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arXiv · 2601.02852

ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct

Christoph Wilms·Wenchao Xu·Gizem Ozler·Simon Jantač·Sonja Schmelter·Holger Grosshans
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Abstract

The electrostatic charge acquired by powders during transport through ducts can cause devastating dust explosions. Our recently developed laser-optical measurement technique can resolve the powder charge along a one-dimensional (1D) path. However, the charge across the duct's complete two-dimensional (2D) cross-section, which is the critical parameter for process safety, is generally unavailable due to limited optical access. To estimate the complete powder charge distribution in a conveying duct, we propose a machine learning (ML) approach using a shallow neural network (SNN). The ML algorithm is trained with cross-sectional data extracted from four different three-dimensional direct numerical simulations of a turbulent duct flow with varying particle size. Through this training with simulation data, the ML algorithm can estimate the powder charge distribution in the duct's cross-section based on only 1D measurements. The results reveal an average $L^1$-error of the reconstructed 2D cross-section of 1.63 %.

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Christoph Wilms, Wenchao Xu, Gizem Ozler, Simon Jantač, Sonja Schmelter, Holger Grosshans. 2026-01-06. ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct. https://doi.org/10.1016/j.jlp.2024.105474

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