Sieving variations due to the choice in pore size distribution model

Derjani-Bayeh, Sylvana; Rodgers, V.G.J.

doi:10.1016/s0376-7388(01)00737-2

Cited by 17 publications

(12 citation statements)

References 13 publications

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“…Some authors have described the nonuniformity of circular capillaries by a parametric probability distribution function [15], the so-called beta function. Derjani-Bayeh and Rodgers [16] compared a two-parametric empirical model with conventional models such as the gamma distribution, which is named from the fact that it is an incomplete function arising from the sum of exponential processes.…”

Section: Introductionmentioning

confidence: 99%

Capillary Pressure Estimation Using Statistical Pore Size Functions

et al. 2007

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Capillary pressure curves, which have been employed for a long period of time by researchers interested in pore size distribution, are commonly obtained from experimental measurements. The dynamic capillary pressure that influences the flow is affected by many factors including the pore size characteristics and pore scale dynamics. Hence, it is important to investigate the variation of the estimated pore size distribution with capillary number. In this study, a glass type micromodel is considered as the porous media sample. A parametric probability density function is proposed to express the pore size distribution of the porous model, which is also measured using an image analysis technique. The capillary pressure saturation mathematical model is developed by integrating the pore size distribution function. Model parameters with a physical significance are estimated by fitting the model to the measured capillary pressure data at different capillary numbers. The results of capillary pressure obtained are well matched to the measured values. The results show that the trends of the extracted pore size distribution curves have similar trends, but they are not exactly the same. Therefore, the dynamic capillary pressure data alone are not sufficient for estimation of the pore size distribution. As a related development, the prediction of the capillary pressure curves based on measured pore size distributions is also presented. The proposed probability distribution function has the flexibility of representing a wide variety of pore size distributions.

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Section: Introductionmentioning

confidence: 99%

Capillary Pressure Estimation Using Statistical Pore Size Functions

et al. 2007

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“…The total pore number density fluctuates around 4·10 16 pore/m 2 for the 5, 20 and 30 kDa membranes, but for M300 which has a high mean radius and thus, a lower total pore number density (1.3·10 15 ) is needed to achieve the given flows.…”

Section: Fitting the Model To The Experimental Resultsmentioning

confidence: 97%

“…As mentioned above, among the several probability density functions, it has been reported that log-normal distribution is suitable for the pore number distribution of many membranes [7,16]. The log-normal distribution is given by Eq.…”

Section: New Approach To Analyze Lldp Resultsmentioning

confidence: 99%

Fitting approach to liquid–liquid displacement porosimetry based on the log-normal pore size distribution

Antón¹,

Calvo²,

Alvarez³

et al. 2014

Journal of Membrane Science

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Liquid-liquid displacement porosimetry (LLDP) has been used to characterize several UF membranes in a wide range of molecular weight cut-offs (MWCO). A new method to convert porosimetric data into pore size distributions and related information has been developed based on assuming log-normal pore size distributions. The results of this are in good agreement with those from the customary data conversion algorithm (as derived by Grabar and Nikitine). The proposed method can also be used when a reduced number of experimental data points is available, leading to a significant reduction of data acquisition time needed to complete a reliable analysis.

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“…Given , r and an initial value of h = 0, we can obtain from (24), R from (23), P i from (22), P f from (21), u from (20) and hence dh/dt from (19). It seems unlikely that there will be an analytical solution for this system of equations.…”

Section: Droplet Flowmentioning

confidence: 99%

“…A review of different pore size probability distribution functions [21] showed that the lognormal distribution is adequate as a general function on which to base the analysis here.…”

Section: Pore Size Distributionmentioning

confidence: 99%

A comparison of liquid–liquid porosimetry equations for evaluation of pore size distribution

Morison

2008

Journal of Membrane Science

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Sieving variations due to the choice in pore size distribution model

Cited by 17 publications

References 13 publications

Capillary Pressure Estimation Using Statistical Pore Size Functions

Capillary Pressure Estimation Using Statistical Pore Size Functions

Fitting approach to liquid–liquid displacement porosimetry based on the log-normal pore size distribution

A comparison of liquid–liquid porosimetry equations for evaluation of pore size distribution

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