Flame stability analysis of flame spray pyrolysis by artificial intelligence

Pan, Jessica; Libera, Joseph A.; Paulson, Noah H.; Stan, Marius

doi:10.1007/s00170-021-06884-z

Flame stability analysis of flame spray pyrolysis by artificial intelligence

Journal Article · Wed Apr 07 00:00:00 EDT 2021 · International Journal of Advanced Manufacturing Technology

DOI:https://doi.org/10.1007/s00170-021-06884-z· OSTI ID:1798171

Pan, Jessica ^[1]; ^[2]; ^[2]; ^[2]

Princeton Univ., NJ (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

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 labeling 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1798171

Journal Information:: International Journal of Advanced Manufacturing Technology, Journal Name: International Journal of Advanced Manufacturing Technology Journal Issue: 7-8 Vol. 114; ISSN 0268-3768

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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