An Improved Multi-objective Genetic Algorithm For Optimal Design Of Groundwater Remediation

Multi-objective genetic algorithm have been widely combined with simulation models for optimal design of groundwater remediation system.However,GA-based multi-objective evolutionary algorithm may not guarantee find the true Pareto optimal set for addressing real world problems.This article presents an algorithm that uses a novel iterative local search(Hill Climber with Step,HCS)in the non-dominated sorting genetic algorithm II(NSGAII)as a hybrid multi-objective algorithm for improving convergence to the true Pareto front.The directional local search increase the probability of convergence to the global Pareto-optimal front.Comparing numerical results on two benchmark problems with solutions obtained by NSGAII,the proposed NSGAII-HCS is able to find much better spread of solutions and better convergence to the true Pareto optimal front Then the proposed NSGAII-HCS is coupled with the commonly used flow and transport code,MODFLOW and MT3DMS,and applied to the synthetic and field pump-and-treat(PAT)groundwater remediation systems.Comparing with the existing NSGAII,the proposed NSGAII-HCS can find Pareto optimal solutions with lower variability and higher reliability and is a promising tool for optimizing the multi-objective design of groundwater remediation systems.Due to the special physical and chemical characteristics of DNAPLs(low aqueous solubility and high interfacial tensions with water),these contaminants are very inefficient to remediate with classic in-situ remediation techniques such as PAT.Surfactant enhanced aquiferremediation(SEAR)is one of the most promising techniques to increase the effectiveness of removing DNAPLs in the aquifer.However,the embedded simulation optimization model requires the simulation model to running repeatedly for ensuring the resolution of state variables.This high computational burden limits the applicability of the multi-objective genetic algorithm for optimal design of SEAR to sites contaminated with DNAPLs.This article presents a combined simulation-optimization model that integrates NSGAII-HCS with a kriging surrogate model which was developed for identifying the optimal designs of 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 model 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.Finally,the proposed MOEA should be tested for optimizing multi-objective design of groundwater remediation under complicated hydrogeological conditions.The kriging-based surrogate model which integrated with advanced MOEA has the potential to speed up multi-objective optimal design of complex model and provided a new approach to solving groundwater quality management model.