A Comparative Study of ANN and CFD Modelling for Pressure Drop Prediction in a Fluidized Bed with Internals

Abstract

In the present study, experiments on fluidized beds were conducted by varying hydrodynamic parameters such as air velocity, bed height and internal spacing and these data were used in the development of a model using artificial neural network (ANN) and simulation using computational fluid dynamics (CFD). Staggered rod internals of width 25% and 50% of the column diameter were used. A two-layered feed-forward back propagation ANN model was developed to predict the pressure drop with 15 neurons and it gave a high R2 value of 0.9966. The model prediction and experimental data are in good agreement. The results from CFD studies are also in good agreement with the experimental data. Pressure drop was found to reduce in the presence of internals and it reduced further with a decrease in internal spacing due to continuous splitting of bubbles. The average error in CFD predicted pressure drop is 9.56% and ANN predicted pressure drop is 0.95%.