Lithologic Reservoirs ›› 2024, Vol. 36 ›› Issue (2): 15-22.doi: 10.12108/yxyqc.20240202

• NEW ENERGY AND ASSOCIATED RESOURCES • Previous Articles    

Heat transfer prediction of medium and deep coaxial casing based on BP algorithm

XIONG Bo, ZHU Dongxue, FANG Chaohe, WANG Shejiao, DU Guanglin, XUE Yafei, MO Shaoyuan, XIN Fudong   

  1. PetroChina Shenzhen New Energy Research Institute Company, Shenzhen 518000, Guangdong, China
  • Received:2023-08-24 Revised:2023-10-09 Published:2024-03-06

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Abstract: By building a heat pump test bench and obtaining test data under multiple working conditions，a BP neural network coaxial casing heat transfer prediction model was established，and the simulation was carried out to predict the heat transfer of the coaxial casing. The results show that：（1）The heat pump system has the highest energy efficiency during stable operation at a water flow rate of 28 m3/h and a return water temperature of 10 °C， and the effective heat transfer of coaxial casing is 563 kW.（2）When the number of hidden layer nodes is 12，the BP neural network prediction model is optimal，with a maximum training mean square error（MSE）is 0.023%， and the basic structure of the optimal model is 9-12-1.（3）Comparing the simulation results of the predicted value and the test value of the coaxial casing，the average percentage error of the prediction of heat transfer of the coaxial casing of the BP neural network is 0.235%，and the prediction accuracy is 99.765%. The prediction model has high accuracy and reliability，and can accurately predict the change trend of heat transfer of coaxial casing，which has a wide range of application value for improving the energy efficiency and performance of heat pump systems.

Key words: BP neural network, medium and deep coaxial casing, heat pumps, simulation, heat transfer prediction

CLC Number: 

  • TE09
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