Surrogate Model Application to the Identification of an Optimal Surfactant-Enhanced Aquifer Remediation Strategy for DNAPL-Contaminated Sites

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.