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...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.展开更多
文摘针对热力站二次回水温度预测模型特征多计算量大、模型准确性难以提升的问题,提出一种极限梯度提升-人工神经网络(xtreme gradient boosting-artifical neural network,XGBoost-ANN)二次回水温度预测模型,模型由特征筛选层和预测层组成。特征筛选层利用XGBoost算法计算原始数据特征的重要性分数,确定影响二次回水温度的主要特征,从而降低模型复杂度,并提高计算效率;采用贝叶斯正则化算法训练三层前馈ANN作为二次回水温度预测层,并通过灰狼优化(grey wolf optimizer,GWO)算法对ANN模型的初始权值和阈值进行优化,用灰狼的位置向量表示ANN模型的权值和阈值,引入适应度函数来评估每组权值和阈值的性能,帮助模型在训练初期避免陷入局部最优,以提升模型的性能与泛化能力。实验结果表明,所构建的XGBoost-GWO-ANN二次回水温度预测模型,相比特征筛选前的模型,均方根误差(root mean squared error,RMSE)性能提升26.8%,R^(2)提升11.3%,模型推理时间降低了46.1%;使用GWO算法对ANN初始权值和阈值进行寻优,相比于未经优化的ANN模型,RMSE性能提升20.0%,R^(2)提升3.4%,预测模型的精度以及泛化能力得到有效提升。
基金Project (2015CB251403) supported by the National Key Basic Research Program of China(973)
文摘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.
