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Volume 24 Issue 6
Dec 2013
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Article Contents
Jiannan Luo, Wenxi Lu, Xin Xin, Haibo Chu. Surrogate Model Application to the Identification of an Optimal Surfactant-Enhanced Aquifer Remediation Strategy for DNAPL-Contaminated Sites. Journal of Earth Science, 2013, 24(6): 1023-1032. doi: 10.1007/s12583-013-0395-1
Citation: Jiannan Luo, Wenxi Lu, Xin Xin, Haibo Chu. Surrogate Model Application to the Identification of an Optimal Surfactant-Enhanced Aquifer Remediation Strategy for DNAPL-Contaminated Sites. Journal of Earth Science, 2013, 24(6): 1023-1032. doi: 10.1007/s12583-013-0395-1

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

doi: 10.1007/s12583-013-0395-1
Funds:

the National Nature Science Foundation of China 41072171

China Geological Survey Project 1212011140027

More Information
  • Corresponding author: Wenxi Lu, luwenxi@jlu.edu.cn
  • Received Date: 01 Apr 2013
  • Accepted Date: 02 Sep 2013
  • Publish Date: 01 Dec 2013
  • 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.

     

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