Cooperative Optimization of A Refrigeration System with A Water-Cooled Chiller and Air-Cooled Heat Pump by Coupling BPNN and PSO

Qinli Deng, Liangxin Xu, Tingfang Zhao, Xuexin Hong (), Xiaofang Shan () and Zhigang Ren
Additional contact information
Qinli Deng: School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
Liangxin Xu: School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
Tingfang Zhao: School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
Xuexin Hong: Wuhan University of Technology Design and Research Institute Co., Ltd., Wuhan 430070, China
Xiaofang Shan: School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China
Zhigang Ren: School of Civil Engineering and Architecture, Wuhan University of Technology, No.122 Luoshi Road, Wuhan 430070, China

Energies, 2022, vol. 15, issue 19, 1-19

Abstract: Aiming at the issues of unreasonable cooperation schemes and inappropriate setting of parameters of the refrigeration system with multi-chiller plants, this paper presents a cooperative optimization method to improve the energy performance of the system composed of water-cooled chillers and air-cooled heat pumps. The cooperative optimization process includes scheme optimization and parameter optimization. To content the dynamic cooling load, the working sequence of air-cooled heat pumps and water-cooled chillers with variable frequency chilled water pumps is first optimized. Based on the optimal scheme, a back-propagation neural network (BPNN) coupled with particle swarm optimization (PSO) is implemented to explore the preferred operating parameters of multiple chiller plants corresponding to the best coefficient of performance (COP). Compared with the performance of the initial operation module, the energy consumption of the water pump and fan decreases by over 50%, and the COP of the refrigeration system is improved by 16% (COP = 3.85) through the scheme operation. After parameter optimization, the total energy consumption is reduced by 21.7%, and COP is increased by 26.5% (COP = 4.20). Therefore, the proposed cooperative optimization method can provide useful operation guidance for the refrigeration system with multi-chiller plants.

Keywords: water-cooled chiller; air-cooled heat pump; back-propagation neural network; particle swarm optimization; optimal operation control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/7077/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7077/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:7077-:d:925927

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().