Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

Abstract

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of $\pm$ 1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.