Monday, October 1, 2012

Stochastic cost optimization of DNAPL remediation – Method description and sensitivity study

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.
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Fig. 4. Results for Case 1a: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
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Fig. 7. Results for Case 5a: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
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Fig. 8. Results for case 5b: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost).
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Fig. 9. Results for Case 6: (a) TCE concentrations at compliance well and (b) NPV cost distribution (without penalty cost). 

by Jack Parkera, Corresponding author contact information, E-mail the corresponding author, 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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