Network simulation for deep bed filtration of Brownian particles

Chang, Yu‐Wei; Chen, Shan-Chih; Chan, Hsun-Chih; Lee, Eric

doi:10.1016/j.ces.2004.07.023

Cited by 14 publications

(13 citation statements)

References 38 publications

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“…2. From those trajectories of influent particles in the network, the temporal variations of the effluent concentrations and the pressure drop in a filtration history can be obtained, 9 and therefore the values of the initial collection efficiency g 0 and the filter coefficient a shown in Eq. 1 can be determined consequently.…”

Section: Brownian Dynamics Simulationmentioning

confidence: 99%

“…9 All pores in the network and particles in the influent are assumed to be of the Raleigh type size distribution. 10 Then, the pore size distribution can be assigned randomly to the bonds in the network as follows, and 0 \ a i \ 1 where the random number a i can be generated by using the standard computer software of IMSL, 11 r f and r mean are the radius of filter grains and the mean radius of pores, respectively.…”

Section: Network Modelmentioning

confidence: 99%

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Correlation equation for predicting filter coefficient under unfavorable deposition conditions

Chang

Chan

2008

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).A new correlation equation for predicting the filter coefficient under unfavorable deposition conditions is presented. By adopting the triangular network model of using the Brownian dynamic simulation method, as the sum of four individual deposition mechanisms, e.g., the Brownian diffusion, the DLVO interactions, the gravitational force, and the interception, the correlation equation is obtained by regressing against a broad range of parameter values governing particle deposition in deep bed filtration. The new correlation equation is able to describe previous experimental results well, especially for those submicro particles with significant Brownian motion behavior.

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Section: Brownian Dynamics Simulationmentioning

confidence: 99%

Section: Network Modelmentioning

confidence: 99%

Correlation equation for predicting filter coefficient under unfavorable deposition conditions

Chang

Chan

2008

AIChE Journal

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“…Similar to the previous papers by Ramarao et al [21] and by authors [8,9,22], with the consideration of the Brownian diffusion force in determining particle trajectories, the method of Brownian dynamics simulation is adopted in the present study. In this simulation method, the new position of the ith particle in the network model can be obtained by the integration of a Langevin type equation with a sufficient short time interval as follows [22],…”

Section: Brownian Dynamics Simulationmentioning

confidence: 93%

“…( Other symbol average value in their models, the particle's Brownian motion behavior was not considered. In our previous papers [8,9], we adopted the Brownian dynamic simulation method of solving Langevin type equations [10], and assumed that the porous media of the filter bed is unconsolidated, we had successfully tracked the motion of individual particles with Brownian motion behavior as they move through the filter bed, by using the 2-D models of the modified square network and the triangular network, respectively. From which, the temporal variations of the permeability reduction and the pressure drop, either caused by the straining or by the direct deposition of particles on the pore walls, were determined.…”

Section: Hamaker Constant A(t)mentioning

confidence: 99%

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Effects of three different network models on the filter coefficient of brownian particles

Chang

Chan

2006

Separation and Purification Technology

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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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Network simulation for deep bed filtration of Brownian particles

Cited by 14 publications

References 38 publications

Correlation equation for predicting filter coefficient under unfavorable deposition conditions

Correlation equation for predicting filter coefficient under unfavorable deposition conditions

Effects of three different network models on the filter coefficient of brownian particles

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

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