Optimal pressure prediction of R744 heat pump system based on PSO-BP-ANN model

Zhixin Li; Dongfang Yang; Weiqing Zhou; Jilong Zhang; Xian Zhou

doi:10.1117/12.2689851

7 September 2023 Optimal pressure prediction of R744 heat pump system based on PSO-BP-ANN model

Zhixin Li, Dongfang Yang, Weiqing Zhou, Jilong Zhang, Xian Zhou

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 127901B (2023) https://doi.org/10.1117/12.2689851
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

As concerns regarding sustainable development, high efficiency, environmental protection, and a low-carbon economy increase, the energy efficiency and economic benefits of heat pump technology for heating issues have captured the public's attention. After analyzing validation sample data with linear regression coefficients, the PSO-BP neural network was found to be the best prediction model for the ideal exhaust pressure of the R744 transcritical heat pump system. The structure of the optimal model was validated by the analysis and discussion of the PSO-BP network's indicated layers and particle swarm iterations. Comparisons are made between the prediction model and ideal exhaust pressure correlations, and the properties and error sources of each algorithm are examined to demonstrate its efficacy. The PSOBP neural network is superior at predicting optimal pressure on units of comparable design, thereby expanding the application scenario for the cross-critical R744 heat pump. Simulated and experimental results demonstrate that the composite system can reliably generate hot water at varying feedwater and ambient temperatures.

Citation Download Citation

Zhixin Li, Dongfang Yang, Weiqing Zhou, Jilong Zhang, and Xian Zhou "Optimal pressure prediction of R744 heat pump system based on PSO-BP-ANN model", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127901B (7 September 2023); https://doi.org/10.1117/12.2689851

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