A cascaded recognition method for copper rougher flotation working conditions
A cascaded recognition method for copper rougher flotation working conditions
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DOI：
10.1016/j.ces.2017.09.048
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发表时间：
2018-01
影响因子：
4.7
通讯作者：
M. Lu;Dongheng Xie;Wei-Hua Gui;Lianghong Wu;Chaoyang Chen;Chunhua Yang
中科院分区：
文献类型：
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作者：
M. Lu;Dongheng Xie;Wei-Hua Gui;Lianghong Wu;Chaoyang Chen;Chunhua Yang
由于铜浮选工艺复杂，矿源条件多变，确定粗选工艺条件，保持生产工艺稳定性困难。在深入分析铜浮选过程特点的基础上，建立了铜粗选机工况识别系统，提出了级联识别方法。在第一阶段，基于泡沫图像局部颜色特征和工艺参数的融合信息，建立识别模型，识别给矿类型。第二阶段，采用工况优先的非对称二叉树SVM多类分类方法（WCP-BTSVM）对铜粗选厂浮选工况进行识别。工业试验表明，该方法能够较为准确地识别铜粗轧机的工况，为后续过程控制提供决策依据。
Due to the complex process of copper flotation and the frequently diversified conditions of ore sources, it is difficult to identify rougher flotation conditions and maintain the stability of production process. By deeply analyzing the characteristics of the copper flotation process, the recognition system for working conditions in copper rougher is established and the cascaded recognition method is presented. At the first stage, the recognition model is built to identify feeding ore types based on fusion information of froth image local colour features and process parameters. At the second stage, the asymmetry binary tree SVM multi-class classification method with working condition priority rating (WCP-BTSVM) is used to recognize copper rougher flotation conditions. As demonstrated in the industrial experiment, the proposed method can relatively accurate identify the working conditions in copper rougher and thus can provide a solid foundation for decision-making in follow-up process control.