Artificial neural network (ANN) modeling of heat transfer from horizontal tube bundles immersed in gas fluidized bed of large particles (mustard, raagi and bajara) was investigated. The effect of fluidizing gas velocity on the heat transfer coefficient in the immersed tube bundles in in-line and staggered arrangement is discussed. The parameters particle diameter, temperature difference between bed and immersed surface were used in the neural network (NN) modeling along with fluidizing velocity. The feed-forward network with back propagation structure implemented using Levenberg–Marquardt's learning rule in the NN approach. The predictions of the ANN were found to be in good agreement with the experiment's values, as well as the results achieved by the developed correlations.
Artificial Neural Network Based Prediction of Heat Transfer From Horizontal Tube Bundles Immersed in Gas–Solid Fluidized Bed of Large Particles
Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 27, 2013; final manuscript received July 18, 2014; published online November 5, 2014. Assoc. Editor: Giulio Lorenzini.
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Kamble, L. V., Pangavhane, D. R., and Singh, T. P. (January 1, 2015). "Artificial Neural Network Based Prediction of Heat Transfer From Horizontal Tube Bundles Immersed in Gas–Solid Fluidized Bed of Large Particles." ASME. J. Heat Transfer. January 2015; 137(1): 012901. https://doi.org/10.1115/1.4028645
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