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

Jiannan Luo; Wenxi Lu; Xin Xin; Haibo Chu

doi:10.1007/s12583-013-0395-1

Volume 24 Issue 6

Dec 2013

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Journal of Earth Science > 2013 > 24(6): 1023-1032.

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

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

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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

Jiannan Luo^{1, 2},
Wenxi Lu^{1, 2
,
,},
Xin Xin^{1, 2},
Haibo Chu^{1, 2}

1.
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China
2.
College of Environment and Resources, Jilin University, Changchun 130021, China

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

Abstract

Abstract

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.
- DNAPL,
- Latin hypercube sampling,
- radial basis function artificial neural network,
- simulation optimization,
- surrogate model

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References(42)

References

Abriola, L. M., 1989. Modeling Multiphase Migration of Organic Chemicals in Groundwater Systems—A Review and Assessment. Environ. Health Perspect. , 83: 117–143, doi: 10.1289/ehp.8983117

Ahlfeld, D. P., Mulvey, J. M., Pinder, G. F., 1988. Contaminated Groundwater Remediation Design Using Simulation, Optimization, and Sensitivity Theory: 2. Analysis of a Field Site. Water Resour. Res. , 24(3): 443–452, doi: 10.1029/WR024i003p00443

Baddari, K., Aïfa, T., Djarfour, N., et al., 2009. Application of a Radial Basis Function Artificial Neural Network to Seismic Data Inversion. Computat. Geosci. , 35(12): 2338–2344, doi: 10.1016/j.cageo.2009.03.006

Bear, J., 2007. Hydraulics of Groundwater. Dover Publications, New York. 67 http://webpac.lib.tongji.edu.cn/opac/item.php?marc_no=0002527605

Carnicer, J. M., 2008. Interpolation and Reconstruction of Curves and Surfaces. Rev. Real Academia de Ciencias. Zaragoza. , 63: 7–40 http://www.unizar.es/acz/05Publicaciones/Revistas/Revista63/p007.pdf

Chatterjee, K., Fang, K. T., Qin, H., 2006. A Lower Bound for the Centered L 2-Discrepancy on Asymmetric Factorials and Its Application. Metrika, 63(2): 243–255, doi: 10.1007/s00184-005-0015-x

Chen, S., Cowan, C. F. N., Grant, P. M., 1991. Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. Proceedings of IEEE Transactions on Neural Networks, 2: 302–309, doi: 10.1109/72.80341

Ciocoiu, I. B., 2002. RBF Networks Training Using a Dual Extended Kalman Filter. Neurocomputing, 48(1–4): 609–622, doi: 10.1016/S0925-2312(01)00631-2

Delshad, M., Pope, G. A., Sepehrnoori, K., 1996. A Compositional Simulator for Modeling Surfactant Enhanced Aquifer Remediation, 1 Formulation. J. Contam. Hydrol. , 23(4): 303–327, doi: 10.1016/0169-7722(95)00106-9

Fen, C. S., Chan, C., Cheng, H. C., 2009. Assessing a Response Surface-Based Optimization Approach for Soil Vapor Extraction System Design. Journal of Water Resources Planning and Management, 135(3): 198–207, doi: 10.1061/(ASCE)0733-9496(2009)135:3(198)

Fernandez-Garcia, D., Bolster, D., Sanchez-Vila, X., et al., 2012. A Bayesian Approach to Integrate Temporal Data into Probabilistic Risk Analysis of Monitored NAPL Remediation. Advances in Water Resources, 36: 108–120, doi: 10.1016/j.advwatres.2011.07.001

Fetter, C. W., 1999. Contaminant Hydrogeology. Macmillan Publishing Company, New York. 208–262

Guan, J., Aral, M., 1999. Optimal Remediation with Well Locations and Pumping Rates Selected as Continuous Decision Variables. J. Hydrol. , 221(1–2): 20–42, doi: 10.1016/S0022-1694(99)00079-7

