Endpoint forecast of different diesel-biodiesel soot filtration process in diesel particulate filters considering ash deposition

Abstract

In order to effectively forecast endpoint of soot loading process in the diesel particulate filter (DPF), an efficient prediction method is presented in this work. Firstly, ash deposition mass is predicted by the fuzzy adaptive variable weight functional link neural network model. Then, pressure drop of the DPF is simulated by a modified soot filtration mathematical model considering ash deposition. Finally, the soot loading endpoints with different fuels and initial ash mass are forecasted based on the cusp catastrophe model. The results show that the fuzzy adaptive variable weight functional link neural network prediction model has higher prediction accuracy with 2.24% average error than other single prediction methods. In addition, pressure drop variation rate of the DPF increases over time and it obviously rises with the increase of the pre-loaded ash mass, DPF with larger initial ash mass has a shorter soot loading time to reach the same pressure drop, and soot mass decreases with the rise of biodiesel proportion in the fuels at the same moment. Moreover, predicted pressure drop and discriminant value Δ indicate that a DPF with elevated ash loads has shorter soot loading time and lower soot mass, biodiesel or its blends can prolong the soot filtration time and the optimal range of endpoint time during B0 soot loading process is between 4.25 h and 4.5 h for a clean DPF. This work offers us great reference value for forecasting soot loading endpoint and managing regeneration of the periodic regenerated particulate filters.