Transport of polymer stabilized nano-scale zero-valent iron in porous media

Mondal, Pijush Kanti; Furbacher, Paul; Cui, Ziteng; Król, Marianna; Sleep, Brent E.

doi:10.1016/j.jconhyd.2017.11.004

Cited by 22 publications

(5 citation statements)

References 47 publications

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“…is the average concentration of the contaminant, N is the number of nodes, C 1 is the concentration of the contaminant at each node, C 1 FDCC is the final desired concentration of the contaminant, TC is the total cost of the remediation process, n OS is the number of oxidant sources and COS denotes the cost of each oxidant source. To achieve the objective of the study, we wish to minimise GA1 and GA3 and maximise GA2 min GA1 max GA2 min GA3 (23) by considering the following constraints:…”

Section: Remediation Design Optimization Using the Ga Approachmentioning

confidence: 99%

“…In recent decades, groundwater contamination has raised concerns of the risk to human health and ecological systems. These concerns are increased because of the growing number of contaminated sites [1,2], and sources of contamination, combined with an increased wordwide water demand from domestic, industrial and agricultural sectors. Since groundwater is a well-defined pathway for contaminant transport and transfer to receptors, the importance of groundwater management is increasing.…”

Section: Introductionmentioning

confidence: 99%

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

Seyedpour

Valizadeh

Kirmizakis

et al. 2021

Water

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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.

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Section: Remediation Design Optimization Using the Ga Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Seyedpour

Valizadeh

Kirmizakis

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…Removal. The storage capability and lifetime of as-synthesized nanoparticles are important to understand when scale-up synthesis and production for use in pilot scale demonstrations are considered [25,[37][38][39]. The nanoparticles were stored at room temperature in argon-bubbled water and then were tested once a week for three weeks following the synthesis week (i.e., week zero).…”

Section: Effect Of Storage Time On Azomentioning

confidence: 99%

Removal of Synthetic Azo Dye Using Bimetallic Nickel-Iron Nanoparticles

Foster

Estoque

Voecks

et al. 2019

Journal of Nanomaterials

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Bimetallic nanoparticles comprised of iron (Fe) and nickel (Ni) were investigated for the removal of an azo dye contaminant in water. Morphology (core shell and alloy) and metal molar ratio (Ni2Fe10, Ni5Fe10, and Ni10Fe10) were tested as key nanoparticle properties. The shelf life of the nanoparticles was tested over a 3-week period, and the effect of initial nanoparticle concentration on dye removal was evaluated. The highest initial nanoparticle concentration (1000 mg/L) showed consistent Orange G removal and the greatest extent of dye removal, as compared to the other tested concentrations (i.e., 750 mg/L, 500 mg/L, and 250 mg/L) for the same nanoparticle morphology and metal molar ratio. The metal molar ratio significantly affected the performance of the core shell morphology, where overall dye removal was found to be 66%, 89%, and 98% with an increasing molar ratio (Ni2Fe10 → Ni5Fe10 → Ni10Fe10). In contrast, the overall removal of the dye for all molar ratios of the alloy nanoparticles only resulted in a variability of ±0.005%. When stored in water for 3 weeks, core shell nanoparticles lost reactivity with an average>17% loss in removal with each passing week. However, the alloy nanoparticles were able to continually remove Orange G from solution after 3 weeks of storage to ~97% when used at a starting nanoparticle concentration of 1000 mg/L. Overall, the Ni2Fe10, Ni5Fe10, and Ni10Fe10 alloy nanoparticles with a starting nanoparticle concentration of 1000 mg/L resulted in the greatest dye removal of 97%, 99%, and 98%, respectively. Kinetic rate models were used to analyze dye removal rate constants as a function of nanoparticle properties. Kinetic rate models were seen to differ from core shell (first-order kinetics) to alloy morphology (second-order kinetics). Alloy nanoparticles resulted in as high as X kinetic rate constant, and core shell nanoparticles resulted in as high as XX kinetic rate constant. Metal leaching from the nanoparticles was investigated; alloy nanoparticles resulted in leaching of 3% Fe and 5% Ni which is similar to core shell leaching of 3.2% Fe and 4.3% Ni from the Fe10Ni10 nanoparticles.

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“…extracting and re-injecting groundwater) or flushing with water or stabilizing solution postinjection [211][212][213]. Other strategies for improving mobility involve decreasing colloid size via stabilization and synthesis techniques [189], starving biocolloids [214,215], dynamic size changes [201], or developing biocolloids that either produce biosurfactants or are naturally adhesion resistant [216].…”

Section: Injected Colloid Mobilitymentioning

confidence: 99%