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Particle detection in slurry using optical visualization

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Abstract

Identifying large particles are a prerequisite for slurry health monitoring. This study presents a real-time monitoring system using an infrared coaxial light source to detect large particles in slurry. The imaging system acquires real-time images under infrared light, which are processed and analyzed to quantify the particles within the fluid flowing through the monitored area. The proposed approach consists of different illumination modes—fixed, random, and continuous. For particles with regular and irregular shapes, various scintillation modes are applied. The results show that the method is highly feasible and effective, and enables real-time monitoring of the slurry. Moreover, the real-time detection approach for different particle samples, particularly, undiluted opaque fluids, is proposed. The feasibility of the proposed method is verified in the irregular particle detection experiment, where the difference between the detection value and the real value is very small. The accuracy and intuitive properties of the method indicate a massive potential across diverse applications in the forthcoming years.

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Acknowledgements

This work was supported by a National Research Foundation (NRF) of Korea grant (2021R1A2C1007142), the Korea technology and information promotion agency for SMEs (G21S303623501), and the Industrial Fundamental Technology Development Program (20014404, Continuous slurry mixing,…, quality measurement system) funded by the Ministry of Trade, Industry & Energy (MOTIE) of Korea.

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Gyeongsang National University, Jinju, South Korea

    Guangxin Ding, Han Van Nguyen, Yubo Jin, Haojie Xu & Hyoung-Bum Kim

  2. School of Energy and Power Engineering, Jiangsu University, Zhenjiang, China

    Haojie Xu

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  1. Guangxin Ding
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  2. Han Van Nguyen
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  3. Yubo Jin
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Correspondence to Hyoung-Bum Kim.

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Ding, G., Van Nguyen, H., Jin, Y. et al. Particle detection in slurry using optical visualization. J Vis 26, 875–887 (2023). https://doi.org/10.1007/s12650-023-00914-6

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  • DOI: https://doi.org/10.1007/s12650-023-00914-6

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