基于SSA-BP神经网络的重力式矿浆浓度检测算法  

Research on Gravity Pulp Concentration Detection Algorithm Based on SSA-BP Neural Network

作　　者：陈永春 黄宋魏[1] 和丽芳[1] 高徐辉 钟婷婷 CHEN Yong-chun;HUANG Song-wei;HE Li-fang;GAO Xu-hui;ZHONG Ting-ting(Faculty of Land and Resources Engineering,Kunming University of Science and Technology)

机构地区：[1]昆明理工大学国土资源工程学院

出　　处：《化工自动化及仪表》2024年第6期1010-1016,1027,共8页Control and Instruments in Chemical Industry

基　　金：云南省万人计划青年拔尖人才项目(批准号:KKRD202156031)资助的课题。

摘　　要：为了克服现有矿浆浓度检测技术的不足,提高矿浆浓度检测技术在复杂选矿环境中的精度和适应性,在对现有矿浆浓度检测技术进行分析的基础上,提出重力式矿浆浓度检测方法,利用麻雀搜索算法(SSA)改进BP神经网络算法优化关键参数,进行了系统设计、算法研究和应用测试,结果表明:算法具有检测精度高、适应性强、稳定性好等优点。For purpose of overcoming shortcomings of the existing pulp concentration detection technologies and improving their accuracy and suitability in complex mineral processing environments,having some slurry concentration detection technologies analyzed and based to propose a technical method for a tube mounted gravity slurry concentration detector was implemented,including making use of the sparrow search algorithm(SSA) improve BP algorithm and optimize key parameters,the system design,algorithm research and application testing.The application results indicate that,the algorithm proposed has advantages of high detection accuracy,strong adaptability and good stability.

关 键 词：SSA-BP神经网络 矿浆浓度 参数校正 重力式 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]