Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection

Meng Li*, Xu Wang, Hao Yao, Henrik Saxén, Yaowei Yu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
104 Downloads (Pure)


Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant.
Original languageEnglish
Article number1902
Number of pages15
Issue number10
Publication statusPublished - 20 Sept 2022
MoE publication typeA1 Journal article-refereed


  • metallurgical coke
  • particle size distribution
  • object detection
  • YOLOv3


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