A New Method for Optimizing the Jet-Cleaning Performance of Self-Cleaning Screen Filters: The 3D CFD-ANN-GA Framework

The jet-type self-cleaning screen filter integrates industrial jet-cleaning technology into the self-cleaning process of screen filters in the drip irrigation system, which has the advantages of low water consumption, high cleaning capacity, and wide applicability compared to traditional filters. However, its commercialization faces challenges as the optimal jet cleaning mode and optimization method have not been determined. This study proposes a framework that combines computational fluid dynamics (CFD), artificial neural networks (ANN), and genetic algorithms (GA) for optimizing jet-cleaning parameters to improve the performance. The results show that, among the main influencing parameters of the nozzle, the incident section diameter d and the V-groove half angle β have the most significant effects on the peak wall shear stress, action area, and water consumption for cleaning. The ANN has a higher accuracy in predicting the performance (R2 = 0.9991, MAE = 9.477), and it can effectively replace the CFD model for predicting the jet-cleaning performance and optimizing the parameters. The optimization resulted in a 1.34% reduction in the peak wall shear stress, a 16.82% reduction in cleaning water consumption, and a 7.6% increase in the action area for the optimal model compared to the base model. The optimization framework combining CFD, ANN, and GA can provide an optimal cleaning parameter scheme for jet-type self-cleaning screen filters.