Advertisement

Emission Control Science and Technology

, Volume 3, Issue 2, pp 171–201 | Cite as

Predicting Pressure Drop, Temperature, and Particulate Matter Distribution of a Catalyzed Diesel Particulate Filter Using a Multi-Zone Model Including Cake Permeability

  • Boopathi S. Mahadevan
  • John H. Johnson
  • Mahdi Shahbakhti
Article

Abstract

A multi-zone particulate filter (MPF) model was developed to predict pressure drop and PM oxidation of a catalyzed diesel particulate filter (CPF). The MPF model builds upon our previous work (Mahadevan et al., J Emiss Control Sci Technol 1:183–202, 2015; Mahadevan et al., J Emiss Control Sci Technol 1:255–283, 2015) by adding a new multi-zone version of a classical 1-D filtration model (Konstandopoulos and Johnson, 1989) to account for PM filtration within the substrate wall and PM cake of a CPF. In addition, pressure drop (∆P) simulation capability was also developed for the MPF model in order to simulate the pressure drop across the substrate wall and PM cake of the CPF. A cake permeability model was developed based on fundamental research findings in the literature. The PM cake and wall pressure drop simulation accounts for the wall and cake permeability variation during loading, PM oxidation, and an additional post-loading after oxidation. This extended MPF model was calibrated using 18 runs of experimental data from a Cummins ISL engine that consisted of passive and active regeneration data sets for ULSD, B10, and B20 fuels. The validation results show that the new MPF model can predict PM loading with a maximum root mean square (RMS) error of 7.4% and predict (∆P) across the filter with an RMS error of within 7.2%. It is found that the permeability of the PM cake layer increases rapidly during PM oxidation. The increase in permeability was attributed to the damage in the PM cake and was simulated using the newly developed cake permeability model. The increased permeability of the damaged PM cake layer and oxidation of cake PM leads to near zero cake PM pressure drop during PM oxidation for the passive and active regeneration experiments.

Keywords

Diesel particulate filter model Particulate matter oxidation Pressure drop Cake permeability modeling Substrate wall permeability modeling 

Abbreviations

AR

Active regeneration

B10

Diesel blend (ULSD) with 10% biodiesel

B20

Diesel blend (ULSD) with 20% biodiesel

CPF

Catalyzed particulate filter

CO2

Carbon dioxide

DOC

Diesel oxidation catalyst

DPF

Diesel particulate filter

ECU

Electronic control unit

HC

Hydrocarbons

MPF

Multi-zone particulate filter

MTU

Michigan Technological University

NO2

Nitrogen dioxide

NO

Nitrogen monoxide

O2

Oxygen

OBD

On-board diagnostics

PO

Passive oxidation

PM

Particulate matter

RMS

Root mean square

RU

Ramp up

SCR

Selective catalytic reduction

ULSD

Ultra-low-sulfur diesel

∆P

Pressure drop

1-D

One-dimensional

3-D

Three-dimensional

Notes

Acknowledgments

The authors would like to thank Kenneth Shiel and James Pidgeon of Michigan Technological University for collecting the temperature distribution data presented in this work and Dr. Kiran Premchand for assistance in understanding his 1-D model simulation of the data presented in this work. We would like to extend our appreciation to the reviewers, whose comments helped us to significantly improve the content.

Compliance with Ethical Standards

Competing Interests

The authors declare that they have no competing interests.

References

  1. 1.
    Raghavan, K.G.: An experimental investigation into the effect of NO2 and temperature on the passive oxidation and active regeneration of particulate matter in a diesel particulate filter, Master’s Thesis, Michigan Technological University (2015)Google Scholar
  2. 2.
    Mahadevan, B.S., Johnson, J.H., Shahbakhti, M.: Development of a catalyzed diesel particulate filter multi-zone model for simulation of axial and radial substrate temperature and particulate matter distribution. Journal of Emiss. Control Sci. Technol. 1, 183–202 (2015). doi: 10.1007/s40825-015-0015-x
  3. 3.
    Mahadevan, B.S., Johnson, J.H., Shahbakhti, M.: Experimental and simulation analysis of temperature and particulate matter distribution for a catalyzed diesel particulate filter. Journal of Emiss. Control. Sci. Technol. 1, 255–283 (2015). doi: 10.1007/s40825-015-0022-y
  4. 4.
    Konstandopoulos, A., Johnson, J.: Wall-flow diesel particulate filters—their pressure drop and collection efficiency, SAE Technical Paper 890405, (1989). doi: 10.4271/890405
  5. 5.
    Shiel, K.L., Naber, J., Johnson, J.H., Hutton, C.R.: Catalyzed particulate filter passive oxidation study with ULSD and biodiesel blended fuel, SAE Technical Paper No. 2012–01-0837 (2012). doi: 10.4271/2012-01-0837
  6. 6.
    Pidgeon, J., Naber, D N., Johnson, J.H.: An engine experimental investigation into the effects of biodiesel blends on particulate matter oxidation in a catalyzed particulate filter during active regeneration, SAE Technical Paper No. 2013-01-0521, (2013)Google Scholar
  7. 7.
    Shiel, K.L.: Study of the effect of biodiesel fuel on passive oxidation in a catalyzed filter. Master’s Thesis, Michigan Technological University (2012)Google Scholar
  8. 8.
    Pidgeon, J.: An experimental investigation into the effect of biodiesel blends on particulate matter oxidation in a catalyzed particulate filter during active regeneration. Master’s Thesis, Michigan Technological University (2013)Google Scholar
  9. 9.
    Kladopoulou, E.V., Yang, S.L., Johnson, H.J., Parker, G.G.: A study describing the performance of diesel particulate filters during loading and regeneration—a lumped parameter model for control applications, SAE Technical Paper No. 2003-01-0842, (2003)Google Scholar
  10. 10.
    Haralampous, O.A., Kandylas, I.P., Koltasakis, G.C., Samaras, Z.C.: Diesel particulate filter pressure drop. Part 1: modeling and experimental validation. Int. J.Engine Res. 5(2), 149–162 (2004)CrossRefGoogle Scholar
  11. 11.
    Haralampous, O.A., Kandylas, I.P., Koltasakis, G.C., Samaras, Z.C.: Diesel particulate filter pressure drop. Part 2: on-board calculation of soot loading. Int.J.Engine Res. 5(2), 163–173 (2004)CrossRefGoogle Scholar
  12. 12.
    Premchand, K.C.: Development of a 1-D catalyzed diesel particulate filter for simulation of the performance and the oxidation of particulate matter and nitrogen oxides using passive oxidation and active regeneration engine experimental data, PhD Dissertation, Michigan Technological University, (2013)Google Scholar
  13. 13.
    Premchand, K.C., Surenahalli, H., Johnson, J.: Particulate matter and nitrogen oxides kinetics based on engine experimental data for a catalyzed diesel particulate filter, SAE Technical Paper No. 2014-01-1553 (2014). doi: 10.4271/2014-01-1553
  14. 14.
    Pulkrabek, W.W., Ibele, W.E.: The effect of temperature on the permeability of the porous material. Int. J. Heat and Mass Transfer. 30(6), 1103–1109 (1987)CrossRefGoogle Scholar
  15. 15.
    Versaevel, P., Colas, H., Rigaudeau, C., Noirot, R., Koltsakis, G.C., Stamatelos, A.M.: Some empirical observations on diesel particulate filter modeling and comparison between simulation and experiments. SAE Paper No. 2000-01-0477, (2000)Google Scholar
  16. 16.
    Konstandopoulos, A.G., Kostoglou, M., Vlachos, N., Kladopoulou, E.: Advances in the science and technology of diesel particulate filter simulation. Adv. Chem. Eng. 33, 213–275 (2007)CrossRefGoogle Scholar
  17. 17.
    Daido, S., Takagi, N.: Visualization of the PM deposition and oxidation behavior inside the DPF wall, SAE Technical Paper No. 2009-01-1473 (2009)Google Scholar
  18. 18.
    Choi, S., Lee, K.: Detailed investigation of soot deposition and oxidation characteristics in a diesel particulate filter using optical visualization, SAE Technical Paper No. 2013-01-0528 (2013). doi:  10.4271/2013-01-0528
  19. 19.
    Shadman, F.: Kinetics of soot combustion during regeneration of surface filters. Comb Sci and Technol. 63, 183–191 (1989)CrossRefGoogle Scholar
  20. 20.
    Kostoglou, M., Konstandopoulos, A.G.: Effect of soot layer microstructure on diesel particulate filter regeneration. AICHE J. 51(9), 2534–2546 (2005)CrossRefGoogle Scholar
  21. 21.
    Picandet, V., Khelidj, A., Bastial, G.: Effect of axial compressive damage on gas permeability of ordinary and high-performance concrete. Cem. Concr. Res. 31(2201), 1525–1532 (2001)CrossRefGoogle Scholar
  22. 22.
    Konstandopoulos, A., Kostoglou, M., Skaperdas, E., Papaioannou, E., Zarvalis, D., and Kladopoulou, E.: Fundamental studies of diesel particulate filters: transient loading, regeneration and aging, SAE Technical Paper 2000-01-1016 (2000). doi: 10.4271/2000-01-1016.
  23. 23.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)CrossRefGoogle Scholar
  24. 24.
    Depcik, C.: Combining the classical and lumped diesel particulate filter models. SAE Int. J. Engines. 8(3), (2015). doi: 10.4271/2015-01-1049

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Boopathi S. Mahadevan
    • 1
  • John H. Johnson
    • 1
  • Mahdi Shahbakhti
    • 1
  1. 1.Michigan Technological UniversityHoughtonUSA

Personalised recommendations