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An ultra-compact particle size analyser using a CMOS image sensor and machine learning

Engineering and Technology

An ultra-compact particle size analyser using a CMOS image sensor and machine learning

R. Hussain, M. A. Noyan, et al.

Discover a groundbreaking particle size analyzer utilizing a CMOS image sensor and machine learning, developed by Rubaiya Hussain and colleagues. This compact and cost-effective device predicts particle sizes with impressive accuracy, making it ideal for real-time industrial process monitoring.

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Playback language: English
Abstract
This paper introduces a novel, ultra-compact particle size analyzer using a CMOS image sensor and machine learning. The device uses a small form factor angular spatial filter to collect light scattered by particles at discrete angles. A light-emitting diode (LED) and CMOS image sensor array acquire angularly resolved scattering images, which are then used by a machine learning model to predict the volume median diameter of the particles. Testing with glass beads (13-125 µm) showed mean absolute percentage errors of 5.09% (without concentration as input) and 2.5% (with concentration as input). The device's compact size and low cost make it suitable for applications outside of standard laboratories, such as online industrial process monitoring.
Publisher
Light: Science & Applications
Published On
Authors
Rubaiya Hussain, Mehmet Alican Noyan, Getinet Woyessa, Rodrigo R. Retamal Marín, Pedro Antonio Martinez, Faiz M. Mahdi, Vittoria Finazzi, Thomas A. Hazlehurst, Timothy N. Hunter, Tomeu Coll, Michael Stintz, Frans Muller, Georgios Chalkias, Valerio Pruneri
Tags
particle size analyzer
CMOS image sensor
machine learning
scattered light
industrial monitoring
volume median diameter
glass beads

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