Artificial neural network (ANN), in comparison with empirical correlations, has
recently received more attention. The present paper includes predictive modeling of
heat transfer coefficient for binary mixtures in pool boiling for hydrocarbon
compounds, using Back propagation techniques through Multilayer Perceptron, one
of the types of the artificial neural networks. To train and learn the system, predictive
neural network was found, which is capable of understanding and predicting the preset
output which is heat transfer coefficient. The principle operation of such neural
networks is based on the experimental data collected from some researchers [1-4]. A
new ANN model is proposed using five inputs (mole fraction, temperature difference,
heat flux, density and viscosity) to predict the heat transfer coefficient. The prediction
using ANN shows 0.0026 AARE (Absolute Average Relative Error) with most widely
known correlations namely those of Calus, Fujita and Thome which have given 0.086,
0.066 and 0.038 respectively.