As a community, our ability to understand and computationally predict wet granulation process has rapidly improved over the past decades. Despite these advancements, we are yet to use models for granulation or other particulate processes to their full advantage, to enable the predictive design of granular product performance.
The aim of this research is to address this challenge to link process and product performance models for wet granulation. To achieve this aim, a unique granule performance model has been developed. Critically, this multi-scale model for swelling driven granule disintegration and dispersion has been developed with the mission to be suitable for linking with existing process models for wet granulation.
A single granule model for the swelling of a granule containing disintegrant has been proposed, with two variations: mono-sized constituent particles, and distributed constituent particles. This model has then been coupled with a population balance model, to allow the evolving particle size of a dispersing granule population to be modelled.
These models have been validated using novel experiments developed in collaboration with the University of Strathclyde, and the results of these experiments have not only provided the opportunity to parameterise these models but have also provided further insights into the rate processes of granule disintegration and dispersion. By varying between formulations of microcrystalline cellulose and dibasic calcium phosphate, the relative contributions of erosion and swelling driven dispersion are elucidated and qualified mathematically. Model assumptions have also been tested through this experimental validation. A key assumption in the population balance model is the dispersion of granules directly into primary particles, with a lack of intermediate granules or aggregates. On testing, it was found that the assumption was valid for dibasic calcium phosphate, but not as suitable for microcrystalline cellulose.
A comprehensive global sensitivity analysis of the models has been performed, and model parameters have been ranked for their importance to the model. This enables the user to prioritise the accurate measurement or estimation of key parameters, such as granule porosity and disintegrant diffusivity, and deprioritise lower ranked parameters.
The development of this model is expected to enable the linking of process and product models for wet granulation in the second phase of this this IFPRI project, and the subsequent use of inverse methods to enable true model driven process design for desired granule performance.