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

Abstract

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.

摘要

本文设计、开发了两种新型高温近共沸混合工质M1 和M2。实验研究了不同工况下M1 和M2 两种工质的性能并进行对比分析。测试结果验证了M1 和M2 作为新型高温工质的可行性和可靠 性。此外, 本文还应用支持向量机及BP 人工神经网络模型对高温热泵性能进行预测。预测结果表明: 对于制热量和输入功率的预测, SVM-RBF、SVM-LF 和BP 三种模型均具有较高的预测精度, 对于性 能系数的预测, SVM-RBF 模型具有较好的预测精度。最后, 本文使用支持向量机模型对M1 和M2 两种工质的潜力进行评估。评估结果表明, M1 的高温热泵在产热温度为130°C时仍具有良好的性能。