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基于多变量图像分析的铜矿泡沫浮选分类与识别

Keywords: 多变量图像,泡沫浮选,泡沫图像,品位

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Abstract:

针对矿物浮选过程中以人工观测为主的浮选状态监测易受人主观因素影响,长流程的浮选现场难以实时获得生产状态信息,引起在线监测信息的不准确性及滞后,严重影响浮选生产工况及时调整,造成生产过程资源和能源浪费的问题,基于多变量图像分析方法研究矿物加工领域的泡沫浮选过程泡沫图像的分析与特征表征,并融合多变量图像分析、多分辨率分析、多分辨率-多变量图像分析、改进分水岭的图像分割算法和基于模板匹配的宏块跟踪方法,提取铜矿泡沫图像的颜色、纹理、尺寸、速度和稳定度特征.在此基础上,对铜矿泡沫浮选生产状态进行了分类与识别,并建立泡沫图像变量特征与工艺指标之间的关系模型,可用来预测铜矿泡沫品位.应用结果表明:该方法可实现铜矿浮选过程的实时监控及生产状态的识别.

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