An experimental study of the role of biodiesel on the performance of diesel particulate filters
2019
The study investigates the impact of the physical properties of
biodieselparticulate matter on the performance of
diesel particulate filters(DPF).
Filtrationefficiency (FE) and pressure drop (PD), as a function of loading time, were studied on a DPF for a range of
biodieselfuels with varying fuel molecular oxygen content from 0% (diesel) to 14%. The change in the oxygen content of the fuel resulted in diesel particle matter (DPM) with significantly different physical properties. FE and PD were investigated during the deep bed
filtrationstage, chosen because it presents the start of the loading process, which is a crucial step for high performance
filtration. Firstly, we investigated the influence of the size distribution of various particles on the deep bed
filtration, wherein size distributions of PM were measured before and after the DPF. The results show that for all fuels the FE is higher for smaller particles, as diffusion is the dominant process governing the
filtrationin tested conditions. Further we found that FE for
biodieselparticles were up to 10% lower than for diesel particles at the beginning of the loading process, but with that difference diminishing as the filter fully loads. This result is attributed to the increase in the particulate fractal dimension with a higher
biodieselfraction resulting in more compact particles with lower diffusion coefficients. In addition, the study also demonstrated that the change of FE during the loading process is dependent on the physical properties of DPM. DPF performs differently for
biodiesel
sootas compared to diesel
soot, with
biodiesel
sootcausing higher PD for the same mass of the
sootloaded on the DPF. This effect was attributed to the smaller primary particulate size of the
biodieselparticles. The results presented in this study will further facilitate understanding of the
filtrationprocesses of particulate matter and validate detailed
filtrationmodels for the prediction of the
filtrationefficiency (FE) and pressure drop (PD) depending on the particle morphological properties.
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