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  4. Powering mobile sensor nodes in fluidized beds: A non-invasive RF energy harvesting proof-of-concept

Powering mobile sensor nodes in fluidized beds: A non-invasive RF energy harvesting proof-of-concept

Citation Link: https://doi.org/10.15480/882.19030
Publikationstyp
Journal Article
Date Issued
2026-10-16
Sprache
English
Author(s)
Jaeger, Marvin
Hochfrequenztechnik E-3
Becker, Maximilian
Mechatronik im Maschinenbau M-4
Orth, Maike
Feststoffverfahrenstechnik und Partikeltechnologie V-3
Schulz, Simon
Hochfrequenztechnik E-3
Heinrich, Stefan
Feststoffverfahrenstechnik und Partikeltechnologie V-3
Kern, Thorsten A.
Mechatronik im Maschinenbau M-4
Koelpin, Alexander
Hochfrequenztechnik E-3
TORE-DOI
10.15480/882.19030
TORE-URI
https://hdl.handle.net/11420/65463
Journal
Industrial & engineering chemistry research
Citation
Industrial & Engineering Chemistry Research (in Press): (2026)
Publisher DOI
10.1021/acs.iecr.6c01157
Publisher
American Chemical Society (ACS)
Is Supplemented By
10.15480/882.16850
Real-time monitoring within industrial processing vessels is essential for advancing process analytical technology. Flow-following sensors represent an ideal approach for delivering continuous, in-line data from the process stream. However, deploying these mobile sensors is constrained by the physical barriers of sealed metallic vessels and harsh, battery-hostile environments. To overcome these limitations, a non-invasive, retrofittable wireless power transfer system is presented. The metallic vessel is structurally repurposed into a 5.23 GHz multi-mode microwave cavity resonator by coupling microwave energy through existing standard flanges. Electromagnetic simulations validate that this method generates continuous energy coverage throughout dynamic flow zones, demonstrated using a fluidized bed granulator as an application example. To harness this energy, an omnidirectional antenna and a compact RF-to-DC rectifier prototype were developed. By enabling the continuous operation of autonomous, battery-free mobile sensor nodes, this framework provides a practical, scalable foundation for high-density spatial analytics and data-driven industrial optimization.
DDC Class
660.2: Chemical Engineering
621.3: Electrical Engineering, Electronic Engineering
Funding(s)
SFB 1615 - SMARTe Reaktoren für die Verfahrenstechnik der Zukunft
SFB 1615 - Teilprojekt A07: Hochintegrierte Sensoren für die Inline-Erfassung des Granulationsfortschrittes in Wirbelschichtreaktoren
Lizenz
https://creativecommons.org/licenses/by/4.0/
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