Selecting Parameter-Optimized Surrogate Models in DNAPL-Contaminated Aquifer Remediation Strategies

Abstract

Abstract Surfactant-enhanced aquifer remediation (SEAR) is an efficient way for clearing dense nonaqueous phase liquids (DNAPLs) which may result in serious environment and health threats. To limit the high cost of SEAR, simulation optimization techniques are generally applied to ensure that an optimal remediation strategy is achieved. Furthermore, surrogate model techniques have been widely used to reduce the high computational burden associated with these processes. However, previous research rarely involved comparison of different surrogate models for multiphase flow numerical simulation models. In this regard, we conducted a comparative analysis to select the optimal modeling technique and parameter optimization algorithm for surrogate models in DNAPL-contaminated aquifer remediation strategy optimization problems. Latin hypercube sampling method was used to collect data in the feasible region of input variables. Surrogate models were developed using radial basis function artificial neural network, Kr...