An Acoustic Hotspot Tracking Algorithm for Highly Centralized Gas Temperature Distribution

Acoustic pyrometer is expected to be a useful noninvasive method for monitoring gas temperature distribution inside a steel-making furnace. On the superficial layer above the burden of a blast furnace, most of the high-temperature gas is concentrated near the center, and tracking the position of the hotspot is critical for productivity. However, most of the existing acoustic temperature distribution reconstruction algorithms are developed with relatively uniform temperature distribution environments. Besides, their capabilities of tracking the pinnacle of temperature distribution in the region of interest (ROI) are rarely discussed. In this research, a reconstruction method of acoustic temperature tomography dedicated for highly centralized gas temperature distribution is proposed and demonstrated. The key metrics include the reproducibility of 2-D temperature distribution, the sensitivity of hotspot shift, and the accuracy of point-to-point (P2P)/peak temperature. To optimize the result of each metric, previous approaches of acoustic temperature tomography have been first evaluated. Then, the investigation of effects from the shape and size of meshes is proceeded to improve the performance. After that, a novel method to address convergence issues while using the iterative method is introduced. Consequently, the reconstruction method proposed in this article could effectively visualize the temperature map while hotspot moves to different locations. It could also sense the occurrence of a hotspot (2.56% of ROI) traveled from center to 1% of ROI’s diameter. Moreover, a competitive accuracy with 5.89% and 1.46% error at P2P root-mean-square (rms) and peak temperature is achieved, respectively. Finally, a practical acoustic 2-D pyrometer consisted of 12 ultrasonic transducers arranged in a circular pattern with a 1-m width of ROI successfully detected the shift of a hotspot when the displacement of a heater reaches 5 cm.