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Modelling particle mass and particle number emissions during the active regeneration of diesel particulate filters

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Lao, CT 
Akroyd, J 
Eaves, N 
Smith, A 
Morgan, N 


A new model has been developed to describe the size-dependent effects that are responsible for transient particle mass (PM) and particle number (PN) emissions observed during experiments of the active regeneration of Diesel Particulate Filters (DPFs). The model uses a population balance approach to describe the size of the particles entering and leaving the DPF, and accumulated within it. The population balance is coupled to a unit collector model that describes the filtration of the particles in the porous walls of the DPF and a reactor network model that is used to describe the geometry of the DPF. Two versions of the unit collector model were investigated. The original version, based on current literature, and an extended version, developed in this work, that includes terms to describe both the non-uniform regeneration of the cake and thermal expansion of the pores in the DPF. Simulations using the original unit collector model were able to provide a good description of the pressure drop and PM filtration efficiency during the loading of the DPF, but were unable to adequately describe the change in filtration efficiency during regeneration of the DPF. The introduction of the extended unit collector description enabled the model to describe both the timing of particle breakthrough and the final steady filtration efficiency of the hot regenerated DPF. Further work is required to understand better the transient behaviour of the system. In particular, we stress the importance that future experiments fully characterise the particle size distribution at both the inlet and outlet of the DPF.



Diesel Particulate Filter (DPF), Regeneration, Population balance model, Unit collector, Emissions

Journal Title

Proceedings of the Combustion Institute

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Elsevier BV
Engineering and Physical Sciences Research Council (1622599)
European Commission Horizon 2020 (H2020) Societal Challenges (724145)
Cambridge Centre for Advanced Research and Education in Singapore (CARES), Royal Dutch Shell. Note: need to add PEMS4NANO (H2020) and EPSRC.
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