A review of regeneration mechanism and methods for reducing soot emissions from diesel particulate filter in diesel engine

With the global emphasis on environmental protection and the proposal of the climate goal of "carbon neutrality," countries around the world are calling for reductions in carbon dioxide, nitrogen oxide, and particulate matter pollution. These pollutants have severe impacts on human lives and should be effectively controlled. Engine exhaust is the most serious pollution source, and diesel engine is an important contributor to particulate matter. Diesel particulate filter (DPF) technology has proven to be an effective technology for soot control at the present and in the future. Firstly, the exacerbating effect of particulate matter on human infectious disease viruses is discussed. Then, the latest developments in the influence of key factors on DPF performance are reviewed at different observation scales (wall, channel, and entire filter). In addition, current soot catalytic oxidant schemes are presented in the review, and the significance of catalyst activity and soot oxidation kinetic models are highlighted. Finally, the areas that need further research are determined, which has important guiding significance for future research. Current catalytic technologies are focused on stable materials with high mobility of oxidizing substances and low cost. The challenge of DPF optimization design is to accurately calculate the balance between soot and ash load, DPF regeneration control strategy, and exhaust heat management strategy.

Impact of Particulate Size During Deep Loading on DPF Management

Wall-flow particulate filters are a required emission control device to abate diesel emission in order to comply with current regulations. DPFs (diesel particulate filters) are characterized by high filtration efficiency—but in order to avoid deterioration of power and performance, they are required to cause low values of backpressure. The periodical oxidation of collected particle allows for the reestablishment of the ideal flow conditions. Studies highlighted that the regeneration event has an important impact on engine emission, since it is responsible for the emission of a large number of smaller particles. From these considerations, the importance of optimizing the DPF management for what concerns both filtration and regeneration mechanisms arises. The present paper focuses on the loading process of the filter. A filtration model was implemented, based on the ‘unit-collector’ and fluid-dynamic approaches, known as valid modelling techniques. The model was used to predict trapped mass and filter backpressure evolution with time during loading processes, in which soot particle sizes varied, with the aim to analyze how particulate size affects the filter pressure drop rise. A wall-flow filter was investigated, and the behavior of clean material was evaluated by a parametric analysis in which particle diameter varies in the filed 20–1000 nm, that is the typical range of soot sizes in diesel engine exhaust. The results demonstrate that soot size has a great influence on the initial deep bed loading process. Moreover, it defines the value from which the linear pressure drop shape during cake filtration starts, not only when the initial loaded is completed, but also each time the regeneration event is concluded. This outcome provides an important guideline to define the most appropriate strategy for the initial DPF loading in order to establish the regeneration event based on the estimation of trapped mass accounting for the filter backpressure and on the time interval between two successive regeneration.

Dynamic Heterogeneous Multiscale Filtration Model: Probing Micro- and Macroscopic Filtration Characteristics of Gasoline Particulate Filters

Motivated by high filtration efficiency (mass- and number-based) and low pressure drop requirements for gasoline particulate filters (GPFs), a previously developed heterogeneous multiscale filtration (HMF) model is extended to simulate dynamic filtration characteristics of GPFs. This dynamic HMF model is based on a probability density function (PDF) description of the pore size distribution and classical filtration theory. The microstructure of the porous substrate in a GPF is resolved and included in the model. Fundamental particulate filtration experiments were conducted using an exhaust filtration analysis (EFA) system for model validation. The particulate in the filtration experiments was sampled from a spark-ignition direct-injection (SIDI) gasoline engine. With the dynamic HMF model, evolution of the microscopic characteristics of the substrate (pore size distribution, porosity, permeability, and deposited particulate inside the porous substrate) during filtration can be probed. Also, predicted macroscopic filtration characteristics including particle number concentration and normalized pressure drop show good agreement with the experimental data. The resulting dynamic HMF model can be used to study the dynamic particulate filtration process in GPFs with distinct microstructures, serving as a powerful tool for GPF design and optimization.