  Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles
through the combustion of an atomized precursor solution; this process has
applications in catalysts, battery materials, and pigments. Current limitations
revolve around understanding how to consistently achieve a stable flame and the
reliable production of nanoparticles. Machine learning and artificial
intelligence algorithms that detect unstable flame conditions in real time may
be a means of streamlining the synthesis process and improving FSP efficiency.
In this study, the FSP flame stability is first quantified by analyzing the
brightness of the flame's anchor point. This analysis is then used to label
data for both unsupervised and supervised machine learning approaches. The
unsupervised learning approach allows for autonomous labelling and
classification of new data by representing data in a reduced dimensional space
and identifying combinations of features that most effectively cluster it. The
supervised learning approach, on the other hand, requires human labeling of
training and test data, but is able to classify multiple objects of interest
(such as the burner and pilot flames) within the video feed. The accuracy of
each of these techniques is compared against the evaluations of human experts.
Both the unsupervised and supervised approaches can track and classify FSP
flame conditions in real time to alert users of unstable flame conditions. This
research has the potential to autonomously track and manage flame spray
pyrolysis as well as other flame technologies by monitoring and classifying the
flame stability.
