This study describes a self-developed intelligently controlled drum–axial airflow coupled sorting device. Using the sorting rate as the evaluation index, single-factor tests were conducted to analyze the influence patterns of axial airflow startup time, drum rotational speed, and axial airflow velocity on the sorting effect. The Box-Behnken response surface design method was adopted to construct a quadratic regression model for the sorting rate, analyze the interaction between factors, and conduct parameter optimization and verification. All three test factors had an extremely significant effect on the sorting rate. Among them, the quadratic effect of axial airflow velocity was the strongest, and parameter fluctuations had the highest sensitivity to the sorting results. Significant coupling interaction between axial airflow startup time and drum rotational speed was observed. Through global optimization, the following optimal process parameter combination was obtained: axial airflow startup time, 6.5 s; drum rotational speed, 16 rpm; and axial airflow velocity, 7.5 m/s. Under these conditions, the measured average sorting rate reached 80.46%, with a relative error of only 1.03% compared with the model predicted value, indicating good model prediction accuracy. These results can provide a theoretical basis and technical support for the research and development of intelligent sorting equipment for multiscale fresh tea leaves and optimization of process parameters.
To address the problems of low sorting accuracy and poor operation stability caused by physical leaf entanglement in traditional drum screening of fresh tea leaves, a multiscale fresh tea leaf sorting system with drum-axial airflow coupling based on intelligent control was designed. The axial moving distances of fresh tea leaves of different scales at wind speeds of 5, 7, and 9 m/s were calibrated through bench tests, and the optimal wind speed parameter for secondary fine screening was determined. A numerical model of the axial airflow field inside the drum was established via Fluent software, and a coupled sorting test platform was built to compare the sorting performance of the traditional pure drum screening mode and that of the coupled intelligent sorting mode. The results showed that 7 m/s is the optimal axial airflow velocity for secondary fine screening of multiscale fresh tea leaves during this test, which can realize effective back-blowing of small-scale materials and accurate screening of large-scale materials. At this velocity, the flow field is evenly distributed, and the effective thrust area highly matched the sorting demand. The average sorting efficiency of the coupled intelligent sorting mode reached 84.8%, which is 25.6% higher than that of traditional pure drum screening, with favorable sorting accuracy and operation stability. These findings can provide a theoretical basis and technical reference for the optimization, upgrading, and intelligent transformation of high-efficiency fresh tea leaf sorting equipment.