Details

Autor(en) / Beteiligte

• Luo, Jiannan
• Lu, Wenxi
• Xin, Xin
• Chu, Haibo

Titel

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

Ist Teil von

• Journal of earth science (Wuhan, China), 2013-12, Vol.24 (6), p.1023-1032

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2013

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.