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Investigating the Optimization-Simulation Problem of Groundwater Remediation Under Various Scenarios

Document Type : Research Paper

Authors

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

2 Assistant Professor, Department of Water Engineering, Lorestan University, Khorramabad, Iran

3 Associate Professor, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources

10.22077/jwhr.2024.7919.1144

Abstract

The efficiency of groundwater remediation by the pump-and-treat (PAT) method is affected by several components. The most important of these components is the pumping wells' location. In this research, hybrid optimization-simulation models were developed to find the appropriate groundwater remediation strategy using the PAT method. The GA-FEM and NSGA-II-FEM models were used to solve two optimization problems for a real aquifer (Ghaen aquifer). These optimization problems were investigated from one objective problem and two objective problems in three scenarios. In solving the single-objective optimization problems, the objective was to determine the optimal location of three, five, and seven pumping wells with a rate of 600 m3/day to minimize the mean of carcinogenic human health risk. The results indicated that the GA-FEM model has a good efficiency with 356.2302×10-6, 356.2253×10-6, and 356.2226×10-6 for three scenarios, respectively. The results indicated that increasing the number of pumping wells between scenarios one and two, 0.0013% and scenarios one and three 0.0021% improves the amount of mean carcinogenic human health risk. In the two-objective problems, the second objective function was defined as minimizing the drawdown of the groundwater head. The results of the two-objective problems in three scenarios indicated that the NSGA-II algorithm had a good performance and the NSGA-II algorithm provided a well-distributed set of solutions along the Pareto-optimal front. Also, the results indicated that when there are 5 pumping wells, the minimum mean of carcinogenic human health risk is 3.56226 and by adding two more pumping wells, this amount reaches 3.56225, while the rate of groundwater drawdown increases by 20 meters. Therefore, increasing the number of pumping wells from one limit not only does not have a significant effect on reducing pollution but also causes an increased groundwater drawdown.

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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.
Alexander, A. C., Ndambuki, J. M., Salim, R. W., & Manda, A. K. (2018). Groundwater remediation optimization using solving constraint integer program (scip). Groundwater for Sustainable Development, 7, 176-184.
Bear, J. (1988). Dynamics of fluids in porous media. Courier Corporation.
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 (pp. 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.
Esfahani, H. K., & Datta, B. (2018). Fractal singularity–based multiobjective monitoring networks for reactive species contaminant source characterization. Journal of Water Resources Planning and Management, 144(6), 04018021.
Felfli, Z., Deb, N. C., Crothers, D. S. F., & Msezane, A. Z. (2002). Angular distributions of electrons in photoionization of 3s subshell of atomic chlorine. Journal of Physics B: Atomic, Molecular and Optical Physics, 35(19), L419.
Freeze, R. A., & Cherry, J. A. (1979). Groundwater. 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.
Joswig, P., Vernocchi, S., Pilone, A., Violetti, A., Brunetti, E., Pellegrini, M., ... & Grava, A. (2017). Continuously optimizing a groundwater remediation system in complex fractured media. Universtà degli Studi di Cagliari.
Kulkarni, N. H. (2015). Numerical simulation of groundwater recharge from an injection well. International Journal of Water Resources and Environmental Engineering, 7(5), 75-83.
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, 27, 29-40.
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.
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.
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, 191, 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, 146, 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.
Water Harvesting Research
Volume 7, Issue 1
March 2024
Pages 125-139
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