Optimizing Pump-and-Treat Method by Using Optimization-Simulation Models

Document Type : Original Research Article

Authors

1 Department of Civil Engineering, Faculty of Engineering, University of Torbat-Heydarieh (UTH), Torbat-Heydarieh, Iran

2 Department of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

3 Department of Water Engineering, Faculty of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

The goals of this research include investigating the efficiency of the finite element method and its combination with meta-heuristic algorithms to solve the optimization problem of the pump and treat (PAT) system. In this research, the hybrid optimization-simulation models were developed to determine the optimal groundwater remediation strategy using the pump and treat (PAT) system. The results indicated that when we consider minimizing the contaminant in groundwater at the end of the remediation period as the objective function, locating the pumping wells in the path of the contaminant flow and close to the contaminant source. In a single objective problem, the GA-FEM model with an average value of 0.0005036 in five runs of the model had the best performance among other models. The results of the two-objective problem indicated that MOMVO-FEM, despite a few solutions in optimal Pareto-front, could find a better location for pumping wells. Finally, it can be said that among factors such as the location of pumping wells and pumping rate, the most influential factor in choosing the right pumping and treatment policy is the proper location of pumping wells. Also, the location of contamination pumping wells does not necessarily correspond to the location of the contamination seepage.

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References

Akbarpour, A., Zeynali, M.J. & Nazeri Tahroudi, M., 2020. Locating optimal position of pumping Wells in aquifer using meta-heuristic algorithms and finite element method. Water Resources Management, 34(1), 21-34.
Boddula, S. & Eldho, T.I., 2017. A moving least squares based meshless local petrov-galerkin method for the simulation of contaminant transport in porous media. Engineering Analysis with Boundary Elements, 78, 8-19.
Darabi, B. & Ghafouri, H., 2007. Optimal identification of ground-water pollution sources.
Eldho, T.I. & Swathi, B., 2018. Groundwater Contamination Problems and Numerical Simulation. In Environmental Contaminants (p. 167-194). Springer, Singapore.
Erickson, M., Mayer, A. & Horn, J., 2002. Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Advances in Water Resources, 25(1), 51-65.
Freeze, R.A. & Cherry, J.A., 1979. Ground~ ater. Prentice-hall.
Gorelick, S.M., Voss, C.I., Gill, P.E., Murray, W., Saunders, M.A. & Wright, M.H., 1984. Aquifer reclamation design: the use of contaminant transport simulation combined with nonlinear programing. Water Resources Research, 20(4), 415-427.
Guneshwor, L., Eldho, T.I. & Vinod Kumar, A., 2018. Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization. Water Resources Management, 32(4), 1517-1538.
He, L., Xu, Z., Fan, X., Li, J. & Lu, H., 2017. Meta‐Modeling‐Based Groundwater Remediation Optimization under Flexibility in Environmental Standard. Water Environment Research, 89(5), 456-465.
Jafarzadeh, A., Pourreza-Bilondi, M., Akbarpour, A., Khashei-Siuki, A. & Samadi, S., 2021. Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies. Journal of Hydroinformatics, 23(6), 1271-1289.
Joswig, P. et al., 2017. Continuously optimizing a groundwater remediation system in complex fractured media.
Kumar, D., Ch, S., Mathur, S. & Adamowski, J., 2015. Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing. Journal of Water and Land Development.
Mategaonkar, M., Eldho, T.I. & Kamat, S., 2018. In-situ bioremediation of groundwater using a meshfree model and particle swarm optimization. Journal of Hydroinformatics, 20(4), 886-897.
Mirjalili, S., Jangir, P., Mirjalili, S.Z., Saremi, S. and Trivedi, I.N., 2017. Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Systems, 134, 50-71.
Mirjalili, S., Mirjalili, S.M. & Hatamlou, A., 2016. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
Ouyang, Q., Lu, W., Hou, Z., Zhang, Y., Li, S. & Luo, J., 2017. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method. Journal of contaminant hydrology, 200, 15-23.
Sbai, M.A., 2019. Well rate and placement for optimal groundwater remediation design with a surrogate model. Water, 11(11), 2233.
Seyedpour, S.M., Kirmizakis, P., Brennan, P., Doherty, R. & Ricken, T., 2019. Optimal remediation design and simulation of groundwater flow coupled to contaminant transport using genetic algorithm and radial point collocation method (RPCM). Science of the Total Environment, 669, 389-399.
Sharief, S.M.V., Eldho, T.I. & Rastogi, A.K., 2008. Optimal pumping policy for aquifer decontamination by pump and treat method using genetic algorithm. ISH Journal of Hydraulic Engineering, 14(2), 1-17.
Singh, A. & Minsker, B.S., 2008. Uncertainty‐based multiobjective optimization of groundwater remediation design. Water resources research, 44(2).
Singh, T.S. & Chakrabarty, D., 2011. Multiobjective optimization of pump-and-treat-based optimal multilayer aquifer remediation design with flexible remediation time. Journal of Hydrologic Engineering, 16(5), 413-420.
Wang, H.F. & Anderson, M.P., 1995. Introduction to groundwater modeling: finite difference and finite element methods. Academic Press.
Wang, Y., Xiao, W.H., Wang, Y.C., Wei, W.X., Liu, X.M., Yang, H. & Chen, Y., 2018. October. Simulating-optimizing coupled method for pumping well layout at a nitrate-polluted groundwater site. In IOP Conference Series: Earth and Environmental Science (v. 191(1), p. 012071). IOP Publishing.
Yang, A.L., Dai, Z.N., Yang, Q., Mcbean, E.A. & Lin, X.J., 2018, May. A Multi-objective Optimal Model for Groundwater Remediation under Health Risk Assessment in a Petroleum contaminated site. In IOP Conference Series: Earth and Environmental Science (v. 146(1), p. 012014). IOP Publishing.
Yang, A., Yang, Q., Fan, Y., Suo, M., Fu, H., Liu, J. & Lin, X., 2018. An Integrated Simulation, Inference and Optimization Approach for Groundwater Remediation with Two-Stage Health-Risk Assessment. Water, 10(6), 694.
Younes, A., Hoteit, H., Helmig, R. & Fahs, M., 2022. A robust Upwind Mixed Hybrid Finite Element method for transport in variably saturated porous media. Hydrology and Earth System Sciences Discussions, 1-29.
Zeynali, M.J., Pourreza-Bilondi, M., Akbarpour, A., Yazdi, J. & Zekri, S., 2022. Optimizing pump-and-treat method by considering important remediation objectives. Applied Water Science, 12(12), 268.
Zeynali, M.J., Pourreza-Bilondi, M., Akbarpour, A., Yazdi, J. & Zekri, S., 2022. Development of a contaminant concentration transport model for sulfate-contaminated areas. Applied Water Science, 12(7), 169.
Zeynali, M.J. & Shahidi, A., 2018. Performance assessment of grasshopper optimization algorithm for optimizing coefficients of sediment rating curve. AUT Journal of Civil Engineering, 2(1), 39-48.

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