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2015 | 24 | 2 |

Tytuł artykułu

A simulation-based nonlinear goal programming model for goundwater remediation systems design

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
This study proposes an integrated method that simulates and optimizes groundwater design and management in combination with goal programming, which establishes the equilibrium between technical and environmental constraints in a pump-and-treat system. This method is applied to a petroleum-contaminated site in Western Canada to identify optimal remediation strategies given a set of remediation scenarios. The significant influential factors are remediation duration, standard concentration levels, and total pumping rate. Results indicate that goal programming can greatly enhance the remediation effect under low contaminant concentrations. In the pump-and-treat system, wells I2, E1, and E3 are the dominant components, whereas wells M7 and M5 are sensitive to variations in the identified influential factors. These wells must therefore be monitored intentionally. Moreover, these factors influence one another in interaction. Thus, high total pumping rates do not always generate favorable outcomes, and a long remediation period is unnecessary. In conclusion, the three identified factors should be spontaneously considered in the general goal-programming framework.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

24

Numer

2

Opis fizyczny

p.563-574,fig.,ref.

Twórcy

autor
  • Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China
autor
  • Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China
  • College of Renewable Energy, North China Electric Power University, Beijing 102206, China
autor
  • Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China
  • College of Renewable Energy, North China Electric Power University, Beijing 102206, China

Bibliografia

  • 1. COMPERNOLLE T., VAN PASSEL S., LEBBE L. The value of groundwater modeling to support a pump and treat design. Ground Water Monit. R. 33, 111, 2013.
  • 2. KO N.Y., LEE K.K. Design of effective remediation system in a contaminated aquifer by controlling constraints. Geosci. J. 13, 415, 2009.
  • 3. KO N.Y., LEE K.K. Information effect on remediation design of contaminated aquifers using the pump and treat method. Stoch. Env. Res. Risk A. 24, 649, 2009.
  • 4. MOUSSAVI G., KHOSRAVI R., FARZADKIA M. Removal of petroleum hydrocarbons from contaminated groundwater using an electrocoagulation process: Batch and continuous experiments. Desalination 278, 288, 2011.
  • 5. GULER C., KAPLAN V., AKBULUT C. Spatial distribution patterns and temporal trends od heavy-metal concentrations in a petroleum hydrocarbon-contaminated site: Karaduvar coastal aquifer (Mersin, SE Turkey). Environ. Earth S. 70, 943, 2013.
  • 6. PARK Y.C., JEONG J.M., EOM S.I., JEONG U.P. Optimal management design of a pump and treat system at the industrial complex in Wonju, Korea. Geosci. J. 15, 207, 2011.
  • 7. MONDAL A., ELDHO T.I., RAO V.V.S.G. Multiobjective groundwater remediation system design using coupled finite-element model and nondominated sorting genetic algorithm II. J. Hydrol. Eng. 15, 350, 2010.
  • 8. CHANG L.C., CHU H.J., HSIAO C.T. Optimal planning of a dynamic pump-treat-inject groundwater remediation system. J. Hydrol. 342, 295, 2007.
  • 9. MATOTT L.S., RABIDEAU A.J., CRAIG J.R. Pump-and-treat optimization using analytic element method flow models. Adv. Water Resour. 29, 760, 2006.
  • 10. BAU D.A., MAYER A.S. Data-worth analysis for multiobjective optimal design of pump-and-treat remediation systems. Adv. Water Resour. 30, 1815, 2007.
  • 11. HE L., HUANG G.H., LU H.W., ZENG G.M. Optimization of surfactant-enhanced aquifer remediation for a laboratory BTEX system under parameter uncertainty. Environ. Sci. Technol. 42, 2009, 2008.
  • 12. YANG Q., HE L., LU H.W. A multiobjective optimisation model for groundwater remediation design at petroleum contaminated sites. Water Resour. Manag. 27, 2411, 2013.
  • 13. CHENINI I., KHEMIRI S. Evaluation of groundwater quality using multiple linear regression and structural equation modeling. Int. J. Environ. S. Tech. 6, 509, 2009.
  • 14. HANRAHAN G., GARZA C., GARCIA E., MILLER K. Experimental design and response surface modeling: a method development application for the determination of reduced inorganic species in environmental samples. J. Environ. Inform. 9, 71, 2007.
  • 15. HE L., HUANG G.H., LU H.W. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development. J. Hazard. Mater. 176, 521, 2010.
  • 16. YAN S.Y., MINSKER B. Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour. Res. 42, 1, 2006.
  • 17. BAU D.A., MAYER A.S. Stochastic management of pump-and-treat strategies using surrogate functions. Adv. Water Resour. 29, 1901, 2006.
  • 18. HE L., HUANG G.H., ZENG G.M., LU H.W. An integrated simulation, inference, and optimization method for identifying groundwater remediation strategies at petroleumcontaminated aquifers in western Canada. Water Res. 42, 2629, 2008.
  • 19. FAN X., HE L., LU H.W., LI J. Environmental- and health-risk-induced remediation design for benzene-contaminated groundwater under parameter uncertainty: A case study in Western Canada. Chemosphere 111, 604, 2014.
  • 20. BRAVO M., GONZALEZ I. Applying stochastic goal programming: A case study on water use planning. Eur. J. Oper. Res. 196, 1123, 2009.
  • 21. NIDUMOLU U.B., KEULEN H.V., LUBBERS M., MAPFUMO A. Combining interactive multiple goal linear programming with an inter-stakeholder communication matrix to generate land use options. Environ. Model. Softw. 22, 73, 2007.
  • 22. AL-REFAIE A., DIABAT A. Optimizing convexity defect in a tile industry using fuzzy goal programming. Measurement 46, 2807, 2013.
  • 23. DELICE E.K., GUNGOR Z. Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system. Int. J. Prod. Res. 51, 6378, 2013.
  • 24. BEN-AWUAH E., ASKARI-NASAB H., AWUAH-OFFEI K. Production scheduling and waster disposal planning for oil sands mining using goal programming. J. Environ. Inform. 20, 20, 2012.
  • 25. TENNEY C.M., LASTOSKIE C.M., DYBAS M.J. A reactor model for pulsed pumping groundwater remediation. Water Res 38, 3869, 2004.
  • 26. MINSKER B.S., SHOEMAKER C.A. Dynamic optimal control of in-situ bioremediation of ground water. J. Water Res.Pl. ASCE 124, 149, 1998.
  • 27. RAO G.T., RAO V.V.S.G., SARMA,V.S. DHAKATE R., SURINAIDU L., MAHESH J., RAMESH G. Hydrogeochemical parameters for assessment of groundwater quality in a river sub-basin. Int. J. Environ. S. Tech. 9, 297, 2012.
  • 28. MIRBAGHERI S.A., MONFARED S.A.H. Pestcide transport and transformation modeling in soil column and groundwater contamination prediction. Int. J. Environ. S. Tech. 6, 233, 2009.
  • 29. KHAMFOROUSH M., BIJAN-MANESH M.J., HATAMI T. Application of the Haug model for process design of petroleum hydrocarbon-contaminated soil bioremediation by composting process Int. J. Environ. S. Tech. 10, 533, 2013.
  • 30. LONGE E.O., ENEKWECHI L.O. Investigation on potential groundwater impacts and influence of local hydrogeology on natural attenuation of leachate at a municipal landfill. Int. J. Environ. S. Tech. 4, 133, 2007.

Typ dokumentu

Bibliografia

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