Prediction and Analysis of Dew Point Indirect Evaporative Cooler Performance by Artificial Neural Network Method

Abstract

The artificial neural network method has been widely applied to the performance prediction of fillers and evaporative coolers, but its application to the dew point indirect evaporative coolers is rare. To fill this research gap, a novel performance prediction model for dew point indirect evaporative cooler based on back propagation neural network was established using Matlab2018. Simulation based on the test date in the moderately humid region of Yulin City (Shaanxi Province, China) finds that: the root mean square error of the evaporation efficiency of the back propagation model is 3.1367, and the r2 is 0.9659, which is within the acceptable error range. However, the relative error of individual data (sample 7) is a little bit large, which is close to 10%. In order to improve the accuracy of the back propagation model, an optimized model based on particle swarm optimization was established. The relative error of the optimized model is generally smaller than that of the BP neural network especially for sample 7. It is concluded that the optimized artificial neural network is more suitable for solving the performance prediction problem of dew point indirect evaporative cooling units.

Keywords

• dew point indirect evaporative cooling
• air conditioning unit
• PSO-BP neural network
• performance prediction