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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. (Publisher's Version)
In: FAN 2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems : 18. - 20. April 2018,
Darmstadt, International Conference on Fan Noise, Aerodynamics, Applications and Systems, Darmstadt, Germany, 18.-20.04.2018, DOI: 10.26083/tuprints-00021357,
[Conference or Workshop Item]

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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Publisher's Version
Title: Softsensor for the characterisation of the process fluid
Language: English
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.

Book Title: FAN 2018 - Proceedings of the International Conference on Fan Noise, Aerodynamics, Applications and Systems : 18. - 20. April 2018
Place of Publication: Darmstadt
Collation: 6 Seiten
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Institute for Fluid Systems (FST)
Event Title: International Conference on Fan Noise, Aerodynamics, Applications and Systems
Event Location: Darmstadt, Germany
Event Dates: 18.-20.04.2018
Date Deposited: 13 May 2022 13:59
Last Modified: 03 Apr 2023 11:56
DOI: 10.26083/tuprints-00021357
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-213572
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21357
PPN: 495522287
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