Browse > Article
http://dx.doi.org/10.1007/s12303-011-0018-8

Optimal management design of a pump and treat system at the industrial complex in Wonju, Korea  

Park, Yu-Chul (Department of Geophysics, College of Natural Sciences, Kangwon National University)
Jeong, Ji-Min (Department of Geophysics, College of Natural Sciences, Kangwon National University)
Eom, Sung-Il (Department of Geophysics, College of Natural Sciences, Kangwon National University)
Jeong, Ui-Pyoung (Department of Geophysics, College of Natural Sciences, Kangwon National University)
Publication Information
Geosciences Journal / v.15, no.2, 2011 , pp. 207-223 More about this Journal
Abstract
The optimization of the management design for the remediation of contaminated groundwater, using a pump and treat system, was performed for an industrial complex site in Wonju, Korea. The groundwater in the study area was contaminated with trichloroethylene (TCE) and other solvents. The pump and treat system was selected as a remediation technique, with a genetic algorithm selected as the optimization technique. The groundwater flow and contaminant transport were simulated using MODFLOW, MT3D and RT3D. Three possible scenarios were considered to obtain a cost effective remediation strategy. The cost effectiveness was determined by the total cost, including the installation cost of pumping wells and the operational cost of the pump and treat system. Scenario 1 involved the removal of TCE from the entire contaminated area using pre-existing candidate pumping wells and additional candidate wells. The results with scenario 1 showed that additional candidate wells were required to reduce the total remediation cost. Scenario 2 involved the simultaneous removals of TCE and other solvents from the entire contaminated area. The total cost of scenario 2 was 180% that of scenario 1. Scenario 3 entailed containing the TCE within a compliance line, with another remediation technique applied to the rest of the contaminated area to reduce the total remediation cost. The total cost of scenario 3 was reduced to 37% that of scenario 1 under the same cleanup time constraint. If the cost of the other remediation technique does not exceed the difference between the total costs of the other two scenarios, the optimal management design of scenario 3 would be the most cost effective remediation strategy.
Keywords
optimal design; pump and treat system; industrial complex; genetic algorithm; cost effective;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 Almasri, M.N. and Kaluarachchi, J.J., 2005, Modular neural networks to predict the nitrate distribution in ground water using the onground nitrogen loading and recharge data. Environmental Modeling and Software, 20, 851-871.   DOI   ScienceOn
2 Ahlfeld, D.P., Mulvey, J.M., Pinder, G.F., and Wood, E.F., 1988, contaminated groundwater remediation design using simulation, optimization, and sensitivity theory: 1. Model development. Water Resources Research, 24, 431-441.   DOI
3 Baek, W. and Lee, J.Y., 2010, Source apportionment of trichloroethylene in groundwater of an industrial complex in Korea: a 15- year dispute and perspective. Water and Environment Journal, DOI: 10.1111/j.1747-6593.2010.00226.x
4 Bau, D.A. and Mayer, A.S., 2006, Stochastic management of pump and treat strategies using surrogate functions. Advances in Water Resources, 29, 1901-1917.   DOI   ScienceOn
5 Chang, L.-C. and Hsiao, C.-T., 2002, Dynamic optimal ground water remediation including fixed and operation costs. Ground Water, 40, 481-490.   DOI   ScienceOn
6 Clement, T.P., 1997, RT3D - A Modular Computer Code for Simulating Reactive Multi-Species Transport in 3-Dimensional Groundwater Aquifers. Pacific Northwest National Laboratory, Richland, Washington, PNNL-11720.
7 Culver, T.B. and Shoemaker, C.A., 1992, Dynamic optimal control for groundwater remediation with flexible management periods. Water Resources Research, 28, 629-641.   DOI
8 Dougherty, D.E. and Marryott, R.A., 1991, Optimal groundwater management: 1. Simulated annealing. Water Resources Research, 27, 2497-2508.
9 Gangwon Province, 2005, Detailed Investigation and Basic Remediation Design for Contaminated Soil and Groundwater in the Woosan Industrial Complex, Wonju City, Gangwon Province, Korea.
