Multiscale CFD Simulation of an Industrial Diameter-Transformed Fluidized Bed Reactor with Artificial Neural Network Analysis of EMMS Drag Markers

Abstract

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.