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
Reads0
Chats0
TLDR
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
More filters
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
More filters
Journal ArticleDOI
Soft Sensor Model for Coal Slurry Ash Content Based on Image Gray Characteristics
TL;DR: Based on image gray features, coal slurry ash content soft sensor models are developed by using BP Neural Network and simple eigenvalue-based Least Square Regression Method (LS), respectively as discussed by the authors.
Journal ArticleDOI
A Fractal Characterisation of the Structure of Coal Froths
TL;DR: In this article, the Sierpinski fractal was used to characterize coal froths and a significant parameter appeared to be the breakpoint between these two regions, which represents the ratio of larger to smaller bubbles within the froth, and its potential as an important indicator in a control strategy.
Journal ArticleDOI
Evaluation of models for air recovery in a laboratory flotation column
TL;DR: In this paper, several models were compared with the results obtained by image analysis to estimate the fraction of air that overflows to the concentrate as unbroken bubbles (air recovery, a) and concentrate recovery.
Journal ArticleDOI
Flotation froth image texture extraction method based on deterministic tourist walks
TL;DR: The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production.