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

Seyedpour, Seyed Morteza ; Valizadeh, Iman ; Kirmizakis, Panagiotis ; Doherty, Rory ; Ricken, Tim (2022)
Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method.
In: Water, 2022, 13 (3)
doi: 10.26083/tuprints-00017800
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Optimization of the Groundwater Remediation Process Using a Coupled Genetic Algorithm-Finite Difference Method
Language: English
Date: 9 February 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Water
Volume of the journal: 13
Issue Number: 3
Collation: 18 Seiten
DOI: 10.26083/tuprints-00017800
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

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.

Uncontrolled Keywords: groundwater flow, reactive contaminant transport, in situ chemical oxidation, finite difference method, genetic algorithm, physical sandbox experiment
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-178003
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Cyber-Physical Simulation (CPS)
Date Deposited: 09 Feb 2022 14:41
Last Modified: 14 Nov 2023 19:03
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17800
PPN: 505613522
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