Optimization of Surfactant-Enhanced Aquifer Remediation for a Laboratory BTEX System under Parameter Uncertainty

This study develops a nonlinear chance-constrained programming (NCCP) model for optimizing surfactant-enhanced aquifer remediation (SEAR) processes. The model can not only address the parameter uncertainty, but provide a reliability level for the identified optimal remediation strategy. To solve the NCCP model, stepwise cluster analysis (SCA) is used to create a set of proxy simulators for quantifying the relationships between operating conditions (i.e., pumping rate) and probabilities of benzene levels in violation of standard. Compared to conventional parametric inference techniques, SCA is independent of prior assumptions for model forms (e.g., linear or exponential ones) and capable of reflecting complex nonlinear relationships between operating conditions and probabilities. To alleviate the computational efforts in the optimization process, the generated proxy simulators are repeatedly called by simulated annealing (SA) to test the feasibility of each potential solution. The implicit of the optimal NCCP solutions is discussed through a laboratory-scale SEAR system where porosity and intrinsic permeability are treated as stochastic parameters. It is observed that well locations, environmental standards, reliability levels and remediation durations would have significant effects on optimal SEAR strategies. By comparing the predicted benzene concentration without and with remediation actions, it is indicated that the optimal SEAR process can guarantee the benzene concentration to meet the environmental standard with a high reliability level.