10 Goldberg, D.E., 1989, Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Inc., Massachusetts, 432 p.
11 Haggerty, R. and Gorelick, S.M., 1994, Design of multiple contaminant remediation: Sensitivity to rate-limited mass transfer. Water Resources Research, 30, 435-446.   DOI
12 Harbaugh, A.W. and McDonald, M.G., 1996, User's documentation for MODFLOW-96, an update to the U.S. Geological Survey modular finite-difference ground-water flow model. U.S. Geological Survey Open-File Report 96-485, U.S. Geological Survey, Reston, Virginia, 55 p.
13 He, L., Huang, G.H., and Lu, H.W., 2009, A coupled simulation-optimization approach for groundwater remediation design under uncertainty: An application to a petroleum-contaminated site. Environmental Pollution, 157, 2485-2492.   DOI   ScienceOn
14 Huang, C. and Mayer, A.S., 1997, Pump-and-treat optimization using well locations and pumping rates as decision variables. Water Resources Research, 33, 1001-1012.   DOI   ScienceOn
15 Huang, G.H., 1992, Stepwise cluster analysis method for predicting air quality in an urban environment. Atmospheric Environment, 26B, 349-357.
16 Huang, Y.F., Wang, G.Q., Huang, G.H., Xiao, H.N., and Chakma, A., 2007, IPCS: an integrated process control system for enhanced in-situ bioremediation. Environmental Pollution, 151, 460-469.
17 Ko, N.Y. and Lee, K.K., 2010, Information effect on remediation design of contaminated aquifers using the pump and treat method. Stoch Environ Res Risk Assess, 24, 646-660.
18 Lee, J.Y. and Lee, K.K., 2000, Use of Hydrologic Time Series Data for Identification of Recharge Mechanism in a Fractured Bedrock Aquifer System. Journal of Hydrology, 229, 190-201.   DOI   ScienceOn
19 Lu, H.W., Huang, G.H., and Zeng, G.M., 2008, An inexact two-stage fuzzy-stochastic programming model for water resources management. Water Resources Management, 22, 991-1016.   DOI   ScienceOn
20 Mackay, D.M. and Cherry, J.A., 1989, Groundwater contamination: Limitations of pump-and-treat remediation. Environmental Science and Technology, 23, 630-636.   DOI   ScienceOn
21 McKinney, D.C. and Lin, M.-D., 1994, Genetic algorithms solution of groundwater management models. Water Resources Research, 30, 1897-1906.   DOI   ScienceOn
22 Rogers, L.L. and Dowla, F.U., 1994, Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research, 30, 457-481.   DOI
23 United States Environmental Protection Agency (USEPA), 2001, A citizen's guide to pump and treat, Office of Solid Waste and Emergency Response, Washington, DC.
24 United States Environmental Protection Agency (USEPA), 2007, A cost comparison framework for use in optimizing ground water pump and treat systems, Office of Solid Waste and Emergency Response, Washington, DC
25 United States Environmental Protection Agency (USEPA), 2010, National primary drinking water regulations, Code of Federal Regulations, Washington, DC.
26 Wang, M. and Zheng, C., 1997, Optimal remediation policy selection under general condition. Ground Water, 35, 757-764.   DOI   ScienceOn
27 Yu, S.-Y., Chae, G.-T., Jeon, K.-H., Jeong, J.-S., and Park, J.-G., 2006, Trichloroethylene Contamination in Fractured Bedrock Aquifer in Wonju, South Korea. Bulletin of Environmental Contamination and Toxicology, 76, 341-348.   DOI   ScienceOn
28 Zheng, C. and Wang, P.P., 1998, MT3DMS: A modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems. Report to the U.S. Army Corps of Engineers, Vicksburg, Mississippi, 221 p.
29 Zheng, C. and Wang, P.P., 2002, A field demonstration of the simulation optimization approach for remediation system design. Ground Water, 40, 258-265.   DOI   ScienceOn