基于数字图像处理的铁矿石深度还原评价方法  

Evaluation on Deep Reduction of Iron Ore Based on Digital Image Processing Techniques

作　　者：高鹏[1] 韩跃新[1] 李艳军[1] 孙永升[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：《东北大学学报（自然科学版）》2012年第1期133-136,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(51074036;51134002);国家高技术研究发展计划项目(2009AA06Z111)

摘　　要：复杂难选铁矿石深度还原包含铁氧化物还原和铁颗粒长大两个过程,目前的评价指标金属化率仅能评价铁氧化物的还原程度,并不能对铁颗粒的粒度特征进行有效评价.为了建立完整的深度还原评价体系,将数字处理技术引入铁颗粒的检测过程,采用数码反光显微镜获取深度还原物料的数字图像,通过处理得到铁颗粒的二维特征参数,结合铁颗粒的球形特征,推导了铁颗粒累计粒度特性曲线的计算方法.实际应用试验表明,铁粉的分选指标和铁颗粒粒度的变化趋势保持高度一致,基于数字图像处理的铁矿石深度还原评价方法是可行的.The deep reduction of iron oxides and the growth of iron particles were conducted during reduction for refractory iron ore. Currently, metallization rate as an evaluation index of reduction can only evaluate reductive degree, but not particle size characteristics of iron particles. For building intact evaluation system of reduction, digital processing technology was used to analyze features of iron particles. Furthermore, formula for calculating the cumulative granularity property curve of iron particles was derived by utilizing the two-dimensional characteristic parameters, which collected and handled from digital images of material after reduction in combination with the spherical feature of iron particles, with which the size characteristics of iron particles under different conditions could be evaluated effectively. The experimental results showed that the separating index of iron powder products follows the changing tendency of iron particle size precisely. Consequently, evaluation method for the deep reduction of iron ore with digital image processing techniques is feasible.

关 键 词：复杂难选铁矿石 深度还原 磁选 数字图像处理 铁颗粒 粒度分布 

分 类 号：TD925.7[矿业工程—选矿]