BP neural network model for material distribution prediction based on variable amplitude anti-blocking screening DEM simulations  

作　　者：Zheng Ma Yongle Zhu Zhiping Wu Souleymane Nfamoussa Traore Du Chen Licheng Xing 

机构地区：[1]School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China [2]Key Laboratory of Modern Agricultural Equipment and Technology,Ministry of Education&Jiangsu Province,Jiangsu University,Zhenjiang 212013,Jiangsu,China [3]Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment,Jiangsu University,Zhenjiang 212013,Jiangsu,China [4]College of Engineering,China Agricultural University,Beijing 100083,China [5]Jiangsu World Agricultural Machinery Co.,Ltd.,Picheng Industrial Park,Danyang 212311,Jiangsu,China

出　　处：《International Journal of Agricultural and Biological Engineering》2023年第4期190-199,共10页国际农业与生物工程学报（英文）

基　　金：supported financially by National Natural Science Foundation of China(Grant No.51975256,52375249);Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment(Grant No.XTCX2011);Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project(Grant No.NJ2021-07);a project funded by the Priority Academic Program of the Development of Jiangsu Higher Education Institutions(PAPD).

摘　　要：The material feeding changing of combine harvester is easy to cause accumulation and blockage of the vibrating screen,which seriously affects the harvest operation.In order to alleviate such accumulation and blockages on the vibrating screen surface,the guide chute rotation angle of the improved variable amplitude screening mechanism was selected as the target variable,and EDEM-RecurDyn was employed to simulate the anti-blocking process of the variable amplitude under a changing feeding quantity(0.5 kg/s abnormal,0.2 kg/s normal)of materials(rice grain and stem mixture).A BP(an error back propagation algorithm)neural network was designed and the prediction model of the material distribution was subsequently constructed on the variable screening surface under different chute angles during abnormal feeding.The results revealed a continuous decrease in the quality and time of the material blockage at the front end of the screen surface with the increasing guide chute angle.At the guide chute angle of 20°-45°and adjustment time of 3-6 s,the blocked and accumulated materials at the front-end screen surface was be moved back to Grid 6 for screening.However,overtime,the screen surface materials continued to move back under the chute angle of 40°-45°,which had a great impact on the screening performance.At the guide chute angle of 30°-35°and adjustment time of 4 s,the materials on the screen surface were evenly distributed in Grid 1-6.This was able to alleviate the accumulation and blockage of the screen surface materials.The R of the material distribution prediction model(BP neural network)on the screen surface was determined as 0.97,indicating the high reliability and accuracy of the material distribution model on the screen surface based on the BP neural network.This work provides an important reference for the variable amplitude intelligent control of screen surface material anti-blocking.

关 键 词：variable amplitude material distribution EDEM-RecurDyn BP neural network 

分 类 号：S565.4[农业科学—作物学]