基于BP神经网络的白云母超细磨工艺参数优化OA北大核心CSTPCD

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.