Indirect estimation of bubble size using visual techniques and superficial gas rate

Vinnett, L.; Álvarez-Silva, M.

doi:10.1016/j.mineng.2015.07.012

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Bubble size was measured in a laboratory-scale flotation cell with a 140 × 140 cm crosssection and a width of 15 cm, which simulated a section of an industrial machine [33,34]. Bubble sampling for photographs was carried out using a McGill bubble size analyser (MBSA).…”

Section: Methodsmentioning

confidence: 99%

The Role of Stereological Assumptions in Bubble Size Estimations and Their Implications for Assessing Critical Coalescence Concentrations

Vinnett

Mesa

2023

Minerals

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Accurate measurement of bubble size is critical for assessing flotation performance. However, the 3D nature of bubbles, in contrast to the 2D nature of photographs obtained using a bubble viewer apparatus, may lead to distortions related to stereological assumptions. This study aimed to quantify the impact of these stereological effects on bubble size measurements in frother characterisations. Our results showed that different assumptions regarding bubble shape and volume resulted in variations in bubble size calculations of up to 10%. Furthermore, these stereological effects were propagated to the calculation of the critical coalescence concentration, leading to uncertainties of up to 14% depending on the type of frother. These findings emphasise the importance of considering stereological effects and selecting an appropriate calculation method when measuring bubble size for flotation and reagent assessments.

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Section: Methodsmentioning

confidence: 99%

The Role of Stereological Assumptions in Bubble Size Estimations and Their Implications for Assessing Critical Coalescence Concentrations

Vinnett

Mesa

2023

Minerals

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“…For instance, Kracht et al (2013) proposed a stochastic method using image background covariance for bubble size distribution (BSD) determination, which was validated in laboratory tests and simulations. Similarly, Vinnett and Alvarez-Silva (2015) established a linear model connecting shadow percentage in binary bubble images to D32, using varying gas rates. The latter was refined by , where a linear model was determined between D32 and over 12 image properties obtained automatically, without bubble segmentation, resulting in a fast and accurate estimation of the Sauter diameters in the range of 1.3-6.7 mm.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features

Vinnett,

León,

Mesa

2024

Physicochem. Probl. Miner. Process.

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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 (D<sub>32</sub>) 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.

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“…Computer vision and image processing technology have been applied in many fields. Many scholars have applied machine vision to the flotation of nonferrous metal minerals and nonmetallic materials such as coal and silica sand (Fu and Aldrich, 2019; Lin et al, 2018; Massinaei et al, 2019; Popli et al, 2018; Vinnett and Alvarez-Silva, 2015; Xu et al, 2016). Some (Massinaei et al, 2019) applied computer vision technology to flotation columns in a coal preparation plant.…”

Section: Introductionmentioning

confidence: 99%

Soft sensor of iron tailings grade based on froth image features for reverse flotation

Zhang

Gao

2022

Transactions of the Institute of Measurement and Control

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In the iron reverse flotation production process, the amount of flotation agent and the quality of flotation products are usually judged according to the grade of tailings, so it is essential to measure the grade of tailings froth. This research applies computer vision and image feature extraction technology to the soft sensor of tailings froth grade. An adaptive selection method for the image target region is proposed. The relationship between RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and Lab color space and tailings grade of reverse flotation in iron mine has been analyzed. A new image feature is proposed to characterize the degree of froth mineralization. The RGB and HSI dual color space feature values and froth mineralization degree values are determined as input, and the tailing grade soft sensor model is established by the multilayer feedforward perceptrons and VGG-19 neural network. A tailings grade soft sensor system has been developed and applied in a flotation workshop. The results of industrial tests show that this method is efficient and reliable.

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Indirect estimation of bubble size using visual techniques and superficial gas rate

Cited by 10 publications

References 11 publications

The Role of Stereological Assumptions in Bubble Size Estimations and Their Implications for Assessing Critical Coalescence Concentrations

The Role of Stereological Assumptions in Bubble Size Estimations and Their Implications for Assessing Critical Coalescence Concentrations

Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features

Soft sensor of iron tailings grade based on froth image features for reverse flotation

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