Multi-information online detection of coal quality based on machine vision

Abstract Online multi-information detection of mineral properties and composition plays a vital role in the realization of digital mining and digital concentrating mill, and the way of machine vision technology is put forward as a cost-effective and safe approach at present. This paper presents an exploratory study employing a bench-scale approach to detect the multi-information of coal quality online by machine vision simultaneously, including particle size distribution, density distribution, the ash content of each density fraction, and the total ash content. Firstly, we adopt a Finite-Erosion-and-Exact-Dilation (FEED) algorithm and a particle-on-edge region segmentation algorithm to segment overlapped particles and ensure the full analysis of target regions. Moreover, twenty-nine features are extracted and optimized to enable the particle mass estimation model, particle size characterization, classification model of density fraction, and prediction model of ash content to be implemented. Finally, an experimental study shows the merits of the proposed approach, and the average prediction errors of size distribution, density distribution, and ash content of each density fraction are 1.85%, 2.57%, 3.36%, respectively. The total ash content error is 2.54%. Results derived using the proposed approach reveal that it has the potential to be applied to the coal processing industry.

[1]  Julio Cesar Alvarez Iglesias,et al.  Deep learning discrimination of quartz and resin in optical microscopy images of minerals , 2019, Minerals Engineering.

[2]  Snehamoy Chatterjee,et al.  Development of machine vision-based ore classification model using support vector machine (SVM) algorithm , 2017, Arabian Journal of Geosciences.

[3]  O. Gomes,et al.  Automatic characterization of iron ore by digital microscopy and image analysis , 2018, Journal of Materials Research and Technology.

[4]  M. Barolo,et al.  Artificial vision system for particle size characterization from bulk materials , 2017 .

[5]  R. D. Hryciw,et al.  Soil Particle Size and Shape Distributions by Stereophotography and Image Analysis , 2017 .

[6]  J. Gutzmer,et al.  Optimal sensor selection for sensor-based sorting based on automated mineralogy data , 2019, Journal of Cleaner Production.

[7]  Hui-Ling Huang,et al.  ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data , 2007, Biosyst..

[8]  Ergin Gülcan,et al.  A novel approach for sensor based sorting performance determination , 2020 .

[9]  C. Liu,et al.  Prediction of the Ash Content of Flotation Concentrate Based on Froth Image Processing and BP Neural Network Modeling , 2018 .

[10]  C. Igathinathane,et al.  Comparison of particle size distribution of celestite mineral by machine vision ΣVolume approach and mechanical sieving , 2012 .

[11]  Wei Liu,et al.  Froth Image Acquisition and Enhancement on Optical Correction and Retinex Compensation , 2018 .

[12]  Jian-guo Yang,et al.  ANALYSIS OF LARGE PARTICLE SIZES USING A MACHINE VISION SYSTEM , 2013 .

[13]  Zelin Zhang,et al.  An improved estimation of coal particle mass using image analysis , 2012 .

[14]  Claudio A. Perez,et al.  Ore grade estimation by feature selection and voting using boundary detection in digital image analysis , 2011 .

[15]  Snehamoy Chatterjee,et al.  Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model , 2010 .

[16]  Snehamoy Chatterjee,et al.  Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade Prediction , 2019, Pattern Recognition and Image Analysis.

[17]  Eric Pirard,et al.  Mineral recognition of single particles in ore slurry samples by means of multispectral image processing , 2019, Minerals Engineering.

[18]  Jayson Tessier,et al.  A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts , 2007 .

[19]  Chris Aldrich,et al.  Online monitoring and control of froth flotation systems with machine vision: A review , 2010 .

[20]  Marc Pollefeys,et al.  A comprehensive shape analysis pipeline for stereoscopic measurements of particulate populations in suspension , 2017 .

[21]  W. Gui,et al.  Nonlinear modeling of the relationship between reagent dosage and flotation froth surface image by Hammerstein-Wiener model , 2018 .

[22]  Matthew J. Thurley,et al.  Application of laser scanning to measure fragmentation in underground mines , 2017 .

[23]  D. W. Moolman,et al.  The identification of perturbations in a base metal flotation plant using computer vision of the froth surface , 1997 .

[24]  M. J. Keyser,et al.  Online Analysis of Coal on A Conveyor Belt by use of Machine Vision and Kernel Methods , 2010 .

[25]  Weihua Gui,et al.  Recognition of flooding and sinking conditions in flotation process using soft measurement of froth surface level and QTA , 2017 .

[26]  Dongyang Dou,et al.  Ash content prediction of coarse coal by image analysis and GA-SVM , 2014 .

[27]  Tobias Andersson,et al.  A machine vision system for estimation of size distributions by weight of limestone particles , 2012 .

[28]  D. Chakravarty,et al.  Development of a mass model in estimating weight-wise particle size distribution using digital image processing , 2017 .

[29]  Snehamoy Chatterjee,et al.  Image-based quality monitoring system of limestone ore grades , 2010, Comput. Ind..

[30]  Morteza Mohammadi Zanjireh,et al.  Detecting the Minerals’ Ore Grade Using the Emotional Network and Image Processing Techniques , 2018 .

[31]  Mohammad Hamiruce Marhaban,et al.  Development of a machine vision system for real-time monitoring and control of batch flotation process , 2017 .

[32]  M. Massinaei,et al.  Estimation of particle size distribution on an industrial conveyor belt using image analysis and neural networks , 2014 .

[33]  Chris Martin,et al.  Techniques and applications for predictive metallurgy and ore characterization using optical image analysis , 2008 .

[34]  Mahdi Khodadadzadeh,et al.  Evaluating the performance of hyperspectral short-wave infrared sensors for the pre-sorting of complex ores using machine learning methods , 2020 .

[35]  Matthew J. Metzger,et al.  Effect of particle size distribution on segregation in vibrated systems , 2013 .

[36]  M. Lu,et al.  A cascaded recognition method for copper rougher flotation working conditions , 2018 .

[37]  Chris Aldrich,et al.  Estimation of platinum flotation grades from froth image data , 2011 .

[38]  Jinshan Yang,et al.  The Density Fraction Estimation of Coarse Coal by Use of the Kernel Method and Machine Vision , 2015 .

[39]  Nor Ashidi Mat Isa,et al.  Automated Intelligent real-time system for aggregate classification , 2011 .

[40]  Robben,et al.  Sensor‐Based Ore Sorting Technology in Mining—Past, Present and Future , 2019, Minerals.

[41]  Mohammad Hamiruce Marhaban,et al.  An image segmentation algorithm for measurement of flotation froth bubble size distributions , 2017 .

[42]  J. Hedlund,et al.  Quantitative image analysis of bubble cavities in iron ore green pellets , 2011 .

[43]  Matthew Thurley,et al.  Automated online measurement of limestone particle size distributions using 3D range data , 2011 .

[44]  C. Liu,et al.  The response of diasporic-bauxite flotation to particle size based on flotation kinetic study and neural network simulation , 2017 .

[45]  İ. Can,et al.  Ash content estimation of lignite with visible light and near-infrared sensors , 2020 .

[46]  Ergin Gülcan,et al.  Optical sorting of lignite and its effects on process economics , 2018 .

[47]  Ergin Gülcan,et al.  Evaluation of complex copper ore sorting: Effect of optical filtering on particle recognition , 2018, Minerals Engineering.