基于气流辅助的颗粒肥流量光电检测系统设计与试验OA

During the flow of granular fertilizers,they tended to obstruct each other,and the external wheel-type fertilizer distributor further caused uneven distribution,making it challenging to accurately measure the fertilizer flow.To address this issue,an airflow-assisted photoelectric detection method that enabled more accurate measurement of granular fertilizer flow was proposed.An airflow-assisted photoelectric granular fertilizer flow sensor was designed,utilizing positive pressure airflow to assist in the reconstruction of the fertilizer flow pattern.A detection model was developed by integrating simulation and static experiments.The model incorporated fertilizer flow volume as an intermediate variable and calculated the flow rate through an area element integration algorithm.Using diammonium phosphate as the test material,static experiments revealed a significant linear relationship between the equivalent diameter of the fertilizer flow and the sensor's response voltage.The correction coefficient's multiple regression model was determined through calibration experiments,thus constructing the fertilizer flow detection model.To validate the effectiveness of the method,a dedicated test platform was set up for verification experiments.The test results showed that under airflow-assisted conditions,the U-V-Q detection model achieved an average absolute percentage error(MAPE)of no more than 4.55％,and a root mean square error(RMSE)of no more than 3.82 g/s.This demonstrated that the airflow-assisted photoelectric detection method for granular fertilizer flow had high detection accuracy and good stability.Compared with the U-V-Q detection model without the airflow assistance device,the MAPE was reduced by 2.44％,and the MSD was reduced by 0.74％.Furthermore,with the airflow assistance device,the MAPE of the U-V-Q detection model was 9.03％lower than that of the U-Q detection model.The research results indicated that using airflow assistance alone or the U-V-Q detection model alone did not achieve optimal detection performance.However,combining airflow assistance technology with the U-V-Q detection model significantly improved both the accuracy and stability of granular fertilizer flow detection,providing insights for real-time flow detection in fertilization machines,which was of great significance for the closed-loop control of precision variable-rate fertilization systems.