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A surrogate model is introduced for identifying the optimal remediation strategy for Dense Non-Aqueous Phase Liquids(DNAPL)-contaminated aquifers.A Latin hypercube sampling(LHS)method was used to collect data in the feasible region for input variables.A surrogate model of the multi-phase flow simulation model was developed using a radial basis function artificial neural network(RBFANN).The developed model was applied to a perchloroethylene(PCE)-contaminated aquifer remediation optimization problem.The relative errors of the average PCE removal rates between the surrogate model and simulation model for 10 validation samples were lower than 5%,which is high approximation accuracy.A comparison of the surrogate-based simulation optimization model and a conventional simulation optimization model indicated that RBFANN surrogate model developed in this paper considerably reduced the computational burden of simulation optimization processes.
A surrogate model is introduced to identifying the optimal remediation strategy for Dense Non-Aqueous Phase Liquids (DNAPL) -contaminated aquifers. A Latin hypercube sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the multi-phase flow simulation model was developed using a radial basis function artificial neural network (RBFANN). developed model was applied to a perchlorethylene (PCE) -contaminated aquifer remediation optimization problem. the relative errors of the average PCE removal rates between the surrogate model and simulation model for 10 validation samples were lower than 5%, which is high approximation accuracy. A comparison of the surrogate-based simulation optimization model and a conventional simulation optimization model indicated that RBFANN surrogate model developed in this paper sufficiently reduced the computational burden of simulation optimization processes.