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    Tytuł:
    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
    Powiązania:
    https://bibliotekanauki.pl/articles/24291653.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    gas dispersion
    bubble size
    flotation
    industrial flotation machine
    Opis:
    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.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 174474
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features
    Autorzy:
    Vinnett, Luis
    León, Roberto
    Mesa, Diego
    Powiązania:
    https://bibliotekanauki.pl/articles/29552038.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    machine learning
    artificial neural network
    flotation
    bubble size
    Sauter diameter
    Opis:
    Bubble size measurements in aerated systems such as froth flotation cells are critical for controlling gas dispersion. Commonly, bubbles are measured by obtaining representative photographs, which are then analyzed using segmentation and identification software tools. Recent developments have focused on enhancing these segmentation tools. However, the main challenges around complex bubble cluster segmentation remain unresolved, while the tools to tackle these challenges have become increasingly complex and computationally expensive. In this work, we propose an alternative solution, circumventing the need for image segmentation and bubble identification. An Artificial Neural Network (ANN) was trained to estimate the Sauter mean bubble size (D32) based on macroscopic image features obtained with simple and inexpensive image analysis. The results showed excellent prediction accuracy, with a correlation coefficient, R, over 0.998 in the testing stage, and without bias in its error distribution. This machine learning tool paves the way for robust and fast estimation of bubble size under complex bubble images, without the need of image segmentation.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 185759
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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