Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach

The present work introduces a way of predicting the local heat transfer coefficient in the combustion chamber of the circulating fluidized bed boiler (CFB) by the artificial neural network (ANN) approach. Neural networks have been successfully applied to calculate the local overall heat transfer coefficient for membrane walls, Superheater I (SH I, Omega Superheater) and Superheater II (SH II, Wing-Walls) in the combustion chamber of the 260 MWe CFB boiler. The previously verified numerical model has been used to obtain the overall heat transfer coefficients, necessary for training and testing the ANN. It has been shown, that the neural networks give quick and accurate results as an answer to the input pattern. The local heat transfer coefficients evaluated using the developed ANN model have been in a good agreement with numerical and experimental results.