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Optimization of Remediation Operations at Petroleum-Contaminated Sites through a Simulation-based Stochastic-MCDA Approach

Abstract A simulation-based stochastic-multi-criteria decision analysis (MCDA) approach was developed for optimizing groundwater remediation operations through integrating the contaminant transport modeling, dual-phase vacuum extraction (DPVE) process modeling, deterministic MCDA, and Monte Carlo simulation into a general framework. A petroleum-contaminated site in western Canada was selected as the study case for demonstrating the applicability of the proposed method. Totally, 12 scenarios were designed for site remediation. Nine criteria were used for evaluating each alternative through the developed MCDA methods. The economical factors consisted of operational cost associated with DPVE and groundwater remediation. Environmental performances were determined based on the magnitude of risky and highly risky areas of benzene, toluene, ethyl-benzene, and xylene (BTEX contamination that were predicted through developed models. The study results demonstrated that the proposed stochastic MCDA method provides more complete information of possible rankings of alternatives than conventional methods. The decision makers cannot only obtain the ranking information under uncertainty directly, but also gain an in-depth understanding on the relative derivation and closeness among different alternatives.