基于灰色关联BP神经网络的压缩式蓄冷系统中的水合物生成量预测

Prediction of hydrate production in compressive cold storage system based on grey relational BP neural network

CO2水合物作为一种新型的蓄冷介质,具有良好的应用前景。在蓄冷系统中,CO2水合物的生成量对蓄冷量有直接影响,但CO2水合物生成计算较为复杂,进而导致系统蓄冷量的计算同样复杂起来,因此建立能够快速分析和预测系统中水合物生成量的模型具有实际意义。本文介绍了可以解决复杂问题的BP神经网络模型(BP)和灰色关联预测模型[GRM(1,n)],并利用Matlab语言编程建立了GRM(1,n)-BP神经网络组合模型来预测水合物的生成量。本文选取实验系统的数据利用3种模型分别进行了预测,并将3种模型的结果与实验结果进行对比,结果表明,GRM(1,n)-BP神经网络组合模型的准确性和稳定性效果更好。最后通过考察充注压力这一单一变量对水合物生成量的影响,并对比模型预测结果,进一步验证了GRM(1,n)-BP神经网络组合模型的准确性。

As a new cold storage medium,CO2 hydrate has a good application prospect.In the cold storage system,the amount of CO2 hydrate generation has a direct impact on the amount of cold storage,but the calculation of the amount of CO2 hydrate generation is complicated,which leads to the calculation of the amount of cold storage in the system is also complicated.Therefore,it is of practical significance to establish a model that can quickly analyze and predict the amount of hydrate production in the system.In this paper,BP neural network model(BP)and grey relational prediction model[GRM(1,n)]which can solve complex problems were introduced,and GRM(1,n)-BP neural network combination model was established by Matlab programming language to predict hydrate production.Three models were selected to predict the data of the experimental system,and the results of the three models were compared with the experimental results.The results showed that the GRM(1,n)-BP neural network combination model has better accuracy and stability.Finally,the accuracy of the GRM(1,n)-BP neural network combination model by investigating the influence of the single variable of charging pressure on hydrate production and comparing the predicted results of the model was further verified.