LAUSR

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.