The hydrodynamics of a gas–solid fluidized bed (FB)
is affected by the bubble diameter, which in turn
strongly influences the performance of a fluidized bed
reactor (FBR). Thus, determining the bubble diameter
accurately is of crucial importance in the design and
operation of an FBR. Various equations are available
for calculating the bubble diameter in an FBR. It has
been found in this study that these models show a
large variation while predicting the experimentally
measured bubble diameters. Accordingly, the present
study proposes a new equation for computing the
bubble diameter in a fluidized bed. This equation has
been developed using an efficient, yet infrequently
employed computational intelligence (CI)-based datadriven
modelling method termed genetic programming
(GP). The prediction and generalization performance
of the GP-based equation has been compared with
that of a number of currently available equations for
computing the bubble diameter in a fluidized bed and
the results obtained show a good performance by the
newly developed equation