Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Abstract

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods—support vector regression (SVR) and Kriging (KRG)—were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.