A tapered
fluidized bed with variable fluid velocity throughout
the bed length is a special type of fluidized system. Accurate estimation
of hydrodynamic characteristics of the tapered fluidized bed is required
for adjusting the operational conditions, optimum design, and process
control of this system. In this way, minimum fluidization velocity
(U
mf), minimum velocity of full fluidization
(U
mff), and maximum pressure drop (ΔP
max) are the main hydrodynamic characteristics
of the tapered fluidized bed. In this study, an artificial neural
network (ANN) paradigm was developed for prediction of these parameters.
Parameters of the ANN model was adjusted through minimization of the
absolute average relative deviation (AARD) and mean square error (MSE)
via a back-propagation algorithm. Finally, the proposed model predicted
the experimental data of U
mf, U
mff, and ΔP
max with AARD of 1.1%, 1.36%, and 0.89%, respectively, while the best
obtained results by five different empirical correlations were 4.12%,
9.4%, and 5.14% for U
mf, U
mff, and ΔP
max.
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