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

Abstract

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.

Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods

“…Another method used to determine the degree of moisture in a material is image processing. The literature presents applications of vision methods to assess the moisture of soil [ 31 , 32 ], milled rice [ 33 ], loess [ 34 ], coal [ 35 ], and iron ore [ 36 , 37 ]. In [ 31 ], the images were processed to determine the median values in the red, green, and blue bands, as well as the hue, saturation, and value of the HSV color space.…”

“…The authors of [ 34 ] proposed a mathematical model to describe the characteristic curve of water content vs. gray value, which was based on RGB values. In [ 35 ], the authors proposed an algorithm for the online testing of coal. In their solution, it was also possible to determine the size of the particles.…”