微型电脑应用2025，Vol.41Issue(1)：43-48,6.
基于多元非线性回归和BP神经网络的矿石加工质量预测和控制
Prediction and Regulation of Ore Processing Quality Based on Multivariate Nonlinear Regression and BP Neural Network
摘要
Abstract
Raw ore quality and production temperature affect the quality of ore product.The quality of raw ore and product can be described by raw ore parameters and quality evaluation index,respectively.Given the quality of raw ore,the processing and production of the ore is mainly achieved by adjusting the temperature of production systems Ⅰ and Ⅱ.Based on processing time,system temperature,raw ore parameters and quality index data recorded in a certain production workshop,a multivariate nonlinear regression model is established between raw ore parameters,system temperature and product quality evaluation in-dex.Through this model,the quality of ore products under different raw ore parameters and system temperature can be calcu-lated.The BP neural network model is established based on customer requirements for ore product quality and product quality evaluation index.The trained BP neural network can quickly calculate the temperature of production systems Ⅰ and Ⅱ to facili-tate actual production operations.关键词
质量指标/原矿参数/温度调节/多元非线性回归/BP神经网络Key words
quality index/raw ore parameter/temperature regulation/multivariate nonlinear regression/BP neural network分类
信息技术与安全科学引用本文复制引用
唐鹏翔,杨能,张晓美..基于多元非线性回归和BP神经网络的矿石加工质量预测和控制[J].微型电脑应用,2025,41(1):43-48,6.基金项目
吉林省职业教育与成人教育教学改革研究重点课题(2022ZCZ051) （2022ZCZ051）
吉林省高等教育教学改革研究课题(JLJY202269980687) （JLJY202269980687）
