Abstract—Abstract—The present study is conducted in order to demonstrate the capability of the artificial neural network (ANN) in predicting the heat transfer in an air cooled heat exchanger equipped with classic twisted tape inserts. The effects of the twist ratio of classic inserts (Y) and Reynolds number (Re) variation on average heat transfer in the air cooler are considered via this prediction. The training data for optimizing the ANN structure is based on available experimental data. The Levenberg-Marquardt back propagation algorithm is used for ANN training. The proposed ANN is developed using MATLAB functions. For the best ANN structure obtained in this investigation, the mean relative errors of 0.457% and 0.478% were reached for the training and test data respectively. The results show that predicted values are very close to experimental values.
Index Terms—Air cooled heat exchanger; classic twisted tape inserts; twist ratio; modeling; artificial neural network (ANN)
A. Amiri and S. F. Seyedpour are with Department of Chemical Engineering, Kermanshah University of Technology, Kermanshah, Iran (e-mail: amin.amiri@kut.ac.ir; fatimaseyedpoor@yahoo.com).
A. M. Karami and E. Rezaei are with Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran (e-mail: alimohammad.karami@gmail.com ; Eehsan.rezaie@yahoo.com)
[PDF]
Cite: A. Amiri, A. M. Karami, S. F. Seyedpour, and E. Rezaei, "Prediction of the Heat Transfer in an Air Cooler Equipped with Classic Twisted Tape Inserts using Artificial Neural Network (ANN)," International Journal of Chemical Engineering and Applications vol. 3, no. 2, pp. 81-85, 2012.
