Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

The temperature of molten iron at the taphole of blast furnace (BF) is an important parameter that reflects molten iron quality and BF conditions. It is not easy to measure the temperature of molten iron at the taphole in real time. To achieve continuous and accurate detection of molten iron temperature (MIT) at taphole, this paper proposes a temperature measurement and compensation method of molten iron based on infrared computer vision. First, an infrared computer vision system is designed and installed to capture the infrared thermal images of molten iron flow at the taphole. Then, the molten iron flow area is determined by using image processing. Afterward, the temperature information of slag region is obtained to calculate the MIT based on threshold segmentation. Furthermore, considering the measurement error caused by dust, the texture features influenced by dust are extracted based on the defined temperature-level co-occurrence matrix and the neighboring temperature-level-dependence matrix, and a compensation model is established to compensate the measurement error based on ensemble neural network and support vector regression. Finally, considering that the MIT and the slag temperature are approximately the same at the taphole, the MIT at the taphole is acquired according to the slag temperature. Industrial experiments and applications demonstrate that the proposed method can measure the MIT at the taphole continuously and accurately and provide reliable MIT data for operators to control BF.

Accurate temperature measurement of blast furnace molten iron is essential for regulating the furnace temperature. However, the dust interference at blast furnace cast field makes it difficult to measure molten iron temperature accurately by infrared temperature measurement method. To reduce the influence of dust on the results of infrared measurement method, a compensation method is proposed using the heterogeneous features of infrared images. First, the infrared image of molten iron flow is divided into subregions, from which the subregions containing only molten iron are selected. Then, the statistic features and texture features influenced by dust are extracted from the selected subregions. Finally, the heterogeneous features are used as the compensation model input to estimate the measurement error of each subregion and compensate for the molten iron temperature. Experimental results demonstrate that the compensation method can significantly reduce the infrared measurement errors caused by dust and obtain accurate molten iron temperature.

Accurate and real-time 3D burden surface topography of a blast furnace plays an important role in improving the gas flow distribution and optimizing the charging operation, and therefore has great potential to reduce both its CO2 emissions and energy consumption. However, because of the high-temperature, dusty and dark environment inside the furnace, as well as limitations in terms of installation position and viewing angle of the image capturing equipment, it is difficult to obtain clear, continuous and complete images of the burden surface and reconstruct accurate 3D topography in real time. To overcome these limitations, a novel 3D topography measurement and completion method of the burden surface is proposed in this study. First, a high-temperature industrial endoscope is designed and installed to capture the burden surface images during the blast furnace ironmaking process. Then, the details of the burden surface images are enhanced using image sharpening methods and the regions of interest of the images are extracted based on texture energy features. Next, a method based on the depth from defocus is used to calculate the depth of burden surface. Finally, considering that most of the images are incomplete because of the limitations above, a new 3D completion model is established to complete the burden surface topography based on a variational auto encoder and a generative adversarial network. On-site experiments and their validation demonstrate that the proposed method can obtain complete and accurate 3D burden surface topography and provide highly reliable topography data to optimize blast furnace operations.