Soot propensity by image magnification and artificial intelligence

Abstract

This paper presents the results of two novel approaches to measure the soot propensity of a flame and their comparison with the Line of Sight Attenuation (LOSA) method. Both approaches are based on the detection of the Smoke Point Height (SPH), concept used to determine when a flame is in a sooting state. The first approach is based on the detection of morphological changes in the flame, identified through their amplification via the Eulerian Video Magnification algorithm. Results show an effective amplification of the flame geometry, allowing the visualization of variations on the flame tip unable to be detected by the naked human eye and therefore the detection of SPH. The second approach is based on the application of Artificial Intelligence models to classify flame images regarding their sooting propensity, taking advantage of the knowledge acquired from a referential data set. Both approaches provide an accurate classification when compared to the conventional method of LOSA. Furthermore, both approaches show a greater implementation potential in practical combustion devices than the conventional method of LOSA, due to their reduced hardware and technical requirements.