Performance Prediction of Shell-and-Tube Heat Exchangers with Helical Baffles Using Multilayer Perception Neural Networks Optimized with Genetic Algorithm

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Abstract

In this paper, an artificial neural network (ANN) was applied for shell-side heat transfer and flow resistance analysis of shell-and-tube heat exchangers with non-continuous helical baffles. Heat exchangers with three helical angles and two types of tubes were experimentally investigated. One of the commonly used types of neural networks, the multilayer perception (MLP), was trained with limited experimental data. The genetic algorithm (GA) was employed to optimize the initial weights and biases of the MLP network, which showed more accuracy in prediction. Different network configurations were also studied to search for a relatively better network for the prediction. Comparisons with the correlations demonstrate the superiority of the MLP-GA network. Furthermore, the network yielded agreeable results when it was used for predicting the shell-side heat transfer rate and pressure drop outside the range of the experiments. It is recommended that the MLP-GA combined method might be used to predict the thermal and hydraulic performance of shell-and-tube heat exchangers with helical baffles.

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	Articles by authors
	Sun Yongli
	Wang Huajin
	Hao Li
	Xiao Xiaoming

Keywords： shell-and-tube heat exchanger with helical baffles； heat transfer； multilayer perception； genetic algorithm

Received 2014-05-23;

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Cite this article:

Sun Yongli, Wang Huajin, Hao Li, Xiao Xiaoming.Performance Prediction of Shell-and-Tube Heat Exchangers with Helical Baffles Using Multilayer Perception Neural Networks Optimized with Genetic Algorithm[J] Chemcial Industry and Engineering, 2016,V33(4): 49-55

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