中南大学学报（自然科学版）2017，Vol.48Issue(2)：282-288,7.DOI:10.11817/j.issn.1672-7207.2017.02.002
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
摘要
Abstract
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 forD80 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Key words
coal-based reduction/metallic iron particles/particle size/fitting analysis/mathematic model分类
矿业与冶金引用本文复制引用
孙永升,韩跃新,高鹏,李艳军..鲕状赤铁矿石深度还原过程中金属铁颗粒粒度预测模型[J].中南大学学报（自然科学版）,2017,48(2):282-288,7.基金项目
国家自然科学基金资助项目(51134002) （51134002）
中央高校基本科研业务费专项资金资助项目(N140108001)(Project(51134002) supported by the National Natural Science Foundation of China （N140108001）
Project(N140108001) supported by the Fundamental Research Funds for the Central Universities of China) （N140108001）
