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Investigation on the real-time control of the optimal discharge pressure in a transcritical CO2 system with data-handling and neural network method
Citation
Yin, X and Cao, F and Wang, X, Investigation on the real-time control of the optimal discharge pressure in a transcritical CO2 system with data-handling and neural network method, Energy Procedia, 13-15 December 2018, Sydney, Australia, pp. 451-458. ISSN 1876-6102 (2019) [Refereed Conference Paper]
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Copyright Statement
Copyright 2019 the Authors CC BY-NC-ND license
DOI: doi:10.1016/j.egypro.2019.02.180
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 Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | CO2 heat pump, optimal discharge pressure, real-time control, neural network, COP |
| Research Division: | Engineering |
| Research Group: | Mechanical engineering |
| Research Field: | Energy generation, conversion and storage (excl. chemical and electrical) |
| Objective Division: | Energy |
| Objective Group: | Energy storage, distribution and supply |
| Objective Field: | Energy systems and analysis |
| UTAS Author: | Wang, X (Professor Xiaolin Wang) |
| ID Code: | 131322 |
| Year Published: | 2019 |
| Web of Science® Times Cited: | 1 |
| Deposited By: | Engineering |
| Deposited On: | 2019-03-13 |
| Last Modified: | 2022-07-06 |
| Downloads: | 32 View Download Statistics |
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