Image-Based Flame Detection and Combustion Analysis for Blast Furnace Raceway

Blast furnace tuyere is an important “window” between the inside and outside of blast furnace. Stable and accurate detection of the combustion condition of tuyere raceway plays a significant role in maintaining longevity, safety, and smooth of blast furnace. The adiabatic combustion temperature as a traditional measurable indicator of the raceway combustion condition considers the raceway temperature as a constant. It cannot explain the productivity and quality fluctuations of iron in practical production while the operation parameters are fixed and unchanged. Thus, this paper presents the image-based flame detection system specific to the blast furnace raceway to research the actual combustion condition in the raceway. The system mainly consists of an optical detector to capture raceway images and a digital image processing unit to extract flame features. In this paper, the nonlinear partial least-squares colorimetry and Monte Carlo method with iterative optimization are developed to obtain the temperature distribution of the raceway. Power spectral analysis is used to extract the flame flicker frequency. Massive raceway images captured from 15 raceways of a 2500-m3 blast furnace were used to analyze raceway flames. The results demonstrate that temperature distribution of the raceway can fluctuate in a wide range and considerable temperature nonuniformity exists among the 15 raceways. This case can explain why productivity and quality of iron are unstable under the same operation parameters. And, the detection system performs well in revealing the nature of the raceway combustion condition.