Molten Steel Level Detection From Thermal Image Sequence Based on the Characteristics of Adhesive Flux

High-temperature medium and time-varying covering flux lead to the difficulty of molten steel level measurement. For the measurement, in our previous work, a novel principle by using temperature gradient was proposed by us, and a refractory sensor was inserted into the metallurgical container to sense the temperature gradients of the flux and the molten steel. However, sometimes liquid adhesive flux on the sensor surface disables the extraction of true temperature gradients. To fix this problem, two new models, the adhesion thickness model and the adhesion flowability model, which are inspired by the adhesion mechanism of the flux, are proposed to detect the steel-flux interface. A unified approach, sequential clustering of the shapes of the pixel gray-time curves, is introduced to conduct the detection. On this basis, the thermal image sequence with 4-D spacetime information of the sensor is used for clustering. First, gray values of each pixel in the sequence are sorted in the time dimension, and grouping of the pixels in space dimension is done. Then, the region of interest is extracted from the image sequence to remove the invalid pixels, and sequential clustering is conducted with each group. Finally, the confidence of the clustering results is measured and the clustering results with the confidence higher than the threshold are retained to detect the steel-flux interface. By utilizing the two new models, the standard deviation of the measurement errors reduces from 4.8 to 3.7 mm.