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Stochastic Cost Optimization of Multistrategy DNAPL Site Remediation
Author(s) -
Parker Jack,
Kim Ungtae,
Kitanidis Peter K.,
Cardiff Michael,
Liu Xiaoyi
Publication year - 2010
Publication title -
groundwater monitoring and remediation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 47
eISSN - 1745-6592
pISSN - 1069-3629
DOI - 10.1111/j.1745-6592.2010.01287.x
Subject(s) - environmental remediation , environmental science , reliability engineering , offset (computer science) , computer science , engineering , contamination , ecology , biology , programming language
This paper investigates numerical optimization of dense nonaqueous phase liquid (DNAPL) site remediation design considering effects of prediction and measurement uncertainty. Results are presented for a hypothetical problem involving remediation using thermal source reduction (TSR) and bioremediation with electron donor (ED) injection. Pump‐and‐treat is utilized as a backup measure if compliance criteria are not met. Remediation system design variables are optimized to minimize expected net present value (ENPV) cost. Adaptive criteria are assumed for real‐time control of TSR and ED duration. Source zone dissolved concentration data enabled more reliable and lower cost operation of TSR than soil concentration data, but using both soil and dissolved data improved results sufficiently to more than offset the additional cost. Decisions to terminate remediation and monitoring or to initiate pump‐and‐treat are complicated by measurement noise. Simultaneous optimization of monitoring frequency, averaging period, and lookback periods to confirm decisions, in addition to remediation design variables, reduced ENPV cost. Results indicate that remediation design under conditions of uncertainty is affected by subtle interactions and tradeoffs between design variables, compliance rules, site characteristics, and uncertainty in model predictions and monitoring data. Optimized designs yielded cost savings of up to approximately 50% compared with a nonoptimized design based on common engineering practices. Significant improvements in accuracy and reductions in cost were achieved by recalibrating the model to data collected during remediation and re‐optimizing design variables. Repeating this process periodically is advisable to minimize total costs and maximize reliability.