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Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI)

  • Sina SadeghfamEmail author
  • Yousef Hassanzadeh
  • Rahman Khatibi
  • Ata Allah Nadiri
  • Marjan Moazamnia
Article

Abstract

Optimum Control by Artificial Intelligence (OCAI) is presented in this paper as a dynamic decision making algorithm for optimising pumpage schedule to remediate a contaminated aquifer using the Pump, Treat and Inject (PTI) method. OCAI integrates three modules to control contaminants, to reduce runtime and to meet water quality constraints and discharge capacity at the wells. There is no bespoke capability for the strategy presented by the paper, which formulates: (i) Module 1 comprises models of physics-based flow and transport for simulating heads and contamination; (ii) Module 2 serves as the “surrogate” of Module 1 by transforming the simulation outputs of Module 1 into two fast forecasting Sugeno Fuzzy Logic (SFL) models; and (iii) Module 3 is a user-defined unit to implement OCAI, to run Genetic Algorithm (GA) and to interrogate Module 2, where Modules 2 and 1 are pre-processed. The OCAI strategy resolves two barriers: (i) the information created in the past time step is passed on to new time step for an efficient control; and (ii) the ‘hunger’ of GA for function evaluation is met by the fast Module 2 but not by the slow Module 1. The novelty in OCAI includes: an optimum PTI schedule to control contaminants and to remediate contaminated plumes. The results show that the maximum Total Dissolved Matter is reduced from a range of 3500 to 8000 to a range from 1490 to 3450. The results provide “proof-of-concept” for OCAI.

Keywords

Contaminant control Dynamic decision making Genetic algorithm Groundwater remediation Pump-treat-inject 

Notes

Acknowledgements

The authors would like to thank the East Azerbaijan Association of Environmental Protection and East Azerbaijan Regional Water Authority for their cooperation in data preparation. In particular, our gratitude is expressed to Dr. Dehganzadeh of Tabriz University of Medical Sciences and to Mr. Rahim Oglu of the East Azerbaijan Association of Environmental Protection for their kind helps and cooperation.

Compliance with Ethical Standards

We assure that the paper incorporates the following statements:

• Disclosure of potential conflicts of interest

• Research involving Human Participants and/or Animals

• Informed consent

References

  1. Becker D, Minsker B, Greenwald R, Zhang Y, Harre K, Yager K, Zheng C, Peralta R (2006) Reducing long-term remedial costs by transport modelling optimization. Ground Water 44:864–875CrossRefGoogle Scholar
  2. Chang LC, Hsiao CT (2002) Dynamic optimal ground water remediation including fixed and operation costs. Ground Water 40:481–490CrossRefGoogle Scholar
  3. Chang LC, Shoemaker CA, Liu PLF (1992) Optimal time-varying pumping rates for groundwater remediation: application of a constrained optimal control algorithm. Water Resour Res 28:3157–3173CrossRefGoogle Scholar
  4. Chang LC, Chu HJ, Hsiao CT (2007) Optimal planning of a dynamic pump-treat-inject groundwater remediation system. J Hydrol 342:295–304CrossRefGoogle Scholar
  5. Chang LC, Chu HJ, Hsiao CT (2012) Integration of optimal dynamic control and neural network for groundwater quality management. Water Resour Manag 26:1253–1269CrossRefGoogle Scholar
  6. Chen MS, Wang SW (1999) Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Sets Syst 103:239–254CrossRefGoogle Scholar
  7. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278CrossRefGoogle Scholar
  8. Culver TB, Shoemaker CA (1993) Optimal control for groundwater remediation by differential dynamic programming with Quasi-Newton approximations. Water Resour Res 29:823–831CrossRefGoogle Scholar
  9. EPA (1996) Pump-and-treat ground-water remediation: a guide for decision makers and practitioners. EPA/625/R-95/005. Office of Research and Development, Washington DCGoogle Scholar
  10. Hsiao CT, Chang LC (2002) Dynamic optimal groundwater management with inclusion of fixed costs. J Water Res 128:57–65CrossRefGoogle Scholar
  11. Hsiao CT, Chang LC (2005) Optimizing remediation of an unconfined aquifer using a hybrid algorithm. Ground Water 43:904–915CrossRefGoogle Scholar
  12. Kazemzadeh-Parsi MJ, Daneshmand F, Ahmadfard MA, Adamowski J (2015) Optimal remediation design of unconfined contaminated aquifers based on the finite element method and a modified firefly algorithm. Water Resour Manag 29:2895–2912CrossRefGoogle Scholar
  13. Liu WH, Medina MA, Thomann W, Piver WT, Jacobs TL (2000) Optimization of intermittent pumping schedules for aquifer remediation using a genetic algorithm. J Am Water Resour Assoc 36:1335–1348CrossRefGoogle Scholar
  14. Luo Q, Wu J, Yang Y, Qian J, Wu J (2014) Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty. J Hydrol 519:3305–3315CrossRefGoogle Scholar
  15. Mark L. Brusseau, (2013) Use of Historical Pump-and-Treat Data to Enhance Site Characterization and Remediation Performance Assessment. Water, Air, & Soil Pollution 224(10):1741Google Scholar
  16. Melanie M (1999) An introduction to genetic algorithms. The MIT Press, CambridgeGoogle Scholar
  17. Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Asghari Moghaddam A (2017) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706CrossRefGoogle Scholar
  18. Rao ZF, Jamieson DG (1997) The use of neural networks and genetic algorithms for design of groundwater remediation schemes. Hydrol Earth Syst Sci 1:345–356CrossRefGoogle Scholar
  19. Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modelling. Water Resour Res 30:457–481CrossRefGoogle Scholar
  20. Sadeghfam S, Hassanzadeh Y, Nadiri AA, Khatibi R (2016a) Mapping groundwater potential field using catastrophe fuzzy membership functions and Jenks optimization method: a case study of Maragheh-Bonab plain, Iran. Environ Earth Sci 75:1–19CrossRefGoogle Scholar
  21. Sadeghfam S, Hassanzadeh Y, Nadiri AA, Zarghami M (2016b) Localization of groundwater vulnerability assessment using catastrophe theory. Water Resour Manag 30:4585–4601CrossRefGoogle Scholar
  22. Sadeghfam S, Ehsanitabar A, Khatibi R, Daneshfaraz R (2018a) Investigating ‘risk’of groundwater drought occurrences by using reliability analysis. Ecological Iindicators 94:170–184.Google Scholar
  23. Sadeghfam S, Hassanzadeh Y, Khatibi R, Moazamnia M, Nadiri AA (2018b) Introducing a risk aggregation rationale for mapping risks to aquifers from point-and diffuse-sources–proof-of-concept using contamination data from industrial lagoons. Environ Impact Asses Rev 72:88–98CrossRefGoogle Scholar
  24. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer Science & Business Media, BerlinGoogle Scholar
  25. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRefGoogle Scholar
  26. Todd DK, Mays LW (2005) Groundwater hydrology, third edn. Wiley, New JerseyGoogle Scholar
  27. Whiffen GJ, Shoemaker CA (1993) Nonlinear weighted feedback control of groundwater remediation under uncertainty. Water Resour Res 29:3277–3289CrossRefGoogle Scholar
  28. WHO (2011) Guidelines for drinking-water quality, 4rd edn. World Health Organization. ISBN 978 92 4 154815 1Google Scholar
  29. Yang Q, He L, Lu HW (2013) A multiobjective optimisation model for groundwater remediation design at petroleum contaminated sites. Water Resour Manag 27:2411–2427CrossRefGoogle Scholar
  30. Zaporozec A (2002) Groundwater contamination inventory: a methodological guide. In: IHP-VI, series on groundwater, vol 2. UNESCO, Paris. 4Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Sina Sadeghfam
    • 1
    Email author
  • Yousef Hassanzadeh
    • 2
  • Rahman Khatibi
    • 3
  • Ata Allah Nadiri
    • 4
  • Marjan Moazamnia
    • 2
  1. 1.Department of Civil EngineeringFaculty of Engineering, University of MaraghehMaraghehIran
  2. 2.Faculty of Civil EngineeringUniversity of TabrizTabrizIran
  3. 3.GTEV-ReX LimitedSwindonUK
  4. 4.Department of Earth SciencesFaculty of Natural Sciences, University of TabrizTabrizIran

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