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

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Treść / Zawartość
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Warianty tytułu
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.
Rocznik
Strony
art. no. 174474
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
  • 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
  • BAILEY, M., GOMEZ, C.O., FINCH, J.A., 2005. Development and application of an image analysis method for wide bubble size distributions. Miner. Eng. 18, 1214-1221.
  • BARBIAN, N., HADLER, K., CILLIERS, J., 2006. The froth stability column: measuring froth stability at an industrial scale. Miner. Eng. 19(6–8), 713–718.
  • CHEN, F., GOMEZ, C., FINCH, J., 2001. Bubble size measurement in flotation machines. Miner. Eng. 14 (4), 427–432.
  • CHEN, M., ZHANG, C., YANG, W., ZHANG, S., & HUANG, W., 2023. End-to-End Bubble Size Distribution Detection Technique in Dense Bubbly Flows Based on You Only Look Once Architecture. Sensors, 23(14), 6582.
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  • GOMEZ, C. O., FINCH, J. A., 2002. Gas dispersion measurements in flotation machines. CIM Bull. 95(1066), 73-78.
  • GORAIN, B.K., 1998. The effect of bubble surface area flux on the kinetics of flotation and its relevance to scale-up. Department of Mining, Minerals and Materials Engineering. University of Queensland.
  • GORAIN, B. K., FRANZIDIS, J. P., MANLAPIG, E. V., 1997. Studies on impeller type, impeller speed and air flow rate in an industrial scale flotation cell. Part 4: Effect of bubble surface area flux on flotation performance. Miner. Eng. 10(4), 367-379.
  • GRAU, R., HEISKANEN, K., 2002. Visual technique for measuring bubble size in flotation machines. Miner. Eng. 15 (7), 507–513.
  • HAAS, T., SCHUBERT, C., EICKHOFF, M., PFEIFER, H, 2020. BubCNN: Bubble Detection Using Faster RCNN and Shape Regression Network. Chem. Eng. Sci. 216, 115467.
  • HERNANDEZ-AGUILAR, J., GOMEZ, C., FINCH, J., 2002. A technique for the direct measurement of bubble size distributions in industrial flotation cells. In: Proceedings of the 34th Annual Meeting of the Canadian Mineral Processors, 389–402.
  • HESSENKEMPER, H., STARKE, S., ATASSI, Y., ZIEGENHEIN, T., Lucas, D., 2022. Bubble Identification from Images with Machine Learning Methods. Int. J. Multiph, 155, 104169.
  • KARN, A.; ELLIS, C.; ARNDT, R.; HONG, J., 2015. An integrative image measurement technique for dense bubbly flows with a wide size distribution. Chem. Eng. Sci. 122, 240–249.
  • MA, Y.; YAN, G.; SCHEUERMANN, A.; BRINGEMEIER, D.; KONG, X.-Z.; LI, L., 2014. Size distribution measurement for densely binding bubbles via image analysis. Exp. Fluids 55, 1860.
  • MALYSA, K., NG, S., CYMBALISTY, L., CZARNECKI, J., MASLIYAH, J., 1999. A method of visualisation and characterization of aggregate flow inside a separation vessel. Part 1. Size, shape and rise velocity of aggregates. Int. J. Miner. Process. 55, 171–188.
  • MESA, D., QUINTANILLA, P., REYES, F., 2022. Bubble Analyser—An open-source software for bubble size measurement using image analysis. Miner. Eng. 180, 107497.
  • NESSET, J., 2011. Modeling the Sauter mean bubble diameter in mechanical, forced-air flotation machines. Ph D. Thesis, Department of Mining and Materials Engineering, McGill University, Canada.
  • POLETAEV, I.E., PERVUNIN, K.S., TOKAREV, M.P., 2016. Artificial neural network for bubbles pattern recognition on the images. J. Phys. Conf. Ser. 752, 072002.
  • RANDALL, E., GOODALL, C., FAIRLAMB, P., DOLD, P., O’CONNOR, C., 1989. A method for measuring the sizes of bubbles in two and three-phase systems. J. Phys. Eng. Sci. Instr. 22, 827–833.
  • RIQUELME, A.; DESBIENS, A.; BOUCHARD, J.; DEL VILLAR, R., 2013, Parameterization of Bubble Size Distribution in Flotation Columns. IFAC Proc. Vol. 46, 128–133.
  • RODRIGUES, R.T., RUBIO, J., 2003. New basis for measuring the size distribution of bubbles. Miner. Eng. 16, 757–765.
  • TAO, D. 2005. Role of Bubble Size in Flotation of Coarse and Fine Particles—A Review. Sep. Sci. Technol 39, 741–760.
  • VINNETT, L., CONTRERAS, F., YIANATOS, J., 2012. Gas dispersion pattern in mechanical flotation cells. Miner. Eng. 26, 80–85.
  • VINNETT, L., URRIOLA, B., ORELLANA, F., GUAJARDO, C., ESTEBAN, A., 2022a. Reducing the Presence of Clusters in Bubble Size Measurements for Gas Dispersion Characterizations. Minerals 12, 1148.
  • VINNETT, L, YIANATOS, J, ACUÑA, C, CORNEJO, I., 2022b. A Method to Detect Abnormal Gas Dispersion Conditions in Flotation Machines. Minerals 12(2), 125.
  • VINNETT, L., YIANATOS, J., ARISMENDI, L., WATERS, K.E., 2020. Assessment of two automated image processing methods to estimate bubble size in industrial flotation machines, Miner. Eng. 159, 106636.
  • WILLS, B.A.; FINCH, J.A., 2016. Froth Flotation. In Wills’ Mineral Processing Technology; Elsevier: Amsterdam, The Netherlands, 2016, pp. 265–380.
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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-57965074-da90-4185-952f-e4791c0bc31f
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