实际矿石过筛粒度的磨矿动力学特征  

Grinding dynamics characterization of ores by under sieve size

作　　者：袁程方 吴彩斌[2,3] 凌莉 谢峰 姚鑫 李哲阳 YUAN Chengfang;WU Caibin;LING Li;XIE Feng;YAO Xin;LI Zheyang(International Institute for Innovation,Jiangxi University of Science and Technology,Nanchang 330000,China;School of Resources and Environmental Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources,Ganzhou 341000,China)

机构地区：[1]江西理工大学国际创新研究院,江西南昌330000 [2]江西理工大学资源与环境工程学院,江西赣州341000 [3]战略金属矿产资源低碳加工与利用江西省重点实验室,江西赣州341000

出　　处：《中南大学学报（自然科学版）》2024年第11期4066-4079,共14页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(51764015);江西理工大学高层次人才科研启动项目(jxust-45)。

摘　　要：以实际磁铁矿石为研究对象,首先采用钢球、瓷球和二元介质3种介质环境进行批次磨矿实验;然后,使用标准套筛对各磨矿产品筛析,获得粒度特性曲线;最后,对过筛粒度进行统计学分析和线性回归计算,得到实际磁铁矿石在不同磨矿介质环境下的过筛粒度磨矿动力学特征。研究结果表明:宽粒级实际矿石过筛粒度变化速率受磨矿时间影响,不符合一阶动力学,但具备n阶动力学特征,线性回归计算的精度较高;磨矿介质环境虽然会对过筛粒度动力学参数产生影响,但不会改变过筛粒度的动力学变化行为,仍符合n阶动力学特征;细粒级磁铁矿石的过筛粒度与磨矿时间的线性相关性不显著(R^(2)<0.90),不具备零阶产出特征,而具备显著的幂函数特征(R^(2)>0.99);实际磁铁矿石过筛粒度动力学预测模型展开式为指数函数嵌套的二元复合函数,其中参数函数s(x)和m(x)没有特定函数特征,均为四阶多项式函数,符合n阶磨矿动力学变化规律,可以准确预测过筛粒度的变化趋势,弥补常规累积产率动力学对磨矿特征表征的不足。The magnetite ore was taken as the subject of study.Firstly,batch grinding experiments were performed by three distinct media environments of steel balls,ceramic balls,and binary media.Secondly,particle size characteristic curves were derived by sieving each grinding product.Finally,statistical analysis and linear regression calculations were applied to the under sieve size,and the grinding kinetics characteristics of magnetite ore under varying grinding media environments were revealed.The results show that under sieve size change rate of actual ores is influenced by grinding time.It does not conform to first-order kinetics but exhibits n-order kinetic characteristics,and the accuracy of linear regression calculation is relatively high.Although the grinding medium environment may affect the dynamic parameters of under sieve size,it does not alter the dynamic behavior,which still conforms to n-order dynamic characteristics.The linear correlation between the sieving particle size and grinding time of fine-grained magnetite is not significant(R^(2)<0.90),indicating that it does not exhibit zero-order output characteristics,but rather demonstrates significant power function characteristics(R^(2)>0.99).The expansion formula of the dynamic prediction model for under sieve size of actual magnetite ore is a binary composite function nested with exponential functions.The parameter functions s(x)and m(x)lack specific functional characteristics are both fourth-order polynomial functions,aligning with the n-order grinding kinetics variation law.They can accurately predict the trend of under sieve size changes and address the limitations of conventional cumulative yield kinetics in characterizing grinding characteristics.

关 键 词：磨矿动力学 过筛粒度 零阶产出特征 批次磨矿 

分 类 号：TD921.4[矿业工程—选矿]