Assessment of two automated image processing methods to estimate bubble size in industrial flotation machines

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 < 2 mm (spherical regime), the first approach shows better performance than the spectral analysis, with significantly higher precision. For D32 ≥ 2 mm (ellipsoidal regime), the conventional image processing presents significant bias, high dispersion as well as a cliff trend 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.