• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.149-153
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• 2001

A study on the particle growth in $TEOS/O_2$ plasma was performed, and particle size and its distribution was measured by the electrical aerosol analyzer (EAA), light scattering particle size analyzer and the particle size was also determined by SEM. The effects of process variables such as total gas flow rate, reactor pressure, supplied power and initial reactant concentration on the particle growth were investigated. From the EAA results, the particle size distribution is divided into three groups of the cluster size and the small and large size particles. The particle size distribution measured by the light scattering particle size analyzer becomes bimodal, because the cluster size particles smaller than 20 nm in diameter cannot be detected by the light scattering particle size analyzer. The size of particles measured by the light scattering particle size analyzer is in good agreements with those by the SEM. Also we could understand that the particle formation is very sensitive to the changes of reactor pressure and reactant concentration. As the total gas flow rate increases, the particle size decreases because of the shorter residence time. As the reactor pressure, or the reactant concentration increases, the particle concentration increases and the particles grow more quickly by the faster coagulation between particles.