鲕状赤铁矿石深度还原过程中金属铁颗粒粒度预测模型  

Prediction model of metallic iron particle size during coal-based reduction of oolitic hematite ore

作　　者：孙永升[1] 韩跃新[1] 高鹏[1] 李艳军[1] 

机构地区：[1]东北大学资源与土木工程学院,辽宁沈阳110819

出　　处：《中南大学学报（自然科学版）》2017年第2期282-288,共7页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(51134002);中央高校基本科研业务费专项资金资助项目(N140108001)~~

摘　　要：以湖北官店鲕状赤铁矿为研究对象,对其进行深度还原试验,利用光学显微图像分析技术对还原物料中金属铁颗粒粒度进行测量,考察还原温度和还原时间对铁颗粒粒度的影响,并采用MATLAB软件对试验数据进行拟合分析,建立铁颗粒粒度与还原条件之间的数学模型。研究结果表明:不同还原条件下金属铁颗粒粒度累积特性曲线呈现出相同的变化规律;升高还原温度或延长还原时间可使铁颗粒粒度明显增加;建立铁颗粒粒度D80与还原温度和还原时间之间的预测模型;模型的计算值与试验值具有良好的吻合性,可用于预估深度还原过程中金属铁颗粒的粒度;基于该模型,可通过调整温度和时间以实现金属铁颗粒粒度的优化与控制。An oolitic iron ore taken from Guandian in Hubei province was reduced, and the size of metallic iron particles in reduced product was measured using optical image analysis. The effects of reduction temperature and time on the size of metallic iron particles were investigated. The experimental data were analyzed by MATLAB software, and the mathematic model of metallic iron particle size considering reduction conditions was proposed. The results indicate that the curves of size cumulative passing percentage of metallic iron particles present similar variation trend under different conditions. When the reduction temperature increases and reduction time extends, the size of metallic iron particles increases obviously. The prediction model for D80 of metallic iron particles considering reduction temperature and time was established. The calculation values determined by this model correlate well with the test results, indicating that it can be used to predict the particle size of metallic iron in coal-based reduction. Based on the model, the particle size of metallic iron can be optimized by means of adjusting reduction temperature and time.

关 键 词：深度还原 铁颗粒 粒度 拟合分析 数学模型 

分 类 号：TD925.7[矿业工程—选矿]