A mechanistic population balance model for granulation processes: Effect of process and formulation parameters

Abstract

This work is concerned with the theoretical development of a semi-mechanistic aggregation kernel in a population balance model (PBM) which takes into account the effect of droplet spreading on a particle surface that aids in the coalescence of particles. Empirical aggregation kernels are more commonly used in simulations however they do not reflect the true physics of the system. The proposed kernel is computationally less expensive, yet it takes into account the various key operating parameters that affect the process. The kernel has been validated qualitatively and the lumped and distributed properties show good agreement with the expected behavior of the process. The various empirical parameters present in the granulation model have been identified and expressed as a function of the measurable operating quantities, thus providing a better knowledge regarding the effect of the process parameters on the final product with the help of a more fundamental, first principle based model. A detailed sensitivity analysis (involving viscosity, impeller speed, contact angle and liquid spray rate) has also been conducted in order to study the influence of the process parameters on the final granule properties. This knowledge provides a theoretical basis for the high-shear wet granulation process design space development. The model has also been able to successfully capture the steady and induction behavior of the process under the expected operating conditions.