Analysis of Heat Exchanger Performance Forecast Based on the BP Neural Network
This paper uses the BP neural network algorithm to predict the performance of heat exchangers, sets up a applied structure of the BP neural network and expounds the realization of predicted algorithm, including the determination of network structure, the learning rate, the network performance evaluation, the training and test aggregate, the network target errors and the network training times and so on, which is the simulation of predicting the performance of a heat exchanger with pipes buried underground in a ground source heat pump system. The results of prediction show that the relative errors of the heat exchanger performance prediction are mostly within 5.4%, and the neural network prediction results agree well with the experimental results, which have better generalization ability. This research method for underground heat exchanger can provide basis for optimizing the parameters, so it has certain practical significance and social value.
Liangchi Zhang, Chunliang Zhang and Tielin Shi
Y. M. Meng et al., "Analysis of Heat Exchanger Performance Forecast Based on the BP Neural Network", Advanced Materials Research, Vols. 139-141, pp. 1697-1701, 2010