Estimation of the Heat Transfer Coefficient in a Liquid–Solid Fluidized Bed Using an Artificial Neural Network

A non-iterative procedure has been developed using an artificial neural network (ANN) for estimating the fluid–particle heat transfer coefficient, hfp, in a liquid–solid fluidized system. It is assumed that in a liquid–solid system, the liquid temperature is time dependent, and the input parameters and output parameters for the ANN are considered on a linear scale. The output configuration yields an optimal ANN model with 10 neurons in each of the three hidden layers. This configuration is capable of predicting the value of Bi in the range of 0.1–10 with an error of less than 3%. The heat transfer coefficient estimated using the ANN has been compared with the data reported in the literature and found to match satisfactorily.