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Softsensor for the characterisation of the process fluid

Schmitz, Christian ; Nakhjiri, Mehdi ; Pelz, Peter F. (2022)
Softsensor for the characterisation of the process fluid.
International Conference on Fan Noise, Aerodynamics, Applications and Systems. Darmstadt, Germany (18.04.2018-20.04.2018)
doi: 10.26083/tuprints-00021357
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Softsensor for the characterisation of the process fluid
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2018
Book Title: FAN 2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems : 18. - 20. April 2018
Collation: 6 Seiten
Event Title: International Conference on Fan Noise, Aerodynamics, Applications and Systems
Event Location: Darmstadt, Germany
Event Dates: 18.04.2018-20.04.2018
DOI: 10.26083/tuprints-00021357
Corresponding Links:
Origin: Secondary publication service
Abstract:

This paper introduces an intelligent fan equipped with a softsensor for the volume flow rate and composition of a two-component process fluid, as e.g. often found in chemical processes. This is done by combining the fans characteristic data with those of cheap pressure and temperature sensors as well as the mixture laws of a two-component gas. An example estimating the percentage of butane for a combustion flow is given. As the experiments show, the estimation uncertainty of the softsensor is 2 % for the butane concentration and 3 % for the volume flow rate and thus of the magnitude of the uncertainties of the used input data.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-213572
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Fluid Systems (FST) (since 01.10.2006)
Date Deposited: 13 May 2022 13:59
Last Modified: 03 Apr 2023 11:56
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21357
PPN: 495522287
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