Stochastic Nonlinear Programming Based on Uncertainty Analysis for DNAPL-Contaminated Aquifer Remediation Strategy Optimization

Abstract

Surrogate-based simulation-optimization techniques are widely applied in developing optimal remediation strategies that increase the efficiency and reduce the cost of surfactant-enhanced aquifer remediation (SEAR) when clearing dense nonaqueous phase liquids (DNAPLs). In such processes, there are many uncertainty factors that may greatly affect the optimization outcome of a SEAR strategy selection. However, previous research on this subject rarely incorporates an uncertainty analysis. This paper presents an uncertainty analysis of both the simulation model and the ensemble surrogate model used to optimize SEAR strategies. Set pair analysis (SPA) and kriging methods were used to build the ensemble surrogate model. The probability distributions of the residuals of the outputs between the ensemble surrogate model and the simulation model, run on 100 testing samples, were analyzed to ascertain the uncertainty of the surrogate model, which was found to be less than 1.5%. The uncertainty of the simulation model was derived by combining a Monte Carlo random simulation with the Sobol’ global sensitivity analysis. Finally, a stochastic nonlinear programming model was established to compute the optimal remediation strategies for the remediation target under different confidence levels. This research will allow decision makers to more confidently select the optimal remediation strategy for a given scenario by balancing the reliability of model prediction with the cost of the remediation strategy according to the demands of the project.