individual remediation system components reveals tradeoffs between increased direct costs for sampling and analysis versus decreased construction and operating costs that arise because more data increases decision reliability. Optimizing protocols for operational monitoring and heating unit shutdown protocols for thermal source treatment (incremental versus all-or-none shutdown, soil versus groundwater sampling, number and frequency of samples) produced cost savings of more than 20%. Defining compliance based on confidence limits of a moving time window regression decreased expected cost and lowered failure probability compared to using measured extreme values over a lookback period. Uncertainty in DNAPL source delineation was found to have a large effect on the cost and probability of achieving remediation objectives for thermal source remediation.
Parker, Jack; Kim, Ungtae; Kitanidis, Peter; Cardiff, Michael; Liu, Xiaoyi; and Beyke, Greg. (2012). "Stochastic Cost Optimization of DNAPL Remediation - Method Description and Sensitivity Study". Environmental Modelling and Software, 3874-88. http://dx.doi.org/10.1016/j.envsoft.2012.05.002