Abstract: 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.
- Fig. 4. Results for Case 1a: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
- Fig. 7. Results for Case 5a: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
- Fig. 8. Results for case 5b: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
- Fig. 9. Results for Case 6: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
- by Jack Parkera,
,
, Ungtae Kima, Peter Kitanidisb, Mike Cardiffc, Xiaoyi Liub, Greg Beyked
- a Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA
- b Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
- c Department of Geosciences, Boise State University, Boise, ID, USA
- d TRS Group, Inc., Nashville, TN, USA
- Environmental Modelling & Software via Elsevier Science Direct www.ScienceDirect.com
- Volume 38; December, 2012; Pages 74–88
- Keywords: Stochastic optimization; Uncertainty analysis; DNAPL; Model calibration; Thermal source treatment; Enhanced bioremediation; Remediation cost
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