Prediction of heat transfer performance of bubbling plates in cross-flow evaporative condensers based on BP neural network

In order to predict the composite heat transfer coefficient of bubbling plates on the air side of evaporative condensers, establishes a heat transfer performance test system consisting of two bubbling plates, and keeps the wall temperature of the plates at 60 ℃ by adjusting the electric heating power under certain working conditions. During the experiment, the environmental conditions vary from 98.8 to 99.3 kPa in atmospheric pressure, 26 to 37 ℃ in inlet air dry bulb temperature and 23 to 32 ℃ in inlet air wet bulb temperature. The range of adjustable parameters is as follows: spray water flow rate 100 to 400 L/h, cross-section wind speed 1.0 to 3.7 m/s, plate spacing 20 to 30 mm. Calculates the composite heat transfer coefficient between plate and air. Processes the experimental data by three-layer BP neural network. The input parameters are inlet air dry and wet bulb temperatures, spray water flow rate, cross-section wind speed and plate spacing, and the output parameter is the composite heat transfer coefficient between plate and air. The correlation coefficient of prediction results is 0.999 2, the average relative error is 0.355 94%, and the root mean square error is 0.508 01 W/(m2·K), which indicates that the BP neural network has high accuracy in predicting the composite heat transfer coefficient of bubbling plate air side in evaporative condensers.