Modeling of gas–solid, heterogeneously
catalytic, diameter-transformed
fluidized bed (DTFB) reactors is intrinsically complex and requires
considering the variation of material properties and operating conditions,
because of reactions and/or diameter transformation. The EMMS-matrix
drag model, which correlates both operating conditions and local parameters,
has been applied in computational fluid dynamics (CFD) simulation
of such complex reactors by simplifying the macroscale operating conditions
with one set of constant parameters. However, a complete scheme has
not been reported that covers a wide range of datasets for a DTFB
reactor with complex reactions. To this end, the artificial neural
network (ANN), which enables exploring a multivariate relation with
the contribution of a set of different parameters, is chosen to couple
with EMMS drag modeling. A complete scheme of EMMS-ANN drag for hot,
reactive simulation of DTFB is thereby established, with comprehensive
evaluation of the contribution of drag markers successively considering
the variation of gas properties and operating parameters. Both a priori
tests and CFD simulations show that the voidage and slip velocity
are the dominant factors in modeling of drag correction, and the effects
of dynamic variation of gas properties and operating hydrodynamics
are marginal; even the heterogeneous reactions and the change in bed
diameter give rise to a remarkable variation in gas properties and
operating parameters. The underlying mechanism is then analyzed to
provide important clues for drag modeling of gas–solid, heterogeneous
catalytic fluidized-bed reactors.