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Abstract

In order to develop an acceptable real-time control approach in terms of accuracy and computation time in industrial and commercial applications, the based Back Propagation Neural Network (BPNN) approach was introduced into the discharge pressure optimization process of the transcritical CO2 heat pump systems. The relevant characteristic variables concerning to the discharge pressure was minimized by the Group Method of Data Handling (GMDH) method, and the relevance of all the variables with the optimal rejection pressure were investigated one by one. Prediction error of different type neural network were compared with each other. Finally, the performance of neural network based transcritical CO2 system was compared with that of conventional empirical correlations-based systems in terms of the optimal discharge pressure, which showed that the novel PSO-BP prediction model provides an innovative and appropriate idea for developers and manufacturers.

Item Type:	Conference Publication
Authors/Creators:	Yin, Xiang and Cao, F and Wang, X
Keywords:	CO2 heat pump, optimal discharge pressure, real-time control, neural network, COP
Journal or Publication Title:	Energy Procedia
Publisher:	Elsevier
ISSN:	1876-6102
DOI / ID Number:	10.1016/j.egypro.2019.02.145
Copyright Information:	Copyright 2019 the AuthorsCC BY-NC-ND license
Related URLs:	Google Scholar: Wang, X UTAS Profile: Wang, X
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