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

被引：0

作者：

罗建男 ^{[1
,2
]}

卢文喜 ^{[1
,2
]}

辛欣 ^{[1
,2
]}

初海波 ^{[1
,2
]}

机构：

[1] Key Laboratory of Groundwater Resources and Environment,Ministry of Education,Jilin University

[2] College of Environment and Resources,Jilin University

来源：

Journal of Earth Science | 2013年 / 06期

关键词：

DNAPL; Latin hypercube sampling; radial basis function artificial neural network; si-mulation optimization; surrogate model;

D O I：

暂无

中图分类号：

O631.3 [高聚物的化学性质];

学科分类号：

070305 ; 080501 ; 081704 ;

摘要：

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.

引用

页码：1023 / 1032

页数：10