Colloid particle size‐dependent dispersivity

Chrysikopoulos, Constantinos V.; Katzourakis, Vasileios E.

doi:10.1002/2014wr016094

Cited by 130 publications

(73 citation statements)

References 83 publications

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“…Larger particles experience more irregular movements induced by the limit of pore size and lead to a larger dispersivity (Figure b), which is in agreement with the theoretical model of Jourak et al () and the experimental results of Bennacer et al (). As a result, the dispersivity of SPs ( α d range: 0.19–0.45 cm) is generally higher than that of DT ( α d range: 0.19–0.26 cm) for the two particles ( D 50 = 25 and 47 μm), especially at lower temperatures ( T = 15–30°C; Figure a), but the difference becomes smaller with increasing temperature, indicating that the dispersion coefficients of DT are inherently different from that of SP (Ahfir et al, ; Chrysikopoulos & Katzourakis, ).…”

Section: Resultsmentioning

confidence: 95%

“…For spherical particles, the molecular diffusion coefficient is determined by the Stokes‐Einstein diffusion equation (Chrysikopoulos & Katzourakis, ):

D_{AB} = \frac{k_{B} T_{abs}}{3 normalπ μ_{B} d_{p}}

…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the molecular diffusion ( D AB ) of SP can be neglected, and thus the hydrodynamic dispersion coefficient of SP is mainly determined by its mechanical dispersion. For the mechanical dispersion term in Equation , the empirical coefficient λ is in a range of 1–1.3, and it is generally taken as λ = 1 (Bennacer et al, ; Chrysikopoulos & Katzourakis, ). As a result, the hydrodynamic dispersion coefficient can be expressed as D = α d u 0 , and the longitudinal dispersivity α d ( α d = D / u 0 ) can be regarded as the dimension of length.…”

Section: Resultsmentioning

confidence: 99%

“…However, inconsistent results have also been reported (Keller et al, ; Vasiliadou & Chrysikopoulos, ). Some studies have shown that there existed a critical ratio of the particle diameter to the collector diameter, below which the straining rate was negligibly small (e.g., Chrysikopoulos & Katzourakis, ; Liu et al, ; Xu et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The effect of temperature on the seepage transport of suspended particles in a porous medium

Bai

Long

Rao

et al. 2016

Hydrological Processes

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The main purpose of this study is to experimentally investigate the effect of temperature on the seepage transport of suspended particles (SP) with a median diameter of 10–47 μm in a porous medium for various seepage velocities. The results show that the rise of temperature accelerates the irregular movements of SPs in the porous medium and reduces their migration velocity. As a result, the pore volume corresponding to the peak value of the breakthrough curves is apparently delayed, and the peak value in the effluent is decreased. The migration velocity of SPs decreases with increasing particle size, regardless of the Darcy velocity and temperature. The longitudinal dispersivity of SPs decreases slightly with increasing temperature and then remains almost unchanged. Larger particles experience more irregular movements induced by the limit of pore size, which leads to a larger dispersivity. The deposition coefficient increases with increasing temperature, especially in the case of a high seepage velocity, and then tends to be stable. The deposition coefficient for large‐sized particles is higher than that for small‐sized particles, which can be attributed to the restriction of large‐sized particles by the narrow pores in the porous medium. The recovery rate decreases slightly with the increase of temperature until a critical value is reached, beyond which it remains almost unchanged. In summary, temperature is a significant factor affecting the transport and deposition of SPs in the porous medium, and the transport parameters such as particle velocity, dispersivity, and deposition coefficient.

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Section: Resultsmentioning

confidence: 95%

“…For spherical particles, the molecular diffusion coefficient is determined by the Stokes‐Einstein diffusion equation (Chrysikopoulos & Katzourakis, ):

D_{AB} = \frac{k_{B} T_{abs}}{3 normalπ μ_{B} d_{p}}

…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effect of temperature on the seepage transport of suspended particles in a porous medium

Bai

Long

Rao

et al. 2016

Hydrological Processes

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“…The factor by which the dispersion coefficient in the field is greater than in the laboratory ranges from 1 to 10,000 for observation scales from 5 to 2000 m [55,59]. In addition to the effect of medium length scale [60], it has been reported recently that the dispersivity will also increase with NPs size [61]. Therefore, for this reason the dispersion coefficient ( ) of NPs at the field scale is assumed to be comparatively larger (∼10 −5 m 2 /s) in magnitude than the diffusion coefficient for NPs at the laboratory scale (∼10 −8 m 2 /s for a NP with a diameter of 10 nm), which can be obtained using the Stokes-Einstein equation as follows:…”

Section: Hydrodynamic Dispersion Coefficientmentioning

confidence: 99%

Retention of Nanoparticles: From Laboratory Cores to Outcrop Scale

Singh

Javadpour

2017

Geofluids

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Laboratory experiments on small scale core plugs have shown controlled nanoparticles (NPs) retention. The length scale of subsurface media where NPs must be transported is an important factor that should be accounted for in a comprehensive manner when translating laboratory results to field scale. This study investigates the fraction of NPs retained inside porous media as a function of length scale of the media. A two-dimensional numerical model was used to simulate the retention of NPs at multiple scales of porous media, starting from laboratory scale cores to heterogeneous outcrop scales. Retention of NPs is modeled based on the concept of reversible and irreversible retention, by using the laboratory scale determined parameters. Our results show that the fraction of retained NPs increases nonlinearly with the length scale of the homogeneous media. The results also show that if the heterogeneity of the medium is consistent across scales, the fraction of retained NPs would behave just like homogeneous medium. In this study, small change in heterogeneity at two outcrop scales affects the retention of NPs, suggesting that heterogeneity may significantly impact the retention behavior of NPs that may not necessarily follow the behavior predicted from homogeneous cores (or periodically heterogeneous medium).

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Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network

Babakhani

Bridge

Doong

et al. 2017

Water Resources Research

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The continuing rapid expansion of industrial and consumer processes based on nanoparticles (NP) necessitates a robust model for delineating their fate and transport in groundwater. An ability to reliably specify the full parameter set for prediction of NP transport using continuum models is crucial. In this paper we report the reanalysis of a data set of 493 published column experiment outcomes together with their continuum modeling results. Experimental properties were parameterized into 20 factors which are commonly available. They were then used to predict five key continuum model parameters as well as the effluent concentration via artificial neural network (ANN)-based correlations. The Partial Derivatives (PaD) technique and Monte Carlo method were used for the analysis of sensitivities and model-produced uncertainties, respectively. The outcomes shed light on several controversial relationships between the parameters, e.g., it was revealed that the trend of K att with average pore water velocity was positive. The resulting correlations, despite being developed based on a ''black-box'' technique (ANN), were able to explain the effects of theoretical parameters such as critical deposition concentration (CDC), even though these parameters were not explicitly considered in the model. Porous media heterogeneity was considered as a parameter for the first time and showed sensitivities higher than those of dispersivity. The model performance was validated well against subsets of the experimental data and was compared with current models. The robustness of the correlation matrices was not completely satisfactory, since they failed to predict the experimental breakthrough curves (BTCs) at extreme values of ionic strengths. Plain Language Summary Models based on advection-dispersion-equation (ADE), have succeeded in describing a variety of nanoparticle (NP) transport mechanisms within subsurface porous media. These models are usually fitted against known observation data to obtain the unknown parameters. Nevertheless, the parameters determined in this way cannot be used for a new problem of the same type and again there exists the need for the data to calibrate the model parameters for a new problem. Black box models, such as artificial neural network (ANN), have been mostly, if not all the times, used in the same way of ADE models, i.e., single problem solver. In this paper we use the ability of ANN to develop a series of simple correlation matrices that can be easily used for the prediction of ADE parameters in the new problem without the need for additional calibration. Although comparisons between ANN model predictions and experimental data show that there is still further work to be done, our approach out-performs other comparable models and offers new insight into the complex interactions among the factors determining NP transport and fate in the environment.

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Colloid particle size‐dependent dispersivity

Cited by 130 publications

References 83 publications

The effect of temperature on the seepage transport of suspended particles in a porous medium

The effect of temperature on the seepage transport of suspended particles in a porous medium

Retention of Nanoparticles: From Laboratory Cores to Outcrop Scale

Parameterization and prediction of nanoparticle transport in porous media: A reanalysis using artificial neural network

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