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 ...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 be- tween 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.展开更多
基金supported by the National Nature Science Foundation of China(No.41072171)China Geological Survey Project(No.1212011140027)
文摘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 be- tween 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.
