Wet granulation processes are difficult to scale up because conventional methods require ample experimental data at all scales to determine the most favourable operating conditions. A systematic model-driven design framework can facilitate this scale-up process by reducing the number of experiments required. To achieve this, a predictive model is required which should be developed based on a good understanding of all major wet granulation mechanisms. Such a model can give a better insight into the process and the effects of the operating conditions on the granulation endpoint which is needed for process design and scale-up studies.
In this study, a new nucleation model is developed to predict the nuclei size distribution. For the first time, model predictions are in good agreement with nucleation experiments over a wide range of operating conditions.
A novel predictive high-shear wet granulation model is developed using a one-dimensional population balance modelling framework. The wet granulation mechanisms are represented by rate expressions which are based on mechanistic understanding. Material characterisation tests and granulation experiