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

Discharge pressure is an important factor that heavily affects the system COP in the transcritical CO2 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 CO2 heat pump could work better in the optimal COP operation condition with the novel statistical model.

[1]  Performance evaluation of a gas injection CO2 heat pump according to operating parameters in extreme heating and cooling conditions , 2018, Energy.

[2]  Hua Tian,et al.  Thermodynamic performance assessment of carbon dioxide blends with low-global warming potential (GWP) working fluids for a heat pump water heater ☆ , 2015 .

[3]  Hema R. Madala,et al.  Inductive Learning Algorithms for Complex Systems Modeling , 2017 .

[4]  Feng Cao,et al.  Real-time minimization of power consumption for air-source transcritical CO2 heat pump water heater system. , 2018 .

[5]  Jaehyeok Heo,et al.  Effects of vapor injection techniques on the heating performance of a CO2 heat pump at low ambient temperatures , 2014 .

[6]  Marco Corradi,et al.  A critical approach to the determination of optimal heat rejection pressure in transcritical systems , 2010 .

[7]  Shengming Liao,et al.  A correlation of optimal heat rejection pressures in transcritical carbon dioxide cycles , 2000 .

[8]  Petter Nekså,et al.  CO2 heat pump systems , 2002 .

[9]  Junjie Gu,et al.  The optimum high pressure for CO2 transcritical refrigeration systems with internal heat exchangers , 2005 .

[10]  Friedrich Kauf,et al.  Determination of the optimum high pressure for transcritical CO2-refrigeration cycles , 1999 .

[11]  Jahar Sarkar,et al.  Optimization of a transcritical CO2 heat pump cycle for simultaneous cooling and heating applications , 2004 .

[12]  Yunting Ge,et al.  Control optimisation of CO2 cycles for medium temperature retail food refrigeration systems , 2009 .

[13]  Marco Corradi,et al.  A real-time algorithm for the determination of R744 systems optimal high pressure. , 2012 .

[14]  Xiaolin Wang,et al.  Experimental investigation of the optimal heat rejection pressure for a transcritical CO2 heat pump water heater , 2013 .

[15]  Xiaozhou Wu,et al.  Dynamic Character Investigation and Optimization of a Novel Air-Source Heat Pump System , 2017 .

[16]  Ciro Aprea,et al.  Heat rejection pressure optimization for a carbon dioxide split system: An experimental study , 2009 .

[17]  Jahar Sarkar,et al.  Simulation of a transcritical CO2 heat pump cycle for simultaneous cooling and heating applicationsElimination des cristaux de givre sur une plaque froide: effets des fréquences stationnaires et de balayage des champs électriques , 2006 .

[18]  Feng Cao,et al.  Experimental investigation on air-source transcritical CO2 heat pump water heater system at a fixed water inlet temperature , 2013 .

[19]  Yaoyu Li,et al.  Extremum seeking control of COP optimization for air-source transcritical CO2 heat pump water heater system , 2015 .

[20]  Yaoyu Li,et al.  Extremum seeking control for efficient operation of hybrid ground source heat pump system , 2016 .