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Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network—Part A: Theoretical modeling.

Author(s) : YIN X., CAO F., WANG J., et al.

Type of article: Article, IJR article

Summary

Discharge pressure is an important factor that heavily affects the system COP in the transcritical CO 2 heat pump. In most cases, it is commonly confirmed by the empirical correlations or calculated by the mathematical model according to a single operation condition, thus leading to the prediction error or lengthy time. In this paper, a novel model using the statistical method known as the group method of data handling-type (GMDH) and PSO-BP-type (Particle-Swarm-Optimization and Back-Propagation) neural network was developed to predict the optimal discharge pressure. The relevance of all the parameters to the optimal discharge pressure was investigated orderly. Results showed that the new model had the highest accuracy compared to the current correlations. The relative error was around 1.6% while the error of traditional methods ranged from 11.1% to 44.9%. Therefore, the CO 2 heat pump could work better in the optimal COP operation condition with the novel statistical model.

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Pages: 549-557

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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 A: Theoretical modeling.
  • Record ID : 30027020
  • 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.2019.04.027

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