Modeling and interpreting the observed effects of ash on diesel particulate filter performance and regeneration

Abstract

Diesel particulate filters (DPF) are devices that physically capture diesel particulates to prevent their release to the atmosphere. Diesel particulate filters have seen widespread use in on- and off-road applications as an effective means for meeting increasingly stringent particle emissions regulations. Although the soot deposit can be removed by regeneration, the incombustible material - ash, primarily derived from metallic additives in the engine lubricant, accumulates in the DPF channels with the increasing vehicle mileage or equivalent running hours. Ash accumulation inside filter increases the flow restriction and reduces the filter soot storage capacity, which results in higher filter regeneration frequencies and larger engine fuel penalty. Combined with experimental observations, DPF models are built to investigate the fundamental mechanisms of DPF aging process. The DPF soot and ash loading model, based on porous media filtration theory, is applied to understand the soot deposition across the substrate wall with soot and ash cake layer formation. DPF models are also used to investigate the process of ash transport and catalyst deactivation with increasing ash load level. DPF ash aging is found to have negative effect on passive regeneration due to the catalyst deactivation and diffusion resistance of ash cake layer. Besides, at given amount of ash load, the effects of ash spatial distribution on DPF performance are studied via simulation. It is found that the ash end plug has significant influences on DPF pressure drop while ash radial and axial distributions have minor effects. At known ash and substrate property, DPF performance can be optimized according the sensitivity map developed from this study. DPF model is beneficial to interpret the experimental observations and it is applied to predict the effects of certain factors, like flow rate and deposit level, on DPF performance. At the same time, modeling results are useful in optimizing the design of the combined engine-aftertreatment-lubricant system for future diesel engines and in understanding the requirements for robust aftertreatment systems.