[17p-K303-1~8] 8.1 Plasma production and diagnostics

3:00 PM - 3:15 PM JST(6:00 AM - 6:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[17p-K303-5] Machine Learning Exploration of Optimal Control Parameters in Different Modulation Cycles of Tandem Pulse-Modulated Induction Thermal Plasmas for Nanoparticle Synthesis

〇Rio Okano¹, Yasunori Tanaka¹, Yusuke Nakano¹, Tatsuo Ishijima¹, Satoshi Kitayama¹, Shiori Sueyasu², Shu Watanabe², Keitaro Nakamura² (1.Kanazawa Univ., 2.Nisshin Seifun Group Inc.)

Keywords:

thermal plasma,nanoparticle synthesis,machine learning

In this study, using a numerical analysis model of nanoparticle synthesis via a tandem-type pulse-modulated inductively coupled thermal plasma with intermittent feedstock feeding (Tandem-PMITP+TCFF method), we optimized control parameters to maximize nanoparticle yield and minimize the average particle size through machine learning-based optimization. The optimization was performed for different modulation cycles, and the results indicated that the key control parameters vary for each modulation cycle, suggesting that a unique approach may be required for each case.