Design and test of mouse cage fresh wolfberry classifier based on flotation method

ObjectiveImprove the rapid grading technology of fresh goji berries.MethodsUtilizing the physical properties of fresh goji berries with a density lower than water, a fresh goji berry classifier was developed using flotation principles. The theoretical structural parameters of the classifier were calculated and specified through mechanical analysis and experimental measurements. Based on ANSYS Fluent simulation of the grading process, analyzed the simulation results, and verified and accurately determined the theoretical structural parameters of the classifier.ResultsThe specific parameters of the classifier were: cage diameter 0.34 m, length 2.1 m, roller bar diameter 10mm, cage tilt angle 17°, water flow rate 1.7×10-2 m3/s, water flow rate 3.5 m/s, cage speed 12 r/min. Using parallel and batch testing methods, the production efficiency and capacity of the prototype were verified. The parallel test results showed that the average classification accuracy of fresh goji berries was about 93.79%, and the non-destructive rate of fresh goji berries was 98.21%. Batch experiments have shown that the average production capacity of fresh goji berries was 1 020.6 kg/h, and the average classification accuracy was 92.85%.ConclusionThe squirrel cage fresh goji berry classifier developed in this article based on flotation method can achieve low loss, fast, and efficient classification of fresh goji berries.