Rathi, VamikaVamikaRathiSehar, FatimaFatimaSeharSommer, Finn LucaFinn LucaSommerGötschel, SebastianSebastianGötschelSteuwe, EikeEikeSteuweKameke, Alexandra vonAlexandra vonKamekeRuprecht, DanielDanielRuprecht2026-10-012026-06-122026-09-28Computers & Chemical Engineering : 109911 (2026) (in Press; CC BY 4.0)https://hdl.handle.net/11420/65132Lagrangian 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%.en0098-13542026Elsevierhttps://creativecommons.org/licenses/by/4.0/Lagrangian sensorsstirred-tank reactorMaxey-Riley-Gatignol equationsKalman filterParticle filterTechnology::660: Chemistry; Chemical Engineering::660.2: Chemical EngineeringJournal Article10.1016/j.compchemeng.2026.10991110.15480/882.1869910.5281/zenodo.20629013