Mr. Yoshiyuki Umegaki Profile

Yoshiyuki Umegaki

at JFE Steel Corp

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Proceedings Article | 23 September 2014 Paper

Classification and quantification of suspended dust from steel plants by using color and transmission image analysis

Yoshiyuki Umegaki, Akira Kazama, Yoshinori Fukuda

Proceedings Volume 9217, 92170G (2014) https://doi.org/10.1117/12.2058757

KEYWORDS: Particles, Image transmission, Scattering, Image processing, Statistical analysis, Silica, Image classification, Image analysis, Adhesives, Chromium

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Some kind of dust can arise from ironmaking and steelmaking processes in steel works. In JFE Steel's steel plants, various measures to prevent the suspended dust from scattering to the surrounding area have been taken. To take effective preventive measures against the dust scattering, it’s important to identify dust sources and scattering routes by much observation and analysis of the dust particles. Conventionally, dust particles were sampled at many observation points in and around JFE’s plants and the amount of particles of each kind was measured visually through a microscope. In such a way, however, the operation is inefficient to measure many dust samples, and also the accuracy of the results depends on the operator. To achieve efficient, operator-independent measurement, a system that can classify and quantify the dust particles automatically has been developed [1]. The system extracts particles from color images of the dust and classifies the particles into three color types – black particles (coke, coal), red particles (iron ore, sintered ore) and white particles (slag, lime). These processes are done basically in the YCrCb color space, where colors are represented by luminance (Y) and chrominance (Cr and Cb). The YCrCb color space is more manageable than the RGB color space to distinguish the three color types. The thresholds for the classification are automatically set on the basis of the mean values of the luminance and chrominance in each image. This means there is no need to tune the thresholds to each image manually. This scheme makes the results independent of operators. Quick analysis is also realized because what the operators have to do is to capture the images of the dust and the analysis is fully automated. Classification results of the sampled particles by the developed system and the obtained statistics in terms of the color type, approach direction and diameter are shown.

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