Surrogate-based source identification of DNAPLs-contaminated groundwater
HOU Ze-yu1,2, LU Wen-xi1,2, WANG Yu1,2
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
Groundwater contamination source identification (GCSI) is critical for taking effective actions in designing remediation strategies, estimating risks, and confirming responsibility. Surrogate-based simulation-optimization technique was applied to source identification and parameter estimation of DNAPLs-contaminated aquifer in this article. The results showed that:1) kernel extreme learning machines (KELM) surrogate model approximated the simulation model accurately. It could simulate the input/output relationship of the simulation model with most of the relative errors less than 5%, and the mean relative error was only 2.98%; 2) Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and maintained high computation accuracy, the identification time was reduced to 3hours from 83days; 3) Simulated annealing-based particle swarm optimization algorithm is efficient in searching the global optimal solution of the nonlinear programming optimization model, and avoiding the optimization process trapping into local optimum.
Qin X S, Huang G H, He L. Simulation and optimization technologies for petroleum waste management and remediation process control[J]. Journal of Environmental Management, 2009,90(1):54-76.
[2]
Ayvaz M T. A linked simulation-optimization model for solving the unknown groundwater pollution source identification problems[J]. Journal of Contaminant Hydrology, 2010,117(1-4):46-59.
[3]
赵莹.基于模拟-优化方法的地下水污染源反演识别[D]. 长春:吉林大学, 2017. Zhao Y. Inversion identification of groundwater pollution source based on simulation-optimization method[D]. Changchun:Jilin University, 2017.
[4]
Sun N Z. Inverse problems in groundwater modeling[M]. Springer Netherlands, 2009.
[5]
张宇.基于随机统计方法的地下水污染源反演识别研究[D]. 长春:吉林大学, 2016. Zhang Y. Inversion recognition of groundwater pollution source based on stochastic statistical method[D]. Changchun:Jilin University, 2016.
[6]
Mahinthakumar G K, Sayeed M. Hybrid genetic algorithm-Local search methods for solving groundwater source identification inverse problems[J]. Journal of Water Resources Planning and Management, 2005,131(1):45-57.
[7]
Liu X, Cardiff M A, Kitanidis P K. Parameter estimation in nonlinear environmental problems[J]. Stochastic Environmental Research and Risk Assessment, 2010,24(7):1003-1022.
[8]
Wang H, Jin X. Characterization of groundwater contaminant source using Bayesian method[J]. Stochastic Environmental Research and Risk Assessment, 2013,27(4):867-876.
[9]
Gurarslan G, Karahan H. Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm[J]. Hydrogeology Journal, 2015,23(6):1109-1119.
[10]
顾文龙.基于MCMC方法的地下水污染源反演识别[D]. 长春:吉林大学, 2017. Gu W L. Inversion identification of groundwater pollution source based on MCMC method[D]. Changchun:Jilin University, 2017.
[11]
Srivastava D, Singh R M. Breakthrough curves characterization and identification of an unknown pollution source in groundwater system using an artificial neural network (ANN)[J]. Environmental Forensics, 2014,15(2):175-189.
[12]
Zhao Y, Lu W X, Xiao C N. A Kriging surrogate model coupled in simulation-optimization approach for identifying release history of groundwater sources[J]. Journal of Contaminant Hydrology, 2016,185:51-60.
[13]
肖传宁.地下水污染源反演识别研究[D]. 长春:吉林大学, 2017. Xiao C N. Inversion identification of groundwater pollution sources[D]. Changchun:Jilin University, 2017.
[14]
李守巨,刘迎曦.基于蚁群算法的地下水污染源辨识方法[J]. 计算机科学, 2005,32(8):289-291. Li S J, Liu Y X. Groundwater pollution source identification method based on ant colony algorithm[J]. Computer Science, 2005,32(8):289-291.
[15]
江思珉,张亚力,蔡奕,等.单纯形模拟退火算法反演地下水污染源强度[J]. 同济大学学报(自然科学版), 2013,41(2):253-257. Jiang S M, Zhang Y L, Xai Y. Inversion of groundwater pollution source intensity by simplex simulated annealing algorithm[J]. Journal of Tongji University (Natural Science Edition), 2013,41(2):253-257.
[16]
Jha M, Datta B. Application of dedicated monitoring-network design for unknown pollutant-source identification based on dynamic time warping[J]. Journal of Water Resources Planning and Management, 2015,141(11):04015022.
[17]
肖传宁,卢文喜,安永凯,等.基于两种耦合方法的模拟-优化模型在地下水污染源识别中的对比[J]. 中国环境科学, 2015,35(8):2393-2399. Xiao C N, Lu W X, An Y K, et al. Comparison of simulation-optimization models based on two coupling methods in the identification of groundwater pollution sources[J]. China Environmental Science, 2015,35(8):2393-2399.
[18]
李敏强,寇纪淞,林丹,等.遗传算法的基本理论与应用[M]. 北京:科学出版社, 2002:63-65. Li M Q, Kou J S, Lin D. The basic theory and application of genetic algorithm[M]. Beijing:Science Press, 2002:63-65.
[19]
余丽.地理网络中求解TSP的并行混合算法研究[D]. 北京:中国地质大学, 2013. Yu L. Research on Parallel Hybrid Algorithm for Solving TSP in Geographical Network[D]. Beijing:China Geosciences University, 2013.
[20]
Sreekanth J, Datta B. Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models[J]. Journal of Hydrology, 2010,393(3/4):245-256.
[21]
Yao W, Chen X, Huang Y, et al. A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy[J]. Optimization Methods and Software, 2014, 29(2):406-429.
[22]
姜雪.DNAPLs污染含水层修复方案优选及不确定性分析[D]. 长春:吉林大学, 2016. Jiang X. Optimization and uncertainty analysis of DNAPLs contaminated aquifer repair scheme[D]. Changchun:Jilin University, 2016.
[23]
Manoochehri M, Kolahan F. Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process[J]. International Journal of Advanced Manufacturing Technology, 2014,73(1-4):241-249.
[24]
侯泽宇.基于替代模型的DNAPLs污染含水层修复方案优选过程的不确定性分析[D]. 长春:吉林大学, 2015. Hou Z Y. Uncertainty analysis of optimization process of DNAPLs contaminated aquifer restoration scheme based on surrogate model[D]. Changchun:Jilin University, 2015.
[25]
Delshad M, Pope G A, Sepehrnoori K. A compositional simulator for modeling surfactant enhanced aquifer remediation, 1. formulation[J]. Journal of Contaminant Hydrology, 1996,23(4):303-327.
