Nuclei size distribution modelling in wet granulation

Nucleation is an important wet granulation rate process that sometimes has a profound effect on granuleattributes and which needs to be captured in process modelling studies. However, existing models fail topredict nuclei size distribution of a range of spray conditions typically used in industry. In this paper, thedimensionless nucleation numberWnis used to develop two new nuclei size distribution models, oneempirical and one semi-mechanistic. The empirical model assumes a log-normal distribution (LND),and the semi-mechanistic model is based on a approach proposed by Hapgood et al. (2009), which appliesthe Poisson distribution (PD) function. Modelling parameters are estimated using Monte Carlo simula-tions (MCS) data. From the models, the nuclei size distribution can be easily determined using analyticalequations, which simplifies the inclusion in a population balance modelling (PBM) framework. Theresults of both models are assessed using MCS data as well as experimental data from literature. Theempirical LND model is able to capture the MCS results accurately, and the predictions agree reasonablywell with the experimental results over a wide range of dimensionless nucleation number (0<Wn<3).The predictions of the semi-mechanistic modified Poisson distribution (MPD) model do not agree qual-itatively with the MCS or experimental results. A sensitivity analysis shows that the MCS modellingassumptions need to capture the spatial drop distribution in the spray accurately, while the drop size dis-tribution can be assumed to be uniform. Overall, we recommend that the LND model with the parametervalues estimated be used in PBM frameworks to determine nuclei size distribution for a wide range ofexperimental conditions in mixers and fluidised granulators.