Journal
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
Volume 83, Issue -, Pages 337-344Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2014.11.085
Keywords
Artificial neural network; Fluidized bed; Heat transfer coefficient; Training function
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This paper describes the selection of training function of an artificial neural network (ANN) for modeling the heat transfer prediction of horizontal tube immersed in gas-solid fluidized bed of large particles. The ANN modeling was developed to study the effect of fluidizing gas velocity on the average heat transfer coefficient between fluidizing bed and horizontal tube surface. The feed-forward network with back propagation structure implemented using Levenberg-Marquardt's learning rule in the neural network approach. The objective of this work is to compare performances of five training functions (TRAINSCG, TRAINBFG, TRAINOSS, TRAINLM and TRAINBR) implemented in training neural network for predicting the heat transfer coefficient. The comparison is shown on the basis of percentage relative error, coefficient of determination, root mean square error and sum of the square error. The predictions by training function TRAINBR found to be in good agreement with the experiment's values. (C) 2014 Elsevier Ltd. All rights reserved.
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