7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

High-Rate Production of Nanomaterials using Modulated Induction ThermalPlasmas and its Optimization by Machine Learning
Yasunori Tanaka1
1
Faculty of Electrical, Information and Communication Eng., Kanazawa University
e-mail(speaker): tanaka@ec.t.kanazawa-u.ac.jp

Nanoparticles/nanopowders have garnered significant
attention across various fields, including electronics,
biomedical, energy, and environmental applications,owing to their unique physical and/or chemical
properties. As the demand for these materials increases
and their applications become more widespread, thedevelopment of efficient mass production methods has
become crucial. In this regard, we have devised a novel
approach for nanoparticle synthesis using a combinationof pulse-modulated induction thermal plasma (PMITP)
and time-controlled feeding of feedstock (TCFF), which
we refer to as the "PMITP+TCFF method." The PMITP
sustains a thermal plasma field through coil current
modulation in a rectangular waveform, resulting inperiodic variations between higher-temperature andlower-temperatureplasmastates.
TheTCFFcomplements this process by synchronously andintermittently supplying feedstock, further enhancing thesynthesis efficiency [1]. At 20 kW power, the PMITP+TCFF method achieves exceptional production rates,
yielding 400 g/h of Al3+-doped TiO2 nanopowder, 720
g/h of Fe3+-doped TiO2 nanopowder, and 180 g/h of Si
nanoparticles/nanowires, among other examples [2,3].
Building on these advancements, the authors havefurther developed the "Tandem-MITP+TCFF method" toachieveevenhigherproduction
ratesfornanoparticles/nanomaterials [4]. The tandem-MITP
involves two independent coils arranged in a single
plasma torch to generate a long axial thermal plasma
field. The upper coil ensures the robustness and stabilityof the thermal plasma, while the lower coil sustains asignificantly modulated thermal plasma state. Utilizing
this approach, we have successfully synthesized a large
quantity of nanoparticles at production rates exceeding
several hundred g/min in experimental settings [4].
The Tandem-MITP+TCFF approach demonstrates apromising avenue for the high-rate production ofnanoparticles and nanomaterials. These innovations holdimmense potential for meeting the escalating demandand promoting the widespread utilization of thesematerials in diverse applications. The efficient mass
production of nanoparticles is a crucial step towards
harnessing their full potential in shaping futuretechnologies and addressing various global challenges.
However, in Tandem-MITP+TCFF method, there are alarge number of parameters that should be controlled,
and it is not easy to optimize them. On the other hand,the authors have developed a numerical thermofluid
model for the thermal plasma field, the behavior of raw
material particles including heating, melting, andevaporation, and nanoparticle production and transport in
the Tandem-MITP + TCFF method [5]. Using this model,
we have investigated the effects of the controlledparameters on nanoparticle production. We also studied

the optimization of operating conditions for the main
control parameters of nanoparticle production using theTandem-MITP + TCFF method by applying a numerical
fluid dynamics model and machine learning techniques.
In this study, the sequential approximate multi-objective
optimization method (SAMOO) with radial basis
function-neural network (RBF-ANN) was used [6], andthe modulation conditions that generate "a largeramount" of Si nanoparticles with smaller diameter withfewer trial calculations were examined [7]. In this study,
the amplitude modulation of the upper and lower coilcurrents were both set to be square waves (PM) with aperiod of 15 ms, and the following nine control
parameters were used as control parameters with limited
ranges: upper input power time averaged 10-20 kW, duty
ratio DFupper of the upper coil current, modulation degree
SCLupper, and lower coil currents DFlower and SCLlower
were set independently to 0-100%. The feedstock wasfed intermittently, and the DFfeed of the feedstock feed
was also varied from 0-100% with a delay phase time oftfeeddelay=0-15 ms. The downstream cooling gas (QG) was
also introduced intermittently, with feed DFQG = 0-100%,
delay phase time tQG = 0-15 ms, and flow rate QQG =0-100 L/min.
One optimal condition obtained is as follows: Pupperin=12 kW, SCLupper= 99%, DFlower= 72%, SCLlower= 28%,DFfeedvalve= 43%, tfeeddelay= 6.5 ms, QQG=95 slpm,DFQGvalve=61%, and tQGdelay= 13 ms. The upper coilcurrent is unmodulated, while the lower coil current ishighly modulated, resulting in large fluctuations in thetemperature and fluid fields. In this modulated plasma,
the material is introduced into the hot thermal plasma by
feeding the material in the latter half of the on-timeperiod, and steady evaporation is obtained in accordancewith the movement of the material. Efficient cooling isachieved by introducing cooling gas in cycles at thetiming when the evaporated vapor reaches thedownstream.
References[1] N. Kodama, et al., J. Phys. D: Appl. Phys. 47, 195304(2014).
[2] K. Kita, et al., 12th Asia Pacific Conf. Plasma
Technol. & 27th Symp.Plasma Sci. for Materials(APCPST12/SPSM27) (2014)[3] Y.Ishisaka, et al., Applied Physics Express, 10,096201 (2017)[4] R.Furukawa, et al, J.Phys.D:Appl.Phys. 55, 044001(2022)[5] R.Furukawa, et al, Powder Technol., 389, 460(2021)[6] S. Kitayama, et al., Strict Multidisc Optim., 48, 501(2013)[7] Y.Tanaka, et al., Int.Symp.Plasma Chem. (ISPC-25),(2023)