基于PSO优化T-S模糊神经网络的磨矿粒度预测  

Grinding size prediction of optimization of T-S fuzzy neural network based on PSO

作　　者：黎芬芳 崔桂梅[1] LI Fenfang;CUI Guimei(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,Inner Mongolia,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《烧结球团》2022年第4期64-70,共7页Sintering and Pelletizing

基　　金：国家自然科学基金资助项目(61763039)。

摘　　要：选矿厂二段磨矿粒度是影响精品矿位和回收率的关键因素。本文针对目前选矿厂无法对磨矿粒度进行实时检测的问题,结合二段磨矿过程,使用基于粒子群算法(PSO)优化T-S模糊神经网络的方法,建立二段磨矿粒度软测量模型;并采集选矿厂实际生产数据,进行模型对比试验。结果表明:PSO优化T-S模糊神经网络的模型命中率为94%,平均相对误差为0.005 4,模型性能明显优于T-S模糊神经网络模型和RBF神经网络模型;优化模型能有效解决二段磨矿粒度与变量间的模糊性问题,且预测精度较高,满足选矿厂对二段磨矿粒度实时检测的要求。本文研究成果可为二段磨矿粒度软测量建模提供新策略。The two-stage grinding size in dressing work is a key factor affecting the iron concentrate grade and recovery rate.Aiming at the problem that the current dressing work cannot detect the grinding size in real time, combined with the two-stage grinding process, the method based on particle swarm optimization(PSO) is used to optimize the T-S fuzzy neural network, and a soft measurement model of two-stage grinding size is established;while the actual production data of the dressing work is collected, the model comparison tests is conducted.The results show that the model hit rate of the T-S fuzzy neural network optimized by PSO is 94%,the average relative error is 0.005 4,and the model performance is significantly better than the T-S fuzzy neural network model and the RBF neural network model;the optimization model can effectively solve the fuzzy problem between the particle size and variables of the two-stage grinding, and the prediction accuracy is high, which meets the requirements of the dressing work for the real-time detection of the size of the two-stage grinding.The research results can provide a new strategy for modeling the soft measurement of the size of the two-stage grinding.

关 键 词：球磨机 磨矿粒度 软测量模型 粒子群算法 T-S模糊神经网络 

分 类 号：TF3[冶金工程—冶金机械及自动化] TH113[机械工程—机械设计及理论] TD453[矿业工程—矿山机电]