基于机器视觉的雷波薄夹层磷矿石预分选研究

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及减少经济成本。针对四川省雷波县巴姑中低品位薄夹层磷矿的特性，本文提出一种基于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进行化验后的精矿品位可到18.3%，这表明本文提出的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。

Study on pre separation of phosphorus ore with thin interlayer in Leibo based on machine vision

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 pre separation algorithm of phosphate rock based on HSV color model, multi threshold method and KNN algorithm is proposed in this paper. The concentrate grade of the ore to be sorted and tested by this algorithm can reach 18.3%, which shows that the recognition accuracy of the algorithm proposed in this paper is high, which basically meets the separation needs of enterprises to identify tailings, and achieves the purpose of tailing.