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
Concentrate Grade Prediction in an Industrial Flotation Column Using Artificial Neural Network
TL;DR: In this article, a new approach has been proposed for metallurgical performance prediction in flotation columns using ANNs, and a case study is carried out in an industrial Metso Minerals CISA flotation column (4 m in diameter and 12 m in height) at the Sarcheshmeh Copper Concentrator Plant.
Journal ArticleDOI
Feature selection in froth flotation for production condition recognition
TL;DR: A feature selection strategy based on the minimal-redundancy-maximal-relevance criterion (mRMR) is used to find the most useful but less redundant features for froth flotation images and it is found that hue and energy of high frequency play significant roles in classification of flotation froth images.
Book ChapterDOI
Multivariate Image Analysis in Mineral Processing
TL;DR: Multivariate image analysis as well as Multiresolution analysis have been shown to be very efficient methods for spectral/textural analysis of process images and could be used for developing new vision sensors for advanced control of mineral processing plants.
Journal ArticleDOI
Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm
TL;DR: In this article, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN), which can be used to predict the Cu and Mo grades and recoveries.
Journal ArticleDOI
Multivariate Image Analysis (MIA) for Industrial Flare Combustion Control
TL;DR: In this article, a multivariate regression model based on flare color images was used to predict the flare performance over a range of operating conditions for steam-assisted flares, and the results showed that simple two-dimensional color images of industrial flares may be a fast, accurate, and inexpensive approach for online monitoring of these industrial combustion systems.