Date
2017Metadata
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Teil eines Buches
Prädiktive simulationsgestützte Optimierung von Kältemaschinen im Verbund
(Predictive Simulation-based Optimization of Cooling Systems)
Abstract
Prerequisite for an efficient cooling energy system is the knowledge and the optimal combination of different operating conditions of individual compression chillers. The performance of cooling systems depending on its part load performance and its condensing temperature are often unknown. Recorded energy data remains unused and manufacturers' data differs significantly from the real performance. For this purpose, manufacturer and real data are combined and continuously adapted to form partial load curve models. A predictive optimization algorithm calculates the optimal operating conditions of multiple chillers. The set points from the optimization are continuously validated by a dynamic simulation on the reaction and feedback effects of the system. Finally, a case study of a meat processing plant evaluates different scenarios against the status quo. Applying an optimal chiller loading and condensing temperature increases energy efficiency of up to 24 %.
Citation
@inbook{doi:10.17170/kobra-202101273066,
author={Peesel, Ron-Hendrik and Schlosser, Florian and Schaumburg, Chris and Meschede, Henning},
title={Prädiktive simulationsgestützte Optimierung von Kältemaschinen im Verbund},
pages={69-78},
publisher={kassel university press},
year={2017}
}
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3000 Peesel, Ron-Hendrik
3010 Schlosser, Florian
3010 Schaumburg, Chris
3010 Meschede, Henning
4000 Prädiktive simulationsgestützte Optimierung von Kältemaschinen im Verbund
:Predictive Simulation-based Optimization of Cooling Systems / Peesel, Ron-Hendrik
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5550 {{Kältemaschine}}
5550 {{Kälteanlage}}
5550 {{Energieeffizienz}}
5550 {{Simulation}}
7136 ##0##http://hdl.handle.net/123456789/12473
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