辽宁工程技术大学学报（自然科学版）2024，Vol.43Issue(3)：273-278,6.DOI:10.11956/j.issn.1008-0562.20230227
基于BP神经网络的白云母超细磨工艺参数优化
Optimization of muscovite ultrafine grinding process parameters based on BP neural network
摘要
Abstract
To improve the efficiency of ultrafine grinding of muscovite,the sensitivity between the ultrafine grinding efficiency and parameters was analyzed by using Pearson's correlation coefficient and the parameters of ultrafine grinding orthogonal test of muscovite was optimized based on a BP neural network model.The reseach results show that the sensitivity of each parameter to the ultrafine grinding efficiency is in the order of ceramic ball gradation,stirring rate,grinding aid type,ultrafine grinding time and grinding aid dosage.The muscovite with a mass fraction of 83.04%at-13 μm can be obtained using the process parameters optimized by the BP neural network for the ultrafine grinding test,which is increased by 2.19%compared with optimum conditions of orthogonal test.This model can improve the efficiency of ultrafine grinding of muscovite,and the prediction accuracy is high.The research conclusions provide a reference for the efficient preparation of ultrafine muscovite powder.关键词
白云母/超细磨/正交试验/BP神经网络/参数优化Key words
muscovite/ultra-fine grinding/orthogonal test/BP neural network/parameter optimization分类
矿业与冶金引用本文复制引用
田家怡,彭祥玉,张帅,王宇斌,赵鑫,肖巍..基于BP神经网络的白云母超细磨工艺参数优化[J].辽宁工程技术大学学报（自然科学版）,2024,43(3):273-278,6.基金项目
国家自然科学基金项目(52004197) （52004197）
中国博士后科学基金项目(2023M732746) （2023M732746）
