Upscaling of reactive transport modeling for surfactant-enhanced aquifer remediation
CHEN Meng-jia1, WU Jian-feng1, SONG Jian1, SUN Xiao-min2, LIN Jin2, WU Ji-chun1
1. Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China;
2. Nanjing Hydraulic Research Institute, Nanjing 210029, China
In order to find an appropriate permeability upscaling method which can greatly improve the computational efficiency of surfactant-enhanced aquifer remediation (SEAR) model while ensuring the accuracy of the simulation, two coarse-scale models based on Laplacian with skin method and the arithmetic mean upscaling method were applied in this study, and their performance was also compared with fine-scale models. The results indicated that the maximum calculation error of the residual mass of aquifer pollutants by the model based on Laplacian with skin was better than the model built by the arithmetic mean upscaling method in all cases. The superiority of Laplacian with skin became more significant when the aquifer heterogeneity was stronger. In addition, Laplacian with skin achieved a better simulation effect on the centroid location and the shape of the pollutant. Particularly, the use of coarse-scale model can greatly reduce the computational cost of SEAR, leading to the significant computational cost-savings, e.g., about 6.5% of the original runtime by using the arithmetic mean upscaling method and 4.5% by Laplacian with skin.
Delshad M, Pope G A, Sepehrnoori K. A compositional simulator for modeling surfactant enhanced aquifer remediation, 1formulation[J]. Journal of Contaminant Hydrology, 1996,23(4):303-327.
[2]
Schaerlaekens J, Mertens J, Linden J V, et al. A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations[J]. Journal of Contaminant Hydrology, 2006,86(3/4):176-194.
[3]
Wu M, Cheng Z, Wu J F, et al. Precise simulation of long-term DNAPL migration in heterogeneous porous media based on light transmission micro-tomography[J]. Journal of Environmental Chemical Engineering, 2017,5(1):725-734.
[4]
李璐璐,赵勇胜,王贺飞,等.溶剂萃取分离地下强化抽出处理液中的污染物和表面活性剂[J]. 中国环境科学, 2014,34(4):912-916. Li L L, Zhao Y S, Wang H F, et al. Solvent extraction separation of surfactant and pollutants during SEAR[J]. China Environmental Science, 2014,34(4):912-916.
[5]
宋健,吴剑锋,杨蕴,等.基于含水层DNAPL污染修复替代模型的多目标优化研究[J]. 中国环境科学, 2016,36(11):3390-3396. Song J, Wu J F, Yang Y, et al. A Kriging-based surrogate model for multi-objective optimization of DNAPL-contaminated aquifer remediation[J]. China Environmental Science, 2016,36(11):3390-3396.
[6]
Haley J L, Hanson B, Enfield C, et al. Evaluating the effectiveness of groundwater extraction systems[J]. Groundwater Monitoring & Remediation, 2010,11(1):19-124.
[7]
Jiang X, Lu W X, Hou Z Y, et al. Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites[J]. Computers & Geosciences, 2015,84(2015):37-45.
[8]
Pennell K D, Jin M, Abriola L M, et al. Surfactant enhanced remediation of soil columns contaminated by residual tetrachloroethylene[J]. Journal of Contaminant Hydrology, 1994,16(1):35-53.
[9]
程洲,徐红霞,吴吉春,等.地下水中Tween80对DNAPL运移和分布的影响[J]. 中国环境科学, 2019,39(3):1068-1077. Cheng Z, Xu H X, Wu J C, et al. Effects of Tween 80 in groundwater on DNAPL migration and distribution[J]. China Environmental Science, 2019,39(3):1068-1077.
[10]
Austin U O T. Volume Ⅱ:Technical documentation for UTCHEM-9.0 a three-dimensional chemical flood simulator[M]. University of Texas, USA, 2000:1-1.
[11]
Qi Q Y, Lu W, Miao T, et al. Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites[J]. Journal of Contaminant Hydrology, 2017,207:31-38.
[12]
Luo J N, Lu W X. Sobol' sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates[J]. Computers & Geosciences, 2014,67(2014):110-116.
[13]
Ouyang Q, Lu W, Hou Z, et al. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method[J]. Journal of Contaminant Hydrology, 2017,200(2017):15-23.
[14]
Qin X S, Huang G H, Chakma A, et al. Simulation-based process optimization for surfactant-enhanced aquifer remediation at heterogeneous DNAPL-contaminated sites[J]. Science of the Total Environment, 2007,381(1-3):17-37.
[15]
Luo J N, Lu W X. Comparison of surrogate models with different methods in groundwater remediation process[J]. Journal of Earth System Science, 2014,123(7):1579-1589.
[16]
Farmer C L. Upscaling:a review[J]. International Journal for Numerical Methods in Fluids, 2002,40(1/2):63-78.
[17]
Wen X H, Gómez-Hernández J J. Upscaling hydraulic conductivities in heterogeneous media:An overview[J]. Journal of Hydrology, 1996,183(1/2):ix-xxxii.
[18]
Mantoglou A, Gelhar L W. Effective hydraulic conductivities of transient unsaturated flow in stratified soils[J]. Water Resources Research, 1987,23(1):57-67.
[19]
Russo D. Upscaling of hydraulic conductivity in partially saturated heterogeneous porous formation[J]. Water Resources Research, 1992,28(28):397-409.
[20]
Haldorsen H H, Lake L W. A new approach to shale management in field scale simulation models[J]. Society of Petroleum Engineers, 1984,24(4):447-457.
[21]
Sanchez-Vila X, Carrera J, Girardi J P. Scale effects in transmissivity[J]. Journal of Hydrology, 1996,183(1):1-22.
[22]
Gómez-Hernández J J. A stochastic approach to the simulation of block conductivity values conditioned upon data measured at a smaller scale[D]. California:Stanford University, 1991.
[23]
Rubin Y, Gómez-Hernández J J. A stochastic approach to the problem of upscaling of conductivity in disordered media:Theory and unconditional numerical simulation[J]. Water Resources Research, 1990,26(4):691-701.
[24]
Fernandez-Garcia D, Gómez-Hernández J J. Impact of upscaling on solute transport:Traveltimes, scale dependence of dispersivity, and propagation of uncertainty[J]. Water Resources Research, 2007,43(2):W02423.
[25]
陈梦佳,吴剑锋,孙晓敏,等.地下水典型非水相液体污染运移模拟的尺度提升研究[J]. 水文地质工程地质, 2019. Chen M J, Wu J F, Sun X M, et al. Upscaling of PCE transport modeling based on UTCHEM in heterogeneous porous media. Hydrogeology & Engineering Geology, 2019.
[26]
Press W H, Flannery B P, Teukolsky S A, et al. Numerical recipes in C[M]. Cambridge:Cambridge University Press, 1988:735.
[27]
Li L P, Zhou H Y, Gómez-HernándezJ J. Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media[J]. Advances in Water Resources, 2011,34(4):478-489.
[28]
Mulligan C N, Yong R N, Gibbs B F. Surfactant-enhanced remediation of contaminated soil:areview[J]. Engineering Geology, 2001,60(1-4):371-380.
[29]
白静.表面活性剂强化地下水循环井技术修复NAPL污染含水层研究[D]. 长春:吉林大学, 2013. Bai J. Remediation of NAPL contaminated aquifer with surfactant-enhanced groundwater circulation well[D]. Changchun Jilin University, 2013.
[30]
West M R, Kueper B H. Numerical simulation of DNAPL source zone remediation with in situ chemical oxidation (ISCO)[J]. Advances in Water Resources, 2012,44(2012):126-139.
[31]
Schaerlaekens J, Carmeliet J, Feyen J. Multi-objective optimization of the setup of a surfactant-enhanced DNAPL remediation[J]. Environmental Science & Technology, 2005,39(7):2327-2333.
[32]
Liu L. Modeling for surfactant-enhanced groundwaterremediation processes at DNAPLs-contaminated sites[J]. Journalof Environmental Informatics, 2005,5(2):42-52.
