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Journal of Chinese Agricultural Mechanization

Journal of Chinese Agricultural Mechanization ›› 2022, Vol. 43 ›› Issue (11): 75-80.DOI: 10.13733/j.jcam.issn.2095-5553.2022.11.012

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Study on BPNN prediction of variable amplitude antiblocking screening performance: Based on EDEM-RecurDyn simulation 

Zhu Yongle, Ma Zheng, Chen Shuren, Souleymane Nfamoussa Traore, Li Yaoming, Jiang Sheng.   

  • Online:2022-11-15 Published:2022-10-24

BP神经网络预测变振幅防堵筛分性能研究——基于EDEM-RecurDyn仿真

朱永乐1, 2,马征1, 2,陈树人1, 2,Souleymane Nfamoussa Traore1, 2,


李耀明1, 2,姜晟1, 2
  

  1. 1. 江苏大学现代农业装备与技术教育部重点实验室,江苏镇江,212013;

    2. 江苏大学农业工程学院,江苏镇江,212013
  • 基金资助:
    国家自然科学基金项目(51975256);江苏省现代农机装备与技术示范推广项目(NJ2021—07);江苏高校优势学科建设工程(PAPD)

Abstract:

Aiming at the problem that the vibrating screen surface is blocked due to the change of material feeding amount, which affects the screening performance, the angle of the guide chute for the variable amplitude screening mechanism as the variable, the variable amplitude antiblocking screening process under abnormal feeding of rice grains and stems (0.5 kg/s as an example) was simulated by RecurDynEDEM. BPNN was used to accurately predict the variable amplitude screening efficiency and impurity content for different chute angles, and optimized the parameters for the variable amplitude screen, to alleviate the accumulation and blockage on the screen surface and improve the screening performance. The results showed that under the abnormal feeding (0.5 kg/s), the screening efficiency increased steadily within 55% - 58% when the guide chute angle was 0°-10°, the impurity content fluctuated in a small range within 0.1%-0.16%, the screening efficiency increased linearly within 58%-75% when the chute angle was 15°-40°, the impurity content fluctuated and increased within 0.11%-0.19%, and the screening efficiency suddenly decreased linearly to 658% when the chute angle was 40°-45°, the impurity content increased sharply to 0.37%. BP neural network predicted that the variable amplitude screening efficiency was the highest, the impurity content was low, and the variable amplitude antiblocking screening performance was better, when the chute angle was 37°. The screening efficiency and impurity content were 74.30% and 020%. The R2 of BP neural network prediction model was 0999, the prediction error was concentrated at -0.000 41, and the prediction curves were highly fitted, which verified the reliability and accuracy. This provides a basis for the intelligent adjustment of variable amplitude antiblocking screening in the future.

Key words: variable amplitude, EDEM-RecurDyn, BP neural network, screening efficiency, impurity content

摘要: 针对物料喂入量变化导致振动筛面堆积堵塞,影响筛分性能的问题。以变振幅筛分机构导向滑槽转角为变量,RecurDyn和EDEM联合仿真水稻籽粒、茎秆非正常喂入(0.5 kg/s)条件下变振幅防堵筛分过程。BP神经网络预测不同滑槽转角下变振幅的筛分效率、含杂率,优化变振幅调节,缓解筛面堆积堵塞,改善筛分性能。结果表明物料非正常喂入(0.5 kg/s)下导向滑槽转角0°~10°时筛分效率在55%~58%内平稳上升,含杂率在0.1%~0.16%内小范围波动变化;滑槽转角15°~40°时筛分效率在58%~75%内线性增大,含杂率在0.11%~0.19%内波动上升;滑槽转角40°~45°时筛分效率骤然线性下降至65.8%,含杂率陡然升高至0.37%。BP神经网络预测滑槽转角在37°时变振幅筛分效率最高、含杂率较低,变振幅防堵筛分性能更好,此时筛分效率和含杂率为74.30%、0.20%。BP神经网络预测模型R2为0.999,预测误差集中在-0.000 41,预测曲线均高度拟合。验证了BP神经网络预测模型的可靠性和精确度。这为未来变振幅防堵筛分的智能化调控提供了依据。


关键词: 变振幅, EDEM-RecurDyn, BP神经网络, 筛分效率, 含杂率

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