A Kriging-based surrogate model for multi-objective optimization of DNAPL-contaminated aquifer remediation
SONG Jian1, WU Jian-feng1, YANG Yun2, ZHU Xiao-bin1, WU Ji-chun1
1. Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China;
2. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
A combined simulation-optimization model that integrates a new hybrid multi-objective genetic algorithm (Nondominated sorting genetic algorithm II-Hill climber with step, NSGAII-HCS) with a kriging surrogate model was developed for identifying the optimal designs of surfactant-enhanced aquifer remediation (SEAR) at a saturated heterogeneous aquifer site contaminated by Tetrachloroethylene (PCE). In the combined model, a three-dimensional multiphase and multicomponent compositional finite difference simulator (UTCHEM) was utilized to simulate the process of SEAR. The fitting mean relative error of removal efficiency output from the kriging-based surrogate model and the SEAR simulation model was only 0.80%, and the correlation coefficient was up to 0.9992, indicating that the surrogate model can convincingly replace the SEAR simulation model. Furthermore, the comparisons of Pareto optimal solutions based on the surrogate model and the SEAR simulation model indicated that the mean relative error of the optimal solutions and their correlation coefficient were 0.70% and 0.9998, respectively. The regression analysis results demonstrated that the proposed kriging-based surrogate models is able to predict the evolution of SEAR and the simulation-optimization tool based on the surrogate model is of lower variability and higher reliability.
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