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

Abstract

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.