It is a well-established fact that the flotation performance is reflected in the structure of the froth surface. A machine vision is able to extract the froth features and present them to the plant operators or the process control system. In this communication, computer vision and machine learning techniques are integrated for recognition of process conditions of a coal column flotation circuit. An industrial flotation column is operated under various process conditions. The metallurgical parameters (combustible recovery and concentrate ash content) are measured and the froth visual (bubble size, froth velocity, and color) and textural (energy, entropy, contrast, homogeneity, and correlation) features are extracted by a machine vision system. The principle component analysis (PCA) is applied to reduce the input space. The relationship between the froth characteristics and the metallurgical parameters is modeled using different intelligent algorithms and a predictive model is built. The froth images are classified based on the froth features using the K-means data-clustering algorithm. The predictive and classification models are eventually integrated to diagnose the process conditions of the flotation column.

众所周知，浮选性能反映在泡沫表面的结构中。机器视觉能够提取泡沫特征并将其呈现给工厂操作员或过程控制系统。在这种交流中，计算机视觉和机器学习技术集成在一起，用于识别煤柱浮选回路的工艺条件。工业浮选塔在各种工艺条件下运行。测量冶金参数（可燃回收率和精矿灰分含量），并通过机器视觉系统提取泡沫外观（气泡大小，泡沫速度和颜色）和质地（能量，熵，对比度，均匀性和相关性）特征。主成分分析（PCA）用于减少输入空间。使用不同的智能算法对泡沫特性与冶金参数之间的关系进行建模，并建立预测模型。使用K均值数据聚类算法，基于泡沫特征对泡沫图像进行分类。最终将预测模型和分类模型集成在一起，以诊断浮选塔的工艺条件。