Title: Comparison of performance prediction of solar water heaters between Artificial Neural Networks and conventional correlations
Authors: J. Razavi, M.R. Riazi, F. Raoufi, A. Sadeghi
Addresses: Chemical Engineering Department, Sharif University of Technology, Tehran 11365, Iran. ' Chemical Engineering Department, Kuwait University, PO Box 5969, Safat 13060, Kuwait. ' Chemical Engineering Department, Sharif University of Technology, Tehran 11365, Iran. ' Chemical Engineering Department, Sharif University of Technology, Tehran 11365, Iran
Abstract: The aim of this study was to develop a predictive method for heat transfer coefficients in solar water heaters and their performance evaluation of such heaters with different materials used as heat collectors. Two approaches have been used: conventional method and an Artificial Neural Network (ANN) to predict the performance of solar water heaters and to compare these two approaches. This performance is measured in terms of outlet temperature by using a set of conventional feed forward multi-layer neural networks. The actual experimental data which were used as our network|s input gathered from published literature (for polypropylene tubes) and from the experiments carried out recently using copper tubes. The results of this study showed that ANN approach can give better approximation than the traditional theoretical correlations which was obtained by linear regression analysis.
Keywords: heat transfer coefficient; artificial neural networks; ANNs; performance evaluation; solar water heaters; outlet temperature; polypropylene tubes; copper tubes; linear regression analysis.
International Journal of Global Energy Issues, 2009 Vol.31 No.2, pp.122 - 131
Published online: 18 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article