Research on Screening Rate of Fresh Tea Leaves Classifier Based on EDEM

LI Bing; LI Weining; BAI Xuanbing; ZHANG Zhengzhu

doi:10.13305/j.cnki.jts.2019.04.013

PDF(843 KB)

Journal of Tea Science ›› 2019, Vol. 39 ›› Issue (4) : 484-494. DOI: 10.13305/j.cnki.jts.2019.04.013

Research on Screening Rate of Fresh Tea Leaves Classifier Based on EDEM

LI Bing^1,2, LI Weining¹, BAI Xuanbing¹, ZHANG Zhengzhu^2,3,*

Author information +

History +

Abstract

In order to improve the screening rate of fresh tea leaves classifier, the key technical parameters of 6CFJ-70 fresh tea leaves classifier were studied with machine picking fresh leaves of willow-Leaf tea plant. Solidworks 2014 was used to build the 3D model of fresh tea leaves classifier. Based on discrete element method, the simulation granular model and contact mechanics model of fresh tea leaves were established, and the key simulation technical parameters were set up. EDEM 2018 software was used to simulate the conical drum of fresh tea leaves. The movement of the conical drum was simulated numerically, and the key parameters affecting the screening rate were the rotational speed and inclination angle of the conical drum. In order to optimize the above parameters, a quadratic rotation orthogonal combination experiment with 2 factors and 5 levels was designed with screening rate as objective function. The quadratic regression model was obtained by response surface method and the related validation tests were carried out. The results show that the main factors affecting the classification efficiency of fresh leaves are the rotational speed of conical drum and the inclination of conical drum in turn. When the rotating speed of conical drum is 24 r·min^-1 and the inclination angle of conical drum is 6 degrees, the screening rate of fresh leaves is 81.7%, which has a good effect of fresh leaves classification. The research content of this paper can provide technical reference for the design and optimization of fresh tea leaves classifier.

Key words

discrete element method / EDEM / response surface analysis / fresh tea leaves classifier

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LI Bing, LI Weining, BAI Xuanbing, ZHANG Zhengzhu. Research on Screening Rate of Fresh Tea Leaves Classifier Based on EDEM[J]. Journal of Tea Science. 2019, 39(4): 484-494 https://doi.org/10.13305/j.cnki.jts.2019.04.013

References

[1] 杨娟, 李中林, 袁林颖, 等. 机采茶鲜叶分级技术初步研究[J]. 中国茶叶加工, 2015(2): 41-45.
[2] 唐小林, 李文萃, 范起业. 机采茶鲜叶分类分级技术及相关设备研究进展[J]. 中国茶叶加工, 2015(2): 5-8.
[3] 张兰兰, 董迹芬, 唐萌, 等. 名优茶机采鲜叶分级技术研究[J]. 浙江大学学报(农业与生命科学版), 2012, 38(5): 593-598.
[4] 孙六莲, 严跃滨, 唐焕华. 茶叶鲜叶分级机主要参数研究[J]. 农业与技术, 2016, 36(1): 38-39.
[5] 袁海波, 滑金杰, 邓余良, 等. 基于YJY-2型鲜叶分级机的机采茶叶分级分类工艺优化[J]. 农业工程学报, 2016, 32(6): 276-282.
[6] 于建群, 付宏, 李红, 等. 离散元法及其在农业机械工作部件研究与设计中的应用[J]. 农业工程学报, 2005(5): 1-6.
[7] 廖庆喜, 张朋玲, 廖宜涛, 等. 基于EDEM的离心式排种器排种性能数值模拟[J]. 农业机械学报, 2014, 45(2): 109-114.
[8] 汪晓华, 李文昊, 何磊, 等. 平面圆筛机筛分参数对筛分效率的影响[J]. 轻工机械, 2013, 31(3): 8-12.
[9] 李志杰, 王素芬, 黄剑虹, 等. 基于EDEM绿茶滚筒杀青机温度场研究[J]. 安徽农业科学, 2016, 44(23): 235-237.
[10] 金石坚. 正确选择圆筒旋转筛转速的探讨[J]. 粮食与饲料工业, 1999(11): 7-9.
[11] 安徽农学院. 制茶学[M]. 合肥: 中国农业出版社, 1989: 25-26.
[12] 李洪昌, 李耀明, 唐忠, 等. 基于EDEM的振动筛分数值模拟与分析[J]. 农业工程学报, 2011, 27(5): 117-121.
[13] 原建博, 李骅, 吴崇友, 等. 基于离散单元法的水稻籽粒快速颗粒建模研究[J]. 南京农业大学学报, 2018, 41(6): 1151-1158.
[14] 高国华, 谢海峰. 基于EDEM的薯土分离机构数值分析与模拟[J]. 农机化研究, 2019, 41(1): 15-21.
[15] 鹿芳媛, 马旭, 齐龙, 等. 基于离散元法的杂交稻振动匀种装置参数优化与试验[J]. 农业工程学报, 2016, 32(10): 17-25.
[16] 许健, 甘义权, 蔡宗寿, 等. 基于EDEM的倾斜圆盘勺式大豆排种器投种性能优化研究[J]. 安徽农业科学, 2018, 46(21): 193-196.
[17] 朱志楠, 赵章风, 钟江, 等. 基于多相流耦合过程数值模拟的茶鲜叶离心式连续脱水设备参数模拟与优化[J]. 茶叶科学, 2017, 37(3): 280-289.
[18] J.-F.Ferellec, G.R.McDowell. A simple method to create complex particle shapes for DEM[J]. Geomechanics and Geoengineering, 2008, 3(3): 211-216.
[19] 施昱, 王庆海, 叶伟. 基于EDEM软件数值模拟的滚筒筛优化设计[J]. 环境工程学报, 2016, 10(9): 5197-5202.
[20] 丁力, 杨丽, 刘守荣, 等. 辅助充种种盘玉米气吸式高速精量排种器设计[J]. 农业工程学报, 2018, 34(22): 1-11.
[21] 耿端阳, 张明源, 何珂, 等. 倾斜双圆环型孔圆盘式玉米排种器设计与试验[J]. 农业机械学报, 2018, 49(1): 68-76.
[22] 朱德泉, 李兰兰, 文世昌, 等. 滑片型孔轮式水稻精量排种器排种性能数值模拟与试验[J]. 农业工程学报, 2018, 34(21): 17-26.