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

Author

Listed:
  • Sina Sadeghfam

    (Faculty of Engineering, University of Maragheh)

  • Yousef Hassanzadeh

    (University of Tabriz)

  • Rahman Khatibi

    (GTEV-ReX Limited)

  • Ata Allah Nadiri

    (Faculty of Natural Sciences, University of Tabriz)

  • Marjan Moazamnia

    (University of Tabriz)

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.

Suggested Citation

  • Sina Sadeghfam & Yousef Hassanzadeh & Rahman Khatibi & Ata Allah Nadiri & Marjan Moazamnia, 2019. "Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1123-1145, February.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:3:d:10.1007_s11269-018-2171-6
    DOI: 10.1007/s11269-018-2171-6
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    References listed on IDEAS

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    1. Q. Yang & L. He & H. Lu, 2013. "A Multiobjective Optimisation Model for Groundwater Remediation Design at Petroleum Contaminated Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2411-2427, May.
    2. Mohammad Kazemzadeh-Parsi & Farhang Daneshmand & Mohammad Ahmadfard & Jan Adamowski, 2015. "Optimal Remediation Design of Unconfined Contaminated Aquifers Based on the Finite Element Method and a Modified Firefly Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2895-2912, June.
    3. Liang-Cheng Chang & Hone-Jay Chu & Chin-Tsai Hsiao, 2012. "Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1253-1269, March.
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    Cited by:

    1. Ali Al-Maktoumi & Mohammad Mahdi Rajabi & Slim Zekri & Chefi Triki, 2021. "A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3447-3462, September.
    2. Partha Majumder & T.I. Eldho, 2020. "Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 763-783, January.
    3. Seyed Hassan Mirhashemi & Farhad Mirzaei & Parviz Haghighat Jou & Mehdi Panahi, 2022. "Evaluation of Four Tree Algorithms in Predicting and Investigating the Changes in Aquifer Depth," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4607-4618, September.
    4. Fan, Qiuyan & Hajiyeva, Aytan Merdan, 2022. "Nexus between energy efficiency finance and renewable energy development: Empirical evidence from G-7 economies," Renewable Energy, Elsevier, vol. 195(C), pages 1077-1086.
    5. Yu Chen & Guodong Liu & Xiaohua Huang & Yuchuan Meng, 2022. "Groundwater Remediation Design Underpinned By Coupling Evolution Algorithm With Deep Belief Network Surrogate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2223-2239, May.

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