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泡沫浮选广泛应用于选矿领域,它是一种能够有效提取矿粒的方法。但是,浮选过程存在着大量的影响因素和严重的非线性,这使得浮选过程的优化控制很难实现。因此,为了保证浮选处于最优工况,有必要依据浮选泡沫的表面特征来调整相应的操作变量。本文提出了基于动态纹理建模的方法应用于浮选工况的分类。采用ARMA模型进行动态纹理建模,通过样本学习得到模型参数A,C,Q。对不同类样本模型参数A,C计算其Martin距离,根据最小距离原则来进行分类识别。仿真结果表明:所提出的动态纹理模型能准确地描述动态泡沫,且能有效地检测浮选泡沫状态。
Foam flotation is widely used in the field of mineral processing, it is a way to effectively extract ore particles. However, the flotation process has a large number of influencing factors and serious nonlinearities, which makes the optimal control of the flotation process difficult to achieve. Therefore, in order to ensure optimal flotation conditions, it is necessary to adjust the corresponding operating variables according to the surface characteristics of the flotation foam. This paper presents a method based on dynamic texture modeling applied to the classification of flotation conditions. The ARMA model is used for dynamic texture modeling, and the model parameters A, C and Q are obtained through sample learning. The Martin distance is calculated for different types of sample parameters A and C, and the classification is performed according to the principle of minimum distance. The simulation results show that the proposed dynamic texture model can accurately describe the dynamic foam and can effectively detect the state of the flotation foam.