Novel soot loading prediction model of diesel particulate filter based on collection mechanism and equivalent permeability

• Published in: Fuel (Guildford) 2021-02, Vol.286, p.119409, Article 119409
• Main Authors: Wang, De-yuan, Tan, Pi-qiang, Zhu, Lei, Wang, Yin-huan, Hu, Zhi-yuan, Lou, Di-ming
• Format: Article
• Language: English
• Subjects: Accuracy; Active control; Ash; Ashes; Cell density; Collection; Collection mechanism; Diameters; Diesel; Diesel engine; Diesel engines; DPF; Durability; Equipment costs; Equivalence; Exhaust systems; Filter cake; Fluid filters; Mass flow; Mathematical models; Multilayers; Packed beds; Parameters; Permeability; Prediction; Prediction models; Pressure drop; Regeneration; Soot; Soot loading; Wall thickness
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2020.119409

[Display omitted] •Novel soot loading prediction model is proposed.•Average prediction error is 2.72%.•Model considers both deep bed and cake collection stage.•Model can provide important guidance for accurate regeneration triggering.•Model is based on collection mechanism and equivalent permeability. Accurate estimation of soot loading in DPF is an important basis for the efficient application of diesel exhaust aftertreatment system. However, some prediction methods need to be equipped with additional equipment, which increases the cost and does not significantly improve the accuracy of soot loading prediction. Moreover, the previous soot loading prediction models rarely consider the ash deposition and particle collection rules from the perspective of collection mechanism. In this study, a novel soot loading prediction model based on collection mechanism and multilayer equivalent permeability considering the deep bed and filter cake collection stage is proposed. The prediction model is based on theory of packed beds of spherical particles. The wall and cake are divided into four different layers: particulate layer, ash layer, wall layer, and clean wall layer, respectively. Finally, the overall soot loading equation is established and the numerical solution is solved by Taylor’s formula. The model is verified by bench test, which can ensure high prediction accuracy without increasing the cost of regeneration. The maximum prediction error is less than 5%, and the average error is 2.72%. Based on the model, effects of the key structural parameters such as DPF diameter, length, cell density and wall thickness, as well as the engine operating parameters such as exhaust mass flow and temperature on the pressure drop and soot loading characteristics were further researched. The research results can provide important guidance for accurate triggering of regeneration, formulation of active regeneration control strategy and improvement of DPF durability.