Machine-Learning Classification of Solids Mass Flow Rate in Gas–Solid Pneumatic Conveying Using Electrostatic Sensor Measurements
Year: 2026
Type: article
Abstract: Electrostatic signals in pneumatic conveying depend on solids mass flow rate, air velocity, particle distribution and material, complicating direct estimation. This study classified five nominal solids mass flow rates (0.1–10 g/s) for wheat flour and ground coffee at four air velocities (15–30 m/s) using six ring electrodes and seven arc segments with guarded electrometer-grade front ends. From 1 s blocks, 29 time-, frequency- and time–frequency-domain features per channel were extracted. The Common Vector Approach (CVA), support vector machines (SVMs), multilayer perceptrons (MLPs), random forests (RFs) and feature-based convolutional neural networks (CNNs) were compared with an end-to-end CNN on raw signals. With one feeding cycle per record held out, the best balanced accuracies were 86.9% (flour) and 89.6% (coffee), and 97.8–100% of predictions were within one class. RF regression yielded typical error factors of 1.34 (flour) and 1.31 (coffee). Depending on the classifier, balanced accuracy declined to 47–70% for unseen air velocities and 26–59% across materials; a no-flow control suggested possible contributions from record-specific offsets to the accuracy. These findings restrict the reported performance to the tested conditions and support velocity-range coverage, material-specific calibration and recording-order controls.
Source: Sensors
DOI: 10.3390/s26217085
Authors: Özcan Kamışlı, Mehmet Bilginer Gülmezoğlu
Institution: Eskişehir Osmangazi University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225616807
FWCI:
Cites:
Cited by:
Related to: