基于BP神经网络的双叶轮浮选机选矿效率预测研究  

Beneficiation Efficiency Prediction of Double Impeller Flotation Machine Based on BP Neural Network

作　　者：陈飞[1] 随婕斐 李智力[1,3] 张泽强[1] 秦芳[1] 唐远 何东升[1] CHEN Fei;SUI Jiefei;LI Zhili;ZHANG Zeqiang;QIN Fang;TANG Yuan;HE Dongsheng(Wuhan Institute of Technology,School of Resources and Safety Engineering;Beijing Metallurgical Industry Press Co.,Ltd.;Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education,Wuhan Institute of Technology)

机构地区：[1]武汉工程大学资源与安全工程学院,湖北省武汉市430074 [2]北京冶金工业出版社有限公司 [3]武汉工程大学磷资源开发利用教育部工程研究中心

出　　处：《现代矿业》2023年第1期207-210,共4页Modern Mining

基　　金：国家重点研发计划项目(编号:2019YFC1905801);湖北省高等学校优秀中青年科技创新团队计划项目(编号:T2021006);武汉工程大学校内科学基金研究项目(编号:K2021099,K202064);湖北三峡试验室开放基金项目(编号:SK211008);磷资源开发利用教育部工程研究中心开放基金项目(编号:LCX2021006)。

摘　　要：为保证浮选机既有足够的充气量,又能产生矿物浮选所需的静态分选环境,通过将离心叶轮与搅拌叶轮有机结合,设计了双叶轮控制系统浮选机。在前期研究的基础上,通过固定双叶轮浮选机离心叶轮结构参数,选取双叶轮浮选机搅拌叶轮直径和转速为输入因子,磷矿选矿效率为输出因子,建立了双叶轮浮选机选矿效率预测模型,并通过样本检验了模型的准确性。研究结果表明:建立的BP神经网络模型能准确预测双叶轮浮选机选矿效率,预测值与试验值的相对误差一般小于5%;建立的选矿效率预测模型可用于双叶轮浮选机浮选参数的优化控制与决策,可减少试验量,节省人力、物力和时间。In order to ensure that the flotation machine has sufficient aeration and can produce the static separation environment required for mineral flotation,a double impeller control system flotation machine is designed by combining the centrifugal impeller with the stirring impeller.Based on the previous research,by fixing the structural parameters of the centrifugal impeller of the double-impeller flotation machine,selecting the diameter and speed of the impeller of the double-impeller flotation machine as the input factors,and the beneficiation efficiency of the phosphate rock as the output factor,the prediction model of the beneficiation efficiency of the double-impeller flotation machine was established,and the accuracy of the model was tested by samples.The results show that the established BP neural network model can accurately predict the beneficiation efficiency of double impeller flotation machine,and the relative error between the predicted value and the experimental value is generally less than 5%.The established prediction model of beneficiation efficiency can be used for optimal control and decision-making of flotation parameters of double impeller flotation machine,which can reduce the amount of test and save manpower,material resources and time.

关 键 词：BP 神经网络 双叶轮浮选机 选矿效率 预测模型 

分 类 号：TD456[矿业工程—矿山机电] TP183[自动化与计算机技术—控制理论与控制工程]