He, L., Huang, G. H., Zeng, G. M., et al., 2008. An Integrated Simulation, Inference, and Optimization Method for Identifying Groundwater Remediation Strategies at Petroleum-Contaminated Aquifers in Western Canada. Water Res. , 42(10–11): 2629–2639, doi: 10.1016/j.watres.2008.01.012

Helton, J. C., Davis, F. J., 2003. Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems. Reliab. Eng. Syst. Saf. , 81(1): 23–69, doi: 10.1016/S0951-8320(03)00058-9

Helton, J. C., Davis, F. J., Johnson, J. D., 2005. A Comparison of Uncertainty and Sensitivity Analysis Results Obtained with Random and Latin Hypercube Sampling. Reliab. Eng. Syst. Saf. , 89(3): 305–330, doi: 10.1016/j.ress.2004.09.006

Hora, S. C., Helton, J. C., 2003. A Distribution-Free Test for the Relationship between Model Input and Output when Using Latin Hypercube Sampling. Reliab. Eng. Syst. Saf. , 79(3): 333–339, doi: 10.1016/S0951-8320(02)00240-5

Huang, Y., Li, J., Huang, G., et al., 2003. Integrated Simulation-Optimization Approach for Real-Time Dynamic Modeling and Process Control of Surfactant-Enhanced Remediation at Petroleum-Contaminated Sites. Pract. Period Hazard Toxic Radioact. Waste Manag. (ASCE), 7(2): 95–105, doi: 10.1061/(ASCE)1090-025X(2003)7:2(95)

Johnson, V. M., Rogers, L. L., 2000. Accuracy of Neural Network Approximators in Simulation-Optimization. Journal of Water Resources Planning and Management, 126(2): 48–65, doi: 10.1061/(ASCE)0733-9496(2000)126:2(48)

Kegl, B., Krzyak, A., Niemann, H., 2000. Radial Basis Function Networks and Complexity Regularization in Function Learning and Classification. In: Proceedings of the 5th International Conference on Pattern Recognition. IEEE, 2: 81–86, doi: 10.1109/ICPR.2000.906022

Kuiper, L. K., Illangasekare, T. K., 1998. Numerical Simulation of NAPL Flow in the Subsurface. Computat. Geosci. , 2(3): 171–189 doi: 10.1023/A:1011550219518

Liu, L., 2005. Modeling for Surfactant-Enhanced Groundwater Remediation Processes at DNAPLs-Contaminated Sites. J. Environ. Inform. , 5(2): 42–52, doi: 10.3808/jei.200500045

Liu, W. H., Medina M. A. Jr., Thomann, W., et al., 2000. Optimization of Intermittent Pumping Schedules for Aquifer Remediation Using a Genetic Algorithm 1. J. Am. Leather Chem. As. , 36(6): 1335–1348, doi: 10.1111/j.1752-1688.2000.tb05730.x

McKay, M. D., Beckman, R. J., Conover, W., 1979. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2): 239–245, doi: 10.2307/1268522

Moradkhani, H., Hsu, K., Gupta, H. V., et al., 2004. Improved Streamflow Forecasting Using Self-Organizing Radial Basis Function Artificial Neural Networks. J. Hydrol. , 295(1–4): 246–262, doi: 10.1016/j.jhydrol.2004.03.027

NRC, 1994. Alternatives for Groundwater Clean up. National Academy Press, Washington DC. 1–316 http://agris.fao.org/openagris/search.do?recordID=US9519460

Olsson, A., Sandberg, G., Dahlblom, O., 2003. On Latin Hypercube Sampling for Structural Reliability Analysis. Struct. Saf. , 25(1): 47–68, doi: 10.1016/S0167-4730(02)00039-5

Pan, W., 2003. The Research and Application of the Online Algorithms: [Dissertation]. Jilin University, Changchun. 1–61 (in Chinese with English Abstract)

Pennell, K. D., Jin, M., Abriola, L. M., et al., 1994. Surfactant Enhanced Remediation of Soil Columns Contaminated by Residual Tetrachloroethylene. J. Contam. Hydrol. , 16(1): 35–53, doi: 10.1016/0169-7722(94)90071-X

Petelet, M., Iooss, B., Asserin, O., et al., 2010. Latin Hypercube Sampling with Inequality Constraints. Asta. Adv. Stat. Anal. , 94(4): 325–339, doi: 10.1007/s10182-010-0144-z

Powell, M. J. D., 1987. Radial Basis Functions for Multivariable Interpolation: A Review. Algorithms for Approximation, 143–167 http://www.ams.org/mathscinet-getitem?mr=911311

Qin, X. S., Huang, G. H., Chakma, A., et al., 2007. Simulation-Based Process Optimization for Surfactant-Enhanced Aquifer Remediation at Heterogeneous DNAPL-Contaminated Sites. Sci. Total Environ. , 381(1–3): 17–37, doi: 10.1109/ICIII.2009.597

Rathfelder, K. M., Abriola, L. M., Taylor, T. P., et al., 2001. Surfactant Enhanced Recovery of Tetrachloroethylene from a Porous Medium Containing Low Permeability Lenses. 2. Numerical Simulation. J. Contam. Hydrol. , 48(3–4): 351–374, doi: 10.1016/S0169-7722(00)00186-8

Rogers, L. L., Dowla, F. U., Johnson, V. M., 1995. Optimal Field-Scale Groundwater Remediation Using Neural Networks and the Genetic Algorithm. Environ. Sci. Techno. , 29(5): 1145–1155, doi: 10.1021/es00005a003

Schaerlaekens, J., Mertens, J., Van Linden, J., et al., 2006. A Multi-Objective Optimization Framework for Surfactant-Enhanced Remediation of DNAPL Contaminations. J. Contam. Hydrol. , 86(3–4): 176–194, doi: 10.1016/j.jconhyd.2006.03.002

Schumaker, M. F., Kramer, D. M., 2011. Comparison of Monte Carlo Simulations of Cytochrome B6f with Experiment Using Latin Hypercube Sampling. Bull. Math. Biol. , 73(9): 2152–2174, doi: 10.1007/s11538-010-9616-2

Shen, W., Guo, X., Wu, C., et al., 2010. Forecasting Stock Indices Using Radial Basis Function Neural Networks Optimized by Artificial Fish Swarm Algorithm. Knowl-Based Syst. , 3(24): 378–385, doi: 10.1016/j.knosys.2010.11.001

Sreekanth, J., Datta, B., 2010. Multi-Objective Management of Saltwater Intrusion in Coastal Aquifers Using Genetic Programming and Modular Neural Network Based Surrogate Models. Journal of Hydrology, 393(3–4): 245–256, doi: 10.1016/j.jhydrol.2010.08.023

Van Camp, M., Walraevens, K., 2009. Pumping Test Interpretation by Combination of Latin Hypercube Parameter Sampling and Analytical Models. Computat. Geosci. , 35(10): 2065–2073, doi: 10.1016/j.cageo.2008.12.011

Yan, S., Minsker, B., 2006. Optimal Groundwater Remediation Design Using an Adaptive Neural Network Genetic Algorithm. Water Resour. Res. , 42(5): 1145–1155, doi: 10.1029/2005WR004303

Yan, S., Minsker, B., 2011. Applying Dynamic Surrogate Models in Noisy Genetic Algorithms to Optimize Groundwater Remediation Designs. Journal of Water Resources Planning and Management, 137: 284–292, doi: 10.1061/(ASCE)WR.1943-5452.0000106

Zhong, L. R., Mayer, A. S., Pope, G. A., 2003. The Effects of Surfactant Formulation on Nonequilibrium NAPL Solubilization. J. Contam. Hydrol. , 60(1–2): 55–75, doi: 10.1016/S0169-7722(02)00063-3

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

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