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Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Citation Link: https://doi.org/10.15480/882.18699
Publikationstyp
Journal Article
Date Issued
2026-09-28
Sprache
English
TORE-DOI
Journal
Article Number
109911
Citation
Computers & Chemical Engineering : 109911 (2026) (in Press; CC BY 4.0)
Publisher DOI
Publisher
Elsevier
Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We explore a setup where in-silico sensors, based on a recently proposed real-world design, are tracked using data from an accelerometer and magnetometer available from a built-in inertial measurement unit. Filtering algorithms, using a bespoke dynamical model, are used to process these readings into position estimates. We compare tracking performance of an extended Kalman filter, a particle filter and the unscented Kalman filter implemented in the pykalman library. Our numerical experiments track in-silico particles moving in an analytically given threedimensional vortex as well as in the experimentally measured flow-field of a lab-scale stirred tank reactor. Using the Maxey-Riley-Gatignol equations for the movement of inertial particles as ground-truth, we demonstrate that the extended Kalman filter and the particle filter reconstruct trajectories from noisy synthetic data with mean relative errors below 3%.
Subjects
Lagrangian sensors
stirred-tank reactor
Maxey-Riley-Gatignol equations
Kalman filter
Particle filter
DDC Class
660.2: Chemical Engineering
Publication version
publishedVersion
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1-s2.0-S0098135426003637-main.pdf
Type
Main Article
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845.29 KB
Format
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