Flame stability analysis of flame spray pyrolysis by artificial intelligence
نویسندگان
چکیده
Flame spray pyrolysis (FSP) is a process used to synthesize nanoparticles through the combustion of an atomized precursor solution; this has applications in catalysts, battery materials, and pigments. Current limitations revolve around understanding how consistently achieve stable flame reliable production nanoparticles. Machine learning artificial intelligence algorithms that detect unstable conditions real time may be means streamlining synthesis improving FSP efficiency. In study, stability first quantified by analyzing brightness flame’s anchor point. This analysis then label data for both unsupervised supervised machine approaches. The approach allows autonomous labeling classification new representing reduced dimensional space identifying combinations features most effectively cluster it. approach, on other hand, requires human training test but able classify multiple objects interest (such as burner pilot flames) within video feed. accuracy each these techniques compared against evaluations experts. Both approaches can track alert users conditions. research potential autonomously manage well technologies monitoring classifying stability.
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ژورنال
عنوان ژورنال: The International Journal of Advanced Manufacturing Technology
سال: 2021
ISSN: ['1433-3015', '0268-3768']
DOI: https://doi.org/10.1007/s00170-021-06884-z