Research on thermal resistance and energy efficiency prediction model of plate heat exchangers based on artificial neural network
SONG Kunqing
CHEN Jingru
SUN Yanhua
Abstract：In response to the prediction requirements of energy efficiency and thermal resistance of plate heat exchangers,taking the flow rates of cold and hot fluids as input parameters and thermal resistance and energy efficiency as output parameters,an artificial neural network model of plate heat exchangers is constructed.The optimal model parameters are determined through hyperparameter analysis.Considering the inlet and outlet temperatures of the fluids on the cold and hot sides,two sets of control models are established respectively.Combined with SHAP analysis,the marginal contributions of each thermodynamic parameter to the prediction of thermal resistance and energy efficiency are given.The results show that the artificial neural network model with the flow rates of cold and hot fluids as input has better predictive performance,and the maximum relative errors for predicting thermal resistance and energy efficiency are 3.08%and 5.97%respectively.The flow rates of cold and hot fluids are important influencing factors for the output of artificial neural network models and should be given priority when constructing the model and selecting the training set.
Keywords：heat exchangerperformance predictionneural networkSHAP analysis
Publication Date：2025-04-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 61-65 )