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Article

Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method

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Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, 70569 Stuttgart, Germany
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Porous Media Laboratory, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, 70569 Stuttgart, Germany
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Cyber-Physical Simulation Group, Department of Mechanical Engineering, Technical University of Darmstadt, Dolivostraße 15, 64293 Darmstadt, Germany
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Department of Geosciences, College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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School of Natural and Built Environment, Queen’s University Belfast, Belfast BT9 5AG, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Vincent Vallès, Alexander Yakirevich and Laurent Barbiero
Water 2021, 13(3), 383; https://doi.org/10.3390/w13030383
Received: 22 December 2020 / Revised: 22 January 2021 / Accepted: 23 January 2021 / Published: 1 February 2021
(This article belongs to the Special Issue Modeling and Prediction of Groundwater Contaminant Plumes)
In situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design. View Full-Text
Keywords: groundwater flow; reactive contaminant transport; in situ chemical oxidation; finite difference method; genetic algorithm; physical sandbox experiment groundwater flow; reactive contaminant transport; in situ chemical oxidation; finite difference method; genetic algorithm; physical sandbox experiment
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MDPI and ACS Style

Seyedpour, S.M.; Valizadeh, I.; Kirmizakis, P.; Doherty, R.; Ricken, T. Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method. Water 2021, 13, 383. https://doi.org/10.3390/w13030383

AMA Style

Seyedpour SM, Valizadeh I, Kirmizakis P, Doherty R, Ricken T. Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method. Water. 2021; 13(3):383. https://doi.org/10.3390/w13030383

Chicago/Turabian Style

Seyedpour, S. M., I. Valizadeh, P. Kirmizakis, R. Doherty, and T. Ricken. 2021. "Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method" Water 13, no. 3: 383. https://doi.org/10.3390/w13030383

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