Open AccessIdentifying morphological characteristics of solid particles based on digital image analysis
Author(s) -
A.V. Ogurtzov,
Yu.V. Khokhlova,
Vadim E. Mizonov,
V.A. Ogurtzov
Publication year - 2020
Publication title -
vestnik igèu
Language(s) - English
Resource type - Journals
ISSN - 2072-2672
DOI - 10.17588/2072-2672.2020.3.064-070
Subject(s) - image processing , sieve analysis , digital image processing , sieve (category theory) , digital image , texture (cosmology) , particle size , materials science , particle (ecology) , particle size distribution , computer science , artificial intelligence , computer vision , mathematics , image (mathematics) , engineering , nanotechnology , combinatorics , chemical engineering , oceanography , geology
Enriched coal as an energy source has a number of undeniable environmental and economic advantages, and has a higher calorific value. Important characteristics that affect the technological process of coal flotation are the particle size distribution, the shape of the coal particles and their surface texture. Screen analysis is the most common method for determining the granulometric composition of granular media. However, it is not sensitive to the shape of the particles and the nature of their surface. With the sieve method of analysis, there is no direct measurement of any axes of the particle, except the case when the particle is ball-shaped. In this case, its size coincides with the edge length of the square mesh of the sieve. Thus, two particles of completely different shapes can pass through the same sieve opening. Our task was to develop a simple technique that allows identifying the shape and texture parameters of particles of the bulk material. To solve the problem in question, the algorithm implemented in MATLAB (Image Processing Toolbox) and the concept of fractals are used. A method for estimating the shape and texture of bulk material particles based on digital image processing has been proposed. The particle size distribution curves are constructed, obtained by sieving method and image processing method. It was found that these curves are in good agreement with each other. Digital image processing is an alternative to identifying the important quality characteristics of bulk materials.