A numerical study of the effects of loading from diffusive deposition on the efficiency of fibrous filters

Dunnett, Sarah J.; Clement, C.F.

doi:10.1016/j.jaerosci.2005.08.001

Cited by 32 publications

(29 citation statements)

References 29 publications

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“…As the structure of fibrous filters is complex, many works have adopted cell models in which fluid flow equations are solved in a two dimensional cell surrounding the single fiber, where the boundary conditions take into account neighboring fibers [2]. Thus, the cell model was adopted to determine the flow field.…”

Section: Modelling For Single Fiber Efficiencymentioning

confidence: 99%

“…In this study, the deposition of aerosol particles in fibrous filters is considered to occur exclusively because of diffusion, which is one of the major particle collection mechanisms, especially when dealing with smaller particles (see [2] [3] [18]). …”

Section: The Physical Model For Single Fiber Efficiencymentioning

confidence: 99%

“…For fibrous filtration, the third term on the right-hand side of Equation (2) is much smaller than the other terms and can be neglected to give [2] [3] …”

Section: The Physical Model For Single Fiber Efficiencymentioning

confidence: 99%

See 2 more Smart Citations

Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

Filletti¹,

Silva²,

Ferreira³

2015

ACES

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In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of a classic model of literature for fiber efficiency, which is numerically solved along with the convection diffusion equation in polar coordinates for particle concentration, with associated initial and boundary conditions. A sufficient number of examples from two numerical simulations are employed to construct a database, from which parameters of a novel neural model are adjusted. This model is constructed based on the back propagation algorithm in order to map two features, namely Peclet number and packing density, which are extracted from the numerical simulations into the corresponding single fiber efficiency. The results indicate that the developed neural model can be trained in a reasonable computational time and is capable of estimating single fiber efficiency from examples of the test set with a maximum error of 1.7%.

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Section: Modelling For Single Fiber Efficiencymentioning

confidence: 99%

Section: The Physical Model For Single Fiber Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

Filletti¹,

Silva²,

Ferreira³

2015

ACES

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“…This approach can be efficiently used only for the initial stage of filtration, when previously deposited particles have not as yet significantly changed the fluid flow field cpe.czasopisma.pan.pl; degruyter.com/view/j/cpe and surface open for deposition. Some papers (Dunnett and Clement, 2006;Dunnett and Clement, 2012) take into account deposit growth, but the approach requires making assumptions of deposit structure. The important advantage in deep bed filtration modeling was introduction of lattice gas automata (Biggs et al, 2003) and lattice-Boltzmann method (Long and Hilpert, 2009), that allows to take into account the geometry of flow change due to deposition of suspended particles.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of particle loading in deep-bed filter. Transport, deposition and reentrainment

Przekop

Gradoń

2016

Chemical and Process Engineering

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Deep bed filtration is an effective method of submicron and micron particle removal from the fluid stream. There is an extensive body of literature regarding particle deposition in filters, often using the classical continuum approach. However, the approach is not convenient for studying the influence of particle deposition on filter performance (filtration efficiency, pressure drop) when nonsteady state boundary conditions have to be introduced. For the purposes of this work the latticeBoltzmann model describes fluid dynamics, while the solid particle motion is modeled by the Brownian dynamics. For aggregates the effect of their structure on displacement is taken into account. The possibility of particles rebound from the surface of collector or reentrainment of deposits to fluid stream is calculated by energy balanced oscillatory model derived from adhesion theory. The results show the evolution of filtration efficiency and pressure drop of filters with different internal structure described by the size of pores. The size of resuspended aggregates and volume distribution of deposits in filter were also analyzed. The model enables prediction of dynamic filter behavior. It can be a very useful tool for designing filter structures which optimize maximum lifetime with the acceptable values of filtration efficiency and pressure drop.

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“…For the last few years, researchers e.g., Dunnett and Clement (2006), Hosseini et al (2012), Wang et al (2012) have worked on the efficiency change of single dust-loaded fibers.…”

Section: Introductionmentioning

confidence: 99%

Modeling Penetration through Fibrous Filter during Dynamic Filtration

Bao

Zhao

et al. 2015

Aerosol Air Qual. Res.

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A model to quantitatively describe penetration change during filter clogging through a fiber filter is proposed. This model is developed using two different analytical methods and indicates that filter penetration is a function of the coefficient K and deposited dust mass in the filter. This is verified through a number of laboratory filter experiments. The coefficient K is obtained using regression analysis via the results of the experiments with different dust loading parameters. K is expressed as a function of aerosol particle diameter, efficiency, and filter material. A larger value of K illustrates a higher increasing rate of efficiency during clogging. A larger value of K also correlates with a smaller aerosol particle size and a higher penetration filter.

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A numerical study of the effects of loading from diffusive deposition on the efficiency of fibrous filters

Cited by 32 publications

References 29 publications

Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

Dynamics of particle loading in deep-bed filter. Transport, deposition and reentrainment

Modeling Penetration through Fibrous Filter during Dynamic Filtration

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