基于机器视觉的雷波薄夹层磷矿石预分选研究  

Study on Pre-separation of Leibo Thin Interlayer Phosphate Ore Based on Machine Vision

作　　者：牟少樊 张翼[1] 李佳楠 顾玉成 陈希阳 王紫越 MOU Shaofan;ZHANG Yi;LI Jianan;GU Yucheng;CHEN Xiyang;WANG Ziyue(School of Resource and Security Engineering,Wuhan Institute of Technology,Wuhan 430074,China)

机构地区：[1]武汉工程大学资源与安全工程学院,武汉430074

出　　处：《有色金属（选矿部分）》2023年第4期80-85,共6页Nonferrous Metals（Mineral Processing Section）

摘　　要：随着磷矿资源的快速消耗,现阶段我国中低品位磷矿存在占比大且利用率较低的问题,导致生产处理成本增加。利用机器视觉技术代替人工观察实现提前抛废,对磷矿进行预富集,提升矿石综合利用效率,减少经济成本。针对四川省雷波县巴姑中低品位薄夹层磷矿的特性,提出一种基于HSV颜色模型采用多阈值法提取特征值并结合KNN算法的磷矿动态实时预分选算法,待选矿石经本算法分选后精矿品位达到18.3%,这表明该算法的识别准确率较高,基本满足企业识别尾矿的分选需求,达到抛尾的目的。With the rapid consumption of phosphate rock resources,there are problems of large proportion and low utilization rate of low-grade phosphate rock in China at this stage,which leads to the increase of production and treatment costs.Instead of manual observation,machine vision technology is used to realize early discarding and preconcentration of phosphate rock,so as to improve the comprehensive utilization efficiency of ore and reduce the economic cost.According to the characteristics of Bagu medium and low-grade thin bedded phosphate rock in Leibo county,Sichuan province,a dynamic real-time preseparation algorithm of phosphate rock based on HSV color model,multi threshold method and KNN algorithm is proposed.The grade of the concentrate reaches 18.3%,which shows that the algorithm has a high recognition accuracy,and basically meets the needs of enterprises to identify the tailings and achieve the purpose of discarding tailings.

关 键 词：薄夹层磷矿 HSV颜色模型 KNN 磷矿预选 

分 类 号：TD925[矿业工程—选矿] TP391.4[自动化与计算机技术—计算机应用技术]