Academic Journal

Parameterization of Multi-Angle Shaker Based on PSO-BP Neural Network

التفاصيل البيبلوغرافية
العنوان: Parameterization of Multi-Angle Shaker Based on PSO-BP Neural Network
المؤلفون: Jinxia Zhang, Yan Wang, Fusheng Niu, Hongmei Zhang, Songyi Li, Yanpeng Wang
المصدر: Minerals; Volume 13; Issue 7; Pages: 929
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2023
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: vibrating screen, vibration parameters, discrete elements, screening efficiency, PSO-BP neural networks
جغرافية الموضوع: agris
الوصف: It was possible to conduct a study on the shape and parameterization of the vibrating screen so as to explore the relationship between detailed vibrating screen motion parameters and particle group distribution under different screen surface states. The motion characteristics of particle groups in various scenes were investigated, screening performance of vibrating screen with complex parameters was studied, interaction between motion parameters of screen surface and motion of material groups in multi-component mixed particle groups was analyzed, segregation distribution law of multi-component mixed material groups was revealed, and this study presents simulation findings based on the discrete element program EDEM. The ensemble learning approach was used to examine the optimized model screen. It was revealed that the screen’s amplitude, vibration frequency, vibration direction angle, swing frequency, swing angle, and change rate of screen surface inclination all had a major impact on its performance. As a result, the vibrating screen’s running state was described by various parameter combinations, and the trend changes of several factors that affected the performance of the screen were examined. The investigation revealed that the particle swarm optimization backpropagation (PSO-BP) neural network model outperformed the backpropagation (BP) neural network model alone in terms of prediction. It had lower root mean square error (RMSE), mean square relative error (MSRE), mean absolute error (MAE), and mean absolute relative error (MARE) than the BP neural network model, but a larger R2. This model’s greatest absolute error was 0.0772, and its maximum relative error was 0.0241. The regression coefficient R value of 0.9859, which displayed the model’s strong performance and high prediction accuracy, showed that the PSO-BP model was feasible and helpful for parameter optimization design of vibrating screens.
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
Relation: Mineral Processing and Extractive Metallurgy; https://dx.doi.org/10.3390/min13070929
DOI: 10.3390/min13070929
الاتاحة: https://doi.org/10.3390/min13070929
Rights: https://creativecommons.org/licenses/by/4.0/
رقم الانضمام: edsbas.324D53CA
قاعدة البيانات: BASE