Application of optimum ANN architecture for heat exchanger modeling

Abstract

The shell and tube heat exchanger is a common type used for heating or cooling of process fluids. The various parameters to be taken into account for developing a model are inlet and outlet temperatures of shell and tube side fluids and their flow rates. Artificial Neural Networks (ANN) are effective in modeling of non-linear multi variable relationships and also referred as black box models. For modeling of shell and tube heat exchanger, ANN architecture has been optimized. In this paper the optimized ANN architecture is employed for water-20% glycerin and water-40% glycerin systems for estimation of exit temperature of both the fluids as a function of inlet temperature conditions and also flow rates. It is observed that ANN model with three hidden layers (4-15-15-15-2) has good level of accuracy (98-99.5%) for predicted values of training and test data set.