(65h) Two-Compartment Modeling and Simulation Study of a Top Sprayed Fluidized Bed Granulator

Spray fluidized bed granulator (SFBG) is promising technique for the production of granules in various industries. In this study, a top spray fluidized bed granulator is being modeled and analyzed with the help of population balance equation (PBE). The mathematical model for SFBG is derived using the concept of compartment modeling. A top spray fluidized bed granulator is divided into two compartments, wet compartment and dry compartment. In the wet compartment, the aggregation is assumed whereas the breakage process is considered in dry compartment as these are the dominating processes in respective compartments. Further, a recently developed discretization ([1] and [2]) is modified and used to solve this model whose idea is based on the conserving the important properties of the system. Moreover, the numerical results of the integral moments derived by the discretization are validated against the newly developed exact results for various combinations of the aggregation and breakage kernels. The model is also tested for physical tractable kernels and the numerical results are also authenticated with the constant volume Monte Carlo [3]. The volume ratio between the wet and dry compartments and the rate of mass exchange between them are important variables and produce size distributions that are distinctly different from those in a single compartment granulator with simultaneous aggregation and breakup. Moreover, the dynamics of the two-compartment model exhibit a diverse range of behaviors that do not always reach steady state, and in some cases are unstable. We conclude that the proposed discretization scheme is capable of tracking the size distribution accurately and provides a robust numerical methodology to study the dynamics of two compartment granulators.

References:

[1] Kumar, J., Kaur, G. and Tsotsas, E. (2016) An accurate and efficient discrete formulation of aggregation population balance equation. Kinetic and Related Models 9(2), 373-391.

[2] Saha, J., Kumar, J., Bück, A. and Tsotsas, E. (2016) Finite volume approximations of breakage population balance equation. Chemical Engineering Research and Design 110, 114-122.

[3] Smith, M. and Matsoukas, T. (1998) Constant-number Monte Carlo simulation of population balances. Chemical Engineering Science 53(9), 1777-1786.