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 %.
@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},
keywords ={620 and Kältemaschine and Kälteanlage and Energieeffizienz and Simulation},
copyright ={http://creativecommons.org/licenses/by-sa/4.0/},
language ={de},
publisher ={Universität Kassel},
year ={2017}
}