Artificial Neural Networks model for predicting wall temperature of supercritical boilers

Prediction of wall temperature for the range of operating conditions and selecting appropriate material for water-wall tubes, cooled by turbulent water/steam with drastic changes in property, is important in boiler design. An analytical route of predicting the wall temperature for such flow conditions is not reliable. Empirical correlations of non-dimensional numbers, based on experimental data, are used for predicting wall temperatures of turbulent flow with abrupt changes in fluid properties. BHEL has conducted many experiments with supercritical water/steam and developed Artificial Neural Network (ANN) based wall temperature prediction model. This model predicts wall temperature using the given inputs of fluid pressure, fluid temperature, product of mass flux and diameter, and heat flux. The model has prediction accuracy of 100% for the experimental data and 81.94% for the literature data at a deviation level of +/- 7 degrees C. This ANN model is useful for predicting wall temperatures of supercritical boilers operating in the tested range of parameters. (C) 2015 Elsevier Ltd. All rights reserved.