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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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
Simple relationships for predicting the recovery of liquid from flowing foams and froths
TL;DR: In this paper, the authors present some theoretical relationships for the prediction of water recovery from a flowing foam and demonstrate that the amount of water collected is independent of the burst rate and the rate is proportional to the gas rate squared and inversely proportional to bubble diameter squared.
Journal ArticleDOI
Application of image processing and radial basis neural network techniques for ore sorting and ore classification
Veerendra Singh,S. Mohan Rao +1 more
TL;DR: In this paper, a novel approach to classify the ores for ferromanganese metallurgical plant feed has been proposed based on the visual texture of the ore particles (Mn, Fe, and Al2O3 rich) and radial basis neural network.
Journal ArticleDOI
Multivariate image analysis in the process industries: A review
TL;DR: An overview of the history, methods and applications of multivariate image analysis methods as developed for use in the process industries is provided and those aspects of the methods that make them suitable for these tasks are discussed.
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The significance of flotation froth appearance for machine vision control
TL;DR: In this paper, the authors provide a clear framework and motivation for the development of a machine vision system for flotation control, based on appearance and metallurgical significance, and an example of how process deviations can be related to froth appearance is provided.
Journal ArticleDOI
Flotation froth image recognition with convolutional neural networks
Y. Fu,Chris Aldrich +1 more
TL;DR: Three pretrained neural networks architectures to estimate froth grades from industrial image data, namely AlexNet, VGG16 and ResNet is considered and, in its pretrained format, AlexNet outperformed previously proposed methods by a significant margin.