Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors (Open Access)--颗粒学报PARTICUOLOGY

Hossein, F., Errigo, M., Cheng, S., Materazzi, M., Lettieri, P., Arcucci, R., & Angeli, P. (2025). Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors. Particuology, 101, 155-165. https://doi.org/10.1016/j.partic.2024.10.005

Acoustic emission and machine learning algorithms for particle size analysis in gas-solid fluidized bed reactors (Open Access)

Fria Hossein^a *, Matteo Errigo^a, Sibo Cheng^b, Massimiliano Materazzi^a, Paola Lettieri^a, Rossella Arcucci^c, Panagiota Angeli^a

a Department of Chemical Engineering, University College London, London, WC1E7JE, UK
b CEREA, Ecole des Ponts ParisTech & EdF R&D, Champs-sur-Marne, 77455, France
c Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ, UK

10.1016/j.partic.2024.10.005

Volume 101, June 2025, Pages 155-165

Received 30 May 2024, Revised 16 August 2024, Accepted 9 October 2024, Available online 18 October 2024, Version of Record 29 May 2025.

E-mail: f.hossein@ucl.ac.uk

Highlights

• A combination of an acoustic emission (AE) techniques with machine learning (ML) for fluid particle flows is developed.

• A theoretical model for the generation of AE signal in gas-solid flow is presented.

• An inversion algorithm to invert AE signal to particle size distribution is reported.

• ML approaches are applied to predict particle sizes based on the AE signal features.

Abstract

In this work, a combination of an acoustic emission (AE) technique and a machine learning (ML) algorithm (Random Forest (RF) and Gradient Boosting Regressor (GBR)) is developed to characterize the particle size distribution in gas-solid fluidized bed reactors. A theoretical approach to explain the generation of acoustic emission signal in gas-solid flows is presented. An AE signal is generated in gas-solid fluidized beds due to the collision and friction between fluidized particles as well as between particles and the bed inner wall. The generated AE signal is in the form of an elastic wave with frequencies >100 KHz and it propagates through the gas-solid mixture. An inversion algorithm is used to extract the information about the particle size starting from the energy of the AE signal. The advantages of this AE technique are that it is a cheap, sensitive, non-intrusive, radiation-free, suitable for on-line measurements. Combining this AE technique with ML algorithms is beneficial for applications to industrial settings, reducing the cost of signal post-processing. Experiments were conducted in a pseudo-2D flat fluidized bed with four glass bead samples, with sizes ranging from 100 μm to 710 μm. AE signals were recorded with a sampling frequency of 5 MHz. The AE signal post-processing and data preparation for the ML process are explained. For the ML process, the AE frequency, AE energy and particle collision velocity data sets were divided into training (60%), cross-validation (20%) and test sets (20%). Two ensemble ML approaches, namely Random Forest and Gradient Boosting Regressor, are applied to predict particle sizes based on the AE signal features. The combination of these two models results in a coefficient of determination (R2) value greater than 0.9504.

Graphical abstract

Keywords

Acoustics; Particle size distribution; Fluidized bed; Inversion; ML; Elastic wave

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