An experimental study of the role of biodiesel on the performance of diesel particulate filters

© 2019 Elsevier Ltd The study investigates the impact of the physical properties of biodiesel particulate matter on the performance of diesel particulate filters (DPF). Filtration efficiency (FE) and pressure drop (PD), as a function of loading time, were studied on a DPF for a range of biodiesel fuels 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 filtration stage, 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 filtration in tested conditions. Further we found that FE for biodiesel particles 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 biodiesel fraction 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 soot as compared to diesel soot, with biodiesel soot causing higher PD for the same mass of the soot loaded on the DPF. This effect was attributed to the smaller primary particulate size of the biodiesel particles. The results presented in this study will further facilitate understanding of the filtration processes of particulate matter and validate detailed filtration models for the prediction of the filtration efficiency (FE) and pressure drop (PD) depending on the particle morphological properties.