Importance of filter’s microstructure in dynamic filtration modeling of gasoline particulate filters (GPFs): Inhomogeneous porosity and pore size distribution

Gong, Jian; Stewart, Mark; Zelenyuk, Alla; Strzelec, Andrea; Viswanathan, Sandeep; Rothamer, David; Foster, David E.; Rutland, Christopher J.

doi:10.1016/j.cej.2018.01.006

Cited by 63 publications

(20 citation statements)

References 44 publications

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“…In this state, the porosities across the substrate of the coconut fibers might influence the filter efficiency. As confirmed in the previous study, the porosity distribution of the filter, including the pore size distribution, can represent the performance of a particulate filter [13]. The porosity level of a filter has a correlation with the filter thickness and density.…”

Section: Discussionsupporting

confidence: 67%

Developing Fine Particle Filtering System for Motorcycle Exhaust Using Coco Fibers

Wardoyo¹,

Yuana²,

Elbasari³

2019

GEOMATE

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Fine particles from motor vehicle exhaust emissions become a major problem for the environment and human health. This burden requires a new technology of filtering system with high efficiency. However, the filtering system for motorcycle exhaust is still limited. Thus, we developed a new filtration system using coco fibers mixed to tapioca glues with the variation ratio such as 50:50 (Filter 1); 60:40 (Filter 2); 70:30 (Filter 3); and 80:20 (Filter 4). The efficiency is calculated by the ratio of the fine particle concentration in the exhaust emission before and after passing through the filter. The fine particle concentration was measured using a Digital Dust Monitor (Kanomax, Model 3443) when the motorcycle was operating in the idle condition. The fine particle concentration was measured for twenty sampling time. The filter efficiency was tested using three different motorcycles (M1, M2, and M3). The result showed that the filter efficiency was found between 12% and 33% depending on the filter thickness, density, and composition of the coco fiber and the glue.

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Section: Discussionsupporting

confidence: 67%

Developing Fine Particle Filtering System for Motorcycle Exhaust Using Coco Fibers

Wardoyo¹,

Yuana²,

Elbasari³

2019

GEOMATE

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“…In order to apply the Kalman filter-based approach for the simulation of other particulate filters with very different microstructure such as gasoline particulate filter (GPF) with inhomogeneous porosity and pore size distribution in Gong et al, 32 the filtration model within the reduced order model equations have to be modified to account for the varying porosity and pore size distribution within each zone of the multi-zone reduced order model presented in this work. In the reduced order MPF model, the substrate wall within each zone is discretized into n number of slabs.…”

Section: Discussionmentioning

confidence: 99%

“…At each slab, the porosity of the substrate wall is evolved based on the PM mass deposition on a series of spherical collectors considering the Brownian diffusion and interception filtration mechanisms. 17 With the inhomogeneous porosity and pore size distribution of the filters, the initial porosity of the each slab of the substrate wall will have to be modified along with the varying diameters of the spherical collectors as explained in Gong et al 32 By accounting for the varying porosity and pore size distribution (i.e. diameter of the spherical collector) for each slab, the filtration efficiency, wall permeability, and wall pressure drop can be estimated at each zone using the reduced order MPF model classical filtration equations in Mahadevan 17 and EKF equations presented in this research.…”

Section: Discussionmentioning

confidence: 99%

Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter

Mahadevan

Johnson

Shahbakhti

2018

International Journal of Engine Research

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The knowledge of the temperature and particulate matter mass distribution is essential for monitoring the performance and durability of a catalyzed particulate filter. A catalyzed particulate filter model was developed, and it showed capability to accurately predict temperature and particulate matter mass distribution and pressure drop across the catalyzed particulate filter. However, the high-fidelity model is computationally demanding. Therefore, a reduced order multi-zone particulate filter model was developed to reduce computational complexity with an acceptable level of accuracy. In order to develop a reduced order model, a parametric study was carried out to determine the number of zones necessary for aftertreatment control applications. The catalyzed particulate filter model was further reduced by carrying out a sensitivity study of the selected model assumptions. The reduced order multi-zone particulate filter model with 5 × 5 zones was selected to develop a catalyzed particulate filter state estimator considering its computational time and accuracy. Next, a Kalman filter–based catalyzed particulate filter estimator was developed to estimate unknown states of the catalyzed particulate filter such as temperature and particulate matter mass distribution and pressure drop (Δ P) using the sensor inputs to the engine electronic control unit and the reduced order multi-zone particulate filter model. A diesel oxidation catalyst estimator was also integrated with the catalyzed particulate filter estimator in order to provide estimates of diesel oxidation catalyst outlet concentrations of NO2 and hydrocarbons and inlet temperature for the catalyzed particulate filter estimator. The combined diesel oxidation catalyst–catalyzed particulate filter estimator was validated for an active regeneration experiment. The validation results for catalyzed particulate filter temperature distribution showed that the root mean square temperature error by using the diesel oxidation catalyst–catalyzed particulate filter estimator is within 3.2 °C compared to the experimental data. Similarly, the Δ P estimator closely simulated the measured total Δ P and the estimated cake pressure drop error is within 0.2 kPa compared to the high-fidelity catalyzed particulate filter model.

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“…The objective of this study is to investigate deep-bed filtration process of the porous wall taking the PSD into consideration, as shown in Figure 1. In this paper, a non-uniform multi-scale porous wall model based on pore size distribution probability density function is constructed according to the dynamic Heterogeneous Multiscale Filtration (HMF) model, 27–29 considering three different PSDs. The evolution of particle deposition and filtration performance of a GPF whose porosity is higher at both ends than that in the middle of the porous wall are studied.…”

Section: Introductionmentioning

confidence: 99%

Simulation of deep-bed filtration of a gasoline particulate filter with inhomogeneous wall structure under different particle size distributions

Shen

Zhang

et al. 2021

International Journal of Engine Research

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In order to describe the microstructure of the porous wall of a gasoline particulate filter (GPF), a pore size distribution based on a probability density function (PDF) and a non-uniform porosity distribution are introduced. The dynamic process of deep-bed filtration in GPF with inhomogeneous wall structure is studied, considering different particle size distributions (PSDs). The results show that most of the particles are captured in the top region of the porous wall, in which the porosity and permeability reduce more obviously during dynamic filtration, and the bottom of the porous wall contributes little to the overall filtration. As time increases, the filtration efficiency of the porous wall for each particle size increases, and the most penetrating particle’s diameter becomes smaller gradually. The dynamic evolution of characteristic parameters of the porous wall, the most efficient filtration region, the pressure drop and the duration of deep-bed filtration are strongly influenced by PSD. This research illustrates the necessity to consider difference of PSDs when working on the filtration process of GPFs.

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Importance of filter’s microstructure in dynamic filtration modeling of gasoline particulate filters (GPFs): Inhomogeneous porosity and pore size distribution

Cited by 63 publications

References 44 publications

Developing Fine Particle Filtering System for Motorcycle Exhaust Using Coco Fibers

Developing Fine Particle Filtering System for Motorcycle Exhaust Using Coco Fibers

Development of a Kalman filter estimator for simulation and control of particulate matter distribution of a diesel catalyzed particulate filter

Simulation of deep-bed filtration of a gasoline particulate filter with inhomogeneous wall structure under different particle size distributions

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