Prediction of Particle-Collection Efficiency for Vacuum-Blowing Cleaning System Based on Operational Conditions

Abstract

The dust-collection system, as the core of a sweeper vehicle, directly inhales dust particles on the pavement. The influence of variable operational conditions on particle-separation performance was investigated using computational fluid dynamics (CFD) Euler–Lagrange multiphase model. The particle-separation performance efficiency and retention time were used to evaluate the dust-collection efficiency. The uniform design (UD) and multiple regression analysis (MRA) methods were employed to predict and optimize the effects of reverse-blowing flow rate, pressure drop, and traveling speed on separation efficiency. The results indicated that the dust-collection performance initially increased and then decreased with increasing reverse-blowing flow rate. As the pressure drop increased, there was an increase in total dust-collection efficiency. However, the efficiency decreased with increasing traveling speed. The regression model showed that the proposed approach was able to predict the particle collection efficiency accurately. In addition, the optimum operational conditions were obtained, namely a reverse-blowing flow rate of 2100 m3/h, a traveling speed of 5 km/h, and a pressure drop of 2400 Pa. The maximum particle-separation efficiency was 99.10%, which showed good agreement with the experimental results.