Evaluation of Particle Size Distribution Using an Efficient Approach Based on Image Processing Techniques

2018 ◽  
Vol 43 (S1) ◽  
pp. 429-441 ◽  
Author(s):  
Majid Damadipour ◽  
Mehdi Nazarpour ◽  
Mohammad Taghi Alami
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Image Processing Techniques ◽  
Efficient Approach ◽  
Processing Techniques

Development of Method of Rapid Analysis of Particle Size Distribution of the Coal Charge Based on Digital Image Processing

2015 ◽  
Vol 770 ◽  
pp. 512-517 ◽  
Author(s):  
O.V. Tailakov ◽  
M.P. Makeev ◽  
A.N. Kormin ◽  
A.I. Smyslov
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Digital Image Processing ◽  
Fractional Composition ◽  
Rapid Analysis ◽  
Optical Images ◽  
Coal Charge ◽  
Filtration Properties

Therein algorithms of application of digital models for evaluation of porosity and fractional composition of coals based on analysis of their optical images are offered. The models allow allocating significant informational objects and estimation of structural and filtration properties of coals. The results of algorithms application on recognition of the optical images of coals are presented, the particle size distribution of coal charge and porosity of coal is defined.

Study on Soil Particle Size Distribution by Digital Image Processing

2013 ◽  
Vol 788 ◽  
pp. 627-630
Author(s):  
Jian Shu Hou
Keyword(s):  
Mechanical Property ◽  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Digital Image ◽  
Traditional Method ◽  
Processing Method ◽  
Distribution Analysis ◽  
Processing Technologies

The particle size distribution of soil is very importantto its physical and mechanical property. The ordinary method of the particlesize distribution analysis is based on shaking the soil through a set of sieves.But it will be difficult to use the method while there have particles largerthan the biggest aperture of the size sieves. Then the digital image processingwas used to solve the problem here. The processing technologies, such as imageanalysis and enhancement, deblurring, edge detection were studied to analyzethe image of soil particles. Then the image processing method was used to getthe particle size distribution accurately. Though some promotions need to becarried out in the further study, it is can be found that the image processingmethod is more efficiently than the traditional method.

Particle size distribution in oil-water dispersions using image processing

1987 ◽  
Vol 11 (4) ◽  
pp. 435-439 ◽  
Author(s):  
A.H. Kamel ◽  
S.A. Akashah ◽  
F.A. Leeri ◽  
M.A. Fahim
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Oil Water

scholarly journals Development and Validation of an Online Analyzer for Particle Size Distribution in Conveyor Belts

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 581
Author(s):  
Claudio Leiva ◽  
Claudio Acuña ◽  
Diego Castillo
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Energy Consumption ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Large Particle ◽  
Equivalent Diameter ◽  
Online Measurement ◽  
Particle Detection ◽  
Detection Algorithms

Online measurement of particle size distribution in the crushing process is critical to reduce particle obstruction and to reduce energy consumption. Nevertheless, commercial systems to determine size distribution do not accurately identify large particles (20–250 mm), leading to particle obstruction, increasing energy consumption, and reducing equipment availability. To solve this problem, an online sensor prototype was designed, implemented, and validated in a copper ore plant. The sensor is based on 2D images and specific detection algorithms. The system consists of a camera (1024p) mounted on the conveyor belt and image processing software, which improves the detection of large particle edges. The algorithms determine the geometry of each particle, from a sequence of digital photographs. For the development of the software, noise reduction algorithms were evaluated and selected, and a routine was designed to incorporate morphological mathematics (erosion, dilation, opening, lock) and segmentation algorithms (Roberts, Prewitt, Sobel, Laplacian–Gaussian, Canny, watershed, geodesic transform). The software was implemented (in MatLab Image Processing Toolbox) based on the 3D equivalent diameter (using major and minor axes, assuming an oblate spheroid). The size distribution adjusted to the Rosin Rammler function in the major axis. To test the sensor capabilities, laboratory images were used, where the results show a precision of 5% in Rosin Rambler model fitting. To validate the large particle detection algorithms, a pilot test was implemented in a large mining company in Chile. The accuracy of large particle detection was 60% to 67% depending on the crushing stage. In conclusion, it is shown that the prototype and software allow online measurement of large particle sizes, which provides useful information for screening equipment maintenance and control of crushers’ open size setting, reducing the obstruction risk and increasing operational availability.

scholarly journals Determination of surface moisture and particle size distribution of coal using online image processing

2020 ◽  
Vol 56 (1) ◽  
pp. 37-46
Author(s):  
P. Choudhary ◽  
T. Maloo ◽  
H. Parida ◽  
P. Khatri ◽  
B. Deo ◽  
...  
Keyword(s):  
Image Processing ◽  
Particle Size ◽  
Moisture Content ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Sponge Iron ◽  
Coal Bed ◽  
Processing Plant ◽  
Surface Moisture ◽  
Moisture Variation

Production of sponge iron requires iron ore, coal, and dolomite. The quality of sponge iron is affected by particle size variation and moisture content of the feed materials. In the present work, image processing was used to detect both particle size and moisture variation of the feed materials on an online basis. Noise and signal irregularities in images were removed by image analysis through MATLAB. Continuous (online, every 30 minutes) images were taken over a coal bed which was moving on a conveyor belt. It was a challenge to determine the particle size distribution and surface moisture of coal online. The distribution of reflectivity of coal in the image varied according to the moisture content and particle size. It affected the intensity information of the image which was then used to predict the surface moisture content of the coal. The method is now being used successfully in a processing plant.

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