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Data-driven modelling of a R744 refrigeration system with parallel compression configuration.

Number: pap. n. 946

Author(s) : ANDREASEN G., STOUSTRUP J., PARDIÑAS A. A., et al.

Summary

The type and characteristics of the employed components, subsystems and refrigerant in a commercial refrigeration system varies significantly depending on required functionalities (e.g. MT (medium temperature), LT (low temperature), AC (air conditioning)) and external factors (e.g. ambient temperature). Thus, modeling for control-oriented objectives becomes a challenging task. As these systems provide a large amount of data, modelling for control-oriented objectives based on a data-driven method become an appealing option. This work focuses on obtaining a (controloriented) data-driven MIMO (multiple input multiple output) model of a CO2 booster refrigeration system, supported by parallel compression and ejectors, producing refrigeration for MT, LT, AC. Subspace identification is used for obtaining the data-driven MIMO model. The data-driven modelling approach is validated with data from a CO2 system where negligible deviations are observed even with change in the main disturbances.

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Pages: 9

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Details

  • Original title: Data-driven modelling of a R744 refrigeration system with parallel compression configuration.
  • Record ID : 30026500
  • Languages: English
  • Source: Proceedings of the 25th IIR International Congress of Refrigeration: Montréal , Canada, August 24-30, 2019.
  • Publication date: 2019/08/24
  • DOI: http://dx.doi.org/10.18462/iir.icr.2019.0946

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