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, Kriging, and support vector regression. Genetic algorithm, self-adaptive particle swarm optimization (PSO), and self-adaptive PSO based on simulated annealing (SA) were applied to optimize the parameters of the surrogate model. Results showed that the optimal surrogate model was Kriging model with the parameters obtained by self-adaptive PSO based on SA. Relative errors of the contaminant removal rates between the optimal surrogate model and simulation model for 100 validation samples were lower than 3.5%, clearly confirming the optimal performance of the proposed model. Finally, computation of run time enabled us to conclude that the surrogate model presented in this article was capable of considerably reducing computational burden of simulation optimization processes.