Parameter optimization of a pure electric sweeper dust port by a backpropagation neural network combined with a whale algorithm

Abstract. Optimizing the structure of the suction port is the key to effectively improving the performance of the sweeping vehicle. The CFD (computational fluid dynamics) method and gas–solid two-phase flow model are used to analyse the influence rule of the structural parameters and the height above ground on the cleaning effect, which is verified by real vehicle tests. The data set was established by an orthogonal test method, and a BP (backpropagation) neural network was used to fit the structural parameters and evaluation indexes. The fitting errors were all within 5 %, indicating that the fitting results of this method were good. According to the fitting relation of the BP neural network output, the whale algorithm should be further used to solve the optimal structural parameters. The results show that the optimal parameter combination is β=63∘, d=168 mm and h=12 mm. The energy consumption of the optimized model is reduced, and the internal airflow loss is reduced. The particle residence time becomes shorter, and the particle can flow out from the outlet faster, thus improving the dust absorption effect. The research can provide a theoretical reference for performance optimization and parameter matching of sweepers.