基于神经网络的磁选控制算法的研究  

Study on Magnetic Separation Based on Neural Network Algorithm

作　　者：李金文[1] 

机构地区：[1]承德石油高等专科学校,河北承德067000

出　　处：《仪表技术》2014年第7期34-36,共3页Instrumentation Technology

基　　金：河北省科技支撑计划项目(12214512)

摘　　要：首先分析了我国钢铁加工行业的现状及面临的问题,阐述了铁矿石的预加工过程及工艺;在该过程中磁选是至关重要的环节,它决定着铁的回收率,而提高铁的回收率的关键取决于磁选算法。BP神经网络具有自学习和自适应性,依据其特点,提出了磁选控制的优化BP神经网络算法,在仿真和实验的基础上,证明了该算法可以有效地提高铁的回收率。The current situation and problems of the steel industry in China are analyzed. The pre-process and technology of iron ore are elaborated. Magnetic separation is a very important link in the process and it determines the iron recovery rate. The effective method of improving iron recovery rate depends on magnetic separation algorithm. BP neural network has the self-study and adaptability. According to its characteristics, this paper presents BP neural network optimization algorithm for magnetic separation. Based on the simulation and experimental verification, it proves that the algorithm can effectively improve the iron recovery rate.

关 键 词：铁矿石 铁的回收率 自适应性 BP神经网络 

分 类 号：TD924[矿业工程—选矿]