ABSTRACT

Accurate detection for pulverized coal injection (PCI) blockage and coke size distribution are effective for blast furnace (BF) operation. Nowadays, tuyere cameras have been applied in BF. However, the appearance of captured raceway images changes, which requests the detection method to be adaptive. Therefore, an intelligent detection method for PCI blockage and coke size distribution is proposed. An adaptive pre-processing algorithm is firstly developed to improve image quality. Secondly, the fitting circle is used to locate tuyere region and the improved U-net network is investigated for lance segmentation to construct the background template. Then, target regions can be obtained by background subtraction algorithm. Finally, the PCI blockage can be detected according to the area information, and the size distribution of cokes can be calculated by multi-directional Feret diameter. Massive raceway videos are used to evaluate the method. Experiment results show the method can detect PCI blockage and cokes size distribution.

摘要

准确检测喷煤 (PCI) 堵塞和焦炭粒度分布对于高炉 (BF) 运行是有效的。如今，风口相机已应用于高炉。然而，捕获的轨道图像的外观会发生变化，这就要求检测方法具有自适应性。因此，提出了一种PCI堵塞和焦炭粒度分布的智能检测方法。首先开发了一种自适应预处理算法来提高图像质量。其次，利用拟合圆定位风口区域，研究改进的U-net网络进行长矛分割，构建背景模板。然后，可以通过背景减法算法获得目标区域。最后可以根据区域信息检测PCI阻塞，焦炭的粒度分布可以通过多向Feret直径计算。大量跑道视频用于评估该方法。实验结果表明，该方法可以检测PCI堵塞和焦炭粒度分布。