Abstract This Short Communication presents a comparison between two automated methodologies to estimate bubble size in industrial flotation equipment (mechanical cells and columns). The studied database includes 106 conditions in flotation machines from 10 to 300 m3, which were sampled by means of the McGill bubble size analyser. The first methodology (conventional image processing) uses circularity, ellipse detection, segmentation and reprocessing of separated objects to estimate the Sauter mean diameter D32. The second methodology evaluates the binary images as trains of pulses, whose spectral bandwidth is correlated with the D32. Both techniques have been compared to a semi-automatic approach, which manually complete the conventional image analysis to obtain the total bubble size distribution. For D32 3.5 mm. On the other hand, the spectral method showed a better sensitivity with an approximately linear trend up to 5 mm. Thus, a combination of these two methodologies is suggested for the D32 characterization in industrial flotation machines.

中文翻译：

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评估工业浮选机气泡尺寸的两种自动化图像处理方法

摘要 这篇短文比较了两种估算工业浮选设备（机械单元和浮选塔）中气泡大小的自动化方法。研究的数据库包括 10 到 300 m3 的浮选机中的 106 个条件，这些条件是通过 McGill 气泡尺寸分析仪采样的。第一种方法（传统图像处理）使用圆形度、椭圆检测、分割和分离对象的再处理来估计 Sauter 平均直径 D32。第二种方法将二进制图像评估为脉冲串，其频谱带宽与 D32 相关。这两种技术都与半自动方法进行了比较，后者手动完成常规图像分析以获得总气泡尺寸分布。对于 D32 3.5 毫米。另一方面，光谱方法显示出更好的灵敏度，具有高达 5 mm 的近似线性趋势。因此，建议将这两种方法结合用于工业浮选机中的 D32 表征。