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Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network—part B: Experimental study.

Author(s) : SONG Y., YANG D., LI M., et al.

Type of article: Article, IJR article

Summary

In this second part of a two-part article, the Particle Swarm Optimization (PSO) based Back-Propagation Neural-Network (BP) based algorithm for the discharge pressure controlling was experimentally achieved based on a subcooler-based transcritical CO2 rig, for further developing an acceptable real-time control approach. The detail of the control strategy in practice was clearly shown including the recirculating water PID control, the PSO-BP based discharge pressure optimization and the electronic expansion valve regulatory mechanism. Besides, the optimal discharge pressure sought by PSO-BP and corresponding system performances were compared with the results from Wang/Liao’s predictions and the tested values, which validated the prominent effectiveness of the PSO-BP method due to its satisfactory consistency with the tested data. Additionally, the subcooler-based rig under the discharge pressure from PSO-BP control had more than 15 and 25% improvements over the baseline cycle under floor heating and radiator conditions, respectively, which provided an innovative and appropriate idea for developers and manufacturers.

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Pages: 248-257

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Details

  • Original title: Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network—part B: Experimental study.
  • Record ID : 30026839
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 106
  • Publication date: 2019/10
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2019.06.008

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