A discussion on the limitations of image analysis for determining bubble size in industrial flotation when using algorithms successfully tested from idealized images

• Autorzy: Vinnett Luis

Identyfikatory

DOI

10.37190/ppmp/174474

• Języki publikacji: EN

Abstrakty

EN

This paper evaluates the capacity of an automated algorithm to detect bubbles and estimate bubble size (Sauter mean diameter, D₃₂) from images recorded in industrial flotation machines. The algorithm is previously calibrated from laboratory images. The D₃₂ results are compared with semi-automated estimations, which are used as "ground truth". Although the automated algorithm is reliable to estimate bubble size at laboratory scale, a significant bias is observed from industrial images for D₃₂ >3.0-4.0 mm. This uncertainty is caused by the presence of small and large bubbles in the same population, with large bubbles forming complex clusters and being observed incomplete, limited by the region of interest. Flotation columns are more prone to this condition, which hinders the estimation of Sauter diameters. The results show the need for bubble size databases that include industrial images. As several image processing tools are currently available, software calibration from ideal bubble images (synthetic or from laboratory rigs) will mostly lead to biased D₃₂ estimations in industrial flotation machines.

Słowa kluczowe

EN

gas dispersion; bubble size; flotation; industrial flotation machine

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2023
• Tom: Vol. 59, iss. 5
• Strony: art. no. 174474
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Vinnett Luis

• Department of Chemical and Environmental Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
• Automation and Supervision Centre for Mining Industry, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).