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

Abstract

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.