Intelligent prediction on performance of high-temperature heat pump systems using different refrigerants

• Published in: Journal of Central South University Vol. 25; no. 11; pp. 2754 - 2765
• Main Authors: Yu, Xiao-hui, Zhang, Yu-feng, Zhang, Yan, He, Zhong-lu, Dong, Sheng-ming, Ma, Xue-lian, Yao, Sheng
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.12.2018; Springer Nature B.V
• Subjects: Back propagation networks; Engineering; Heat; Heat pumps; High temperature; Mathematical models; Metallic Materials; Neural networks; Performance prediction; Refrigerants; Reliability aspects; Support vector machines; back propagation neural network; performance prediction; 实验性能; 支持向量机; 误差回传神经网络; 性能预测; support vector machine; high-temperature heat pump; experimental performance; 高温热泵
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-018-3951-0

Two new binary near-azeotropic mixtures named M1 and M2 were developed as the refrigerants of the high-temperature heat pump (HTHP). The experimental research was used to analyze and compare the performance of M1 and M2-based in the HTHP in different running conditions. The results demonstrated the feasibility and reliability of M1 and M2 as new high-temperature refrigerants. Additionally, the exploration and analyses of the support vector machine (SVM) and back propagation (BP) neural network models were made to find a practical way to predict the performance of HTHP system. The results showed that SVM-Linear, SVM-RBF and BP models shared the similar ability to predict the heat capacity and power input with high accuracy. SVM-RBF demonstrated better stability for coefficient of performance prediction. Finally, the proposed SVM model was used to assess the potential of the M1 and M2. The results indicated that the HTHP system using M1 could produce heat at the temperature of 130 °C with good performance.