Journal ArticleDOI
A comprehensive review of froth surface monitoring as an aid for grade and recovery prediction of flotation process. Part B: Texture and dynamic features
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In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and st...Abstract:
In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and st...read more
Citations
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Journal ArticleDOI
Froth image feature engineering-based prediction method for concentrate ash content of coal flotation
TL;DR: The feature engineering of coal flotation froth image in this paper can make a good prediction of thecoal flotation concentrate ash content and can be used as the theoretical basis for the intelligent construction of flotation.
Journal ArticleDOI
Flotation Froth Phase Bubble Size Measurement
TL;DR: In this article, methods to measure froth phase bubble sizes in mineral froth flotation are reviewed and the state of development, equipment set-up, bubble size estimation procedure, and bubble size estimator are discussed.
Journal ArticleDOI
LTGH: A Dynamic Texture Feature for Working Condition Recognition in the Froth Flotation
TL;DR: Li et al. as discussed by the authors proposed a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP).
Journal ArticleDOI
A digital twin dosing system for iron reverse flotation
Dingsen Zhang,Xianwen Gao +1 more
TL;DR: Based on digital twin technology and machine learning algorithms, a digital twin system for iron reverse flotation reagents was designed in this article , where a soft sensor model of tailings grade was established to monitor the product quality in real-time.
Journal ArticleDOI
Ash determination of coal flotation concentrate by analyzing froth image using a novel hybrid model based on deep learning algorithms and attention mechanism
TL;DR: Wang et al. as discussed by the authors proposed a convolution-attention parallel network (CAPNet) for coal flotation analysis, which achieved a R 2 of 0.926, which was about 5% to 10% higher than those of baseline CNN models, and over 30% to those of machine learning (ML) methods.
References
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Journal ArticleDOI
The use of the froth surface lamellae burst rate as a flotation froth stability measurement
TL;DR: In this article, a machine vision technique was developed to measure the rate at which lamellae on the froth surface burst, which may be a more practical measure for an industrial environment and the potential of using this stability measurement for the management of a flotation bank is discussed.
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A new approach for subset 2-D AR model identification for describing textures
TL;DR: This paper addresses the problem of identification of appropriate autoregressive (AR) components to describe textural regions of digital images by a general class of two-dimensional (2-D) AR models by using singular value decomposition and orthonormal with column pivoting factorization techniques.
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Relationship between surface froth features and process conditions in the batch flotation of a sulphide ore
TL;DR: In this article, the effect of high intensity conditioning on the batch flotation of a sulphide ore from the Merensky reef in South Africa was investigated, and the significantly beneficial effect of the high intensity condition on the performance of the flotation was clearly reflected in the smaller bubble size distributions and greater stability of the froths.
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Modeling the Relationship between Froth Bubble Size and Flotation Performance Using Image Analysis and Neural Networks
TL;DR: In this article, the relationship between process conditions and the surface bubble size as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled by neural networks.
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An improved method to estimate the fractal dimension of colour images
TL;DR: The improved differential box counting method is applied to the 24 bit representation of RGB color images to extract the roughness of color images and showed that the proposed method is able to capture the accurate sharp variation of roughness as compared to the existing methods.