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
Performance of Convolutional Neural Networks for Feature Extraction in Froth Flotation Sensing
TL;DR: Convolutional Neural Networks mitigates the curse of dimensionality inherent in fully connected networks but must be trained, unlike other feature extractors, which allows both textural and spectral features to be discovered and utilised.
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Machine vision based monitoring and analysis of a coal column flotation circuit
TL;DR: In this paper, a machine vision system is successfully developed and implemented in a coal column flotation circuit, which can be used for diagnosing the process conditions as well as predicting the process performance at different operating conditions.
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Machine vision based monitoring of an industrial flotation cell in an iron flotation plant
TL;DR: In this paper, a machine vision system was installed on a rougher circuit of an iron flotation plant to monitor the process at different conditions, including bubble size distribution, number of bubbles, froth velocity and stability.
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Interpretation of the effect of froth structure on the performance of froth flotation using image analysis
TL;DR: In this paper, the authors investigated the possibility of regulating the performance of a flotation cell by using image analysis to define a desired bubble size in the concentrate, and subsequently to use measured deviations to control bubble coalescence by the compensating addition of surfactants.
Journal ArticleDOI
Integrated prediction model of bauxite concentrate grade based on distributed machine vision
TL;DR: Considering the unity, locality and inaccuracy of existing prediction methods of concentrate grade based on machine vision, a distributed machine vision system of bauxite flotation process is built in this article.