IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i9p4776-d546578.html
   My bibliography  Save this article

Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models

Author

Listed:
  • Michael Saah Hayford

    (College of Science and Engineering, James Cook University, Townsville, QLD 4814, Australia)

  • Bithin Datta

    (College of Science and Engineering, James Cook University, Townsville, QLD 4814, Australia)

Abstract

The most important first step in the management and remediation of contaminated groundwater aquifers is unknown contaminant source characterization. Often, the hydrogeological field data available for accurate source characterization are very sparse. In addition, hydrogeological and geochemical parameter estimates and field measurements are uncertain. Particularly in complex contaminated sites such as abandoned mine sites, the geochemical processes are very complex and identifying the sources of contamination in terms of location, magnitude, and duration, and determination of the pathways of pollution become very difficult. The reactive nature of the contaminant species makes the geochemical transport process very difficult to model and predict. Additionally, the source identification inverse problem is often non-unique and ill posed. This study is about developing and demonstrating a source characterization methodology for a complex contaminated aquifer with multiple reactive species. This study presents linked simulation optimization-based methodologies for characterization of unknown groundwater pollution source characteristics, i.e., location, magnitude and duration or timing. Optimization models are solved using an adaptive simulated annealing (ASA) optimization algorithm. The performance of the developed methodology is evaluated for different complex scenarios of groundwater pollution such as distributed mine waste dumps with reactive chemical species. The method is also applied to a real-life contaminated aquifer to demonstrate the potential applicability and optimal characterization results. The illustrative example site is a mine site in Northern Australia that is no longer active.

Suggested Citation

  • Michael Saah Hayford & Bithin Datta, 2021. "Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models," IJERPH, MDPI, vol. 18(9), pages 1-42, April.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4776-:d:546578
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/9/4776/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/9/4776/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. L. Ingber, 1993. "Simulated annealing: Practice versus theory," Lester Ingber Papers 93sa, Lester Ingber.
    2. L. Ingber & B. Rosen, 1992. "Genetic algorithms and very fast simulated reannealing: A comparison," Lester Ingber Papers 92ga, Lester Ingber.
    3. L. Ingber, 1989. "Very fast simulated re-annealing," Lester Ingber Papers 89vf, Lester Ingber.
    4. Pooran Mahar & Bithin Datta, 2000. "Identification of Pollution Sources in Transient Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 14(3), pages 209-227, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Bowman & L. Ingber, 1997. "Canonical momenta of nonlinear combat," Lester Ingber Papers 97cm, Lester Ingber.
    2. Lester Ingber & Radu Paul Mondescu, 2000. "Optimization of Trading Physics Models of Markets," Papers physics/0007075, arXiv.org.
    3. L. Ingber & J.K. Wilson, 2000. "Statistical mechanics of financial markets: Exponential modifications to Black-Scholes," Lester Ingber Papers 00fm, Lester Ingber.
    4. L. Ingber & R.P. Mondescu, 2003. "Automated internet trading based on optimized physics models of markets," Lester Ingber Papers 03ai, Lester Ingber.
    5. L. Ingber, 1996. "Adaptive simulated annealing (ASA): Lessons learned," Lester Ingber Papers 96as, Lester Ingber.
    6. Bergey, Paul K. & Ragsdale, Cliff, 2005. "Modified differential evolution: a greedy random strategy for genetic recombination," Omega, Elsevier, vol. 33(3), pages 255-265, June.
    7. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2020. "Establishing optimal forest harvesting regulation with continuous approximation," Operations Research Perspectives, Elsevier, vol. 7(C).
    8. Mayer, D. G. & Belward, J. A. & Burrage, K., 1996. "Use of advanced techniques to optimize a multi-dimensional dairy model," Agricultural Systems, Elsevier, vol. 50(3), pages 239-253.
    9. L. Ingber, 2018. "Quantum Variables in Finance and Neuroscience," Lester Ingber Papers 18qv, Lester Ingber.
    10. L. Ingber, 2022. "Quantum Variables in Finance," Lester Ingber Papers 22qv, Lester Ingber.
    11. L. Ingber, 2018. "Model of Models (MOM)," Lester Ingber Papers 18mo, Lester Ingber.
    12. Ingber, Lester, 2000. "High-resolution path-integral development of financial options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 283(3), pages 529-558.
    13. L. Ingber, 2018. "Quantum calcium-ion interactions with EEG," Lester Ingber Papers 18qc, Lester Ingber.
    14. Dimitris Bertsimas & Omid Nohadani, 2010. "Robust optimization with simulated annealing," Journal of Global Optimization, Springer, vol. 48(2), pages 323-334, October.
    15. L. Ingber, 1992. "Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions," Lester Ingber Papers 92gm, Lester Ingber.
    16. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    17. Pereira, Robert, 2000. "Genetic Algorithm Optimisation for Finance and Investments," MPRA Paper 8610, University Library of Munich, Germany.
    18. Genetha Anne Gray & Tamara G. Kolda & Ken Sale & Malin M. Young, 2004. "Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination," INFORMS Journal on Computing, INFORMS, vol. 16(4), pages 406-418, November.
    19. L. Ingber, 1993. "Statistical mechanics of combat and extensions," Lester Ingber Papers 93ce, Lester Ingber.
    20. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4776-:d:546578. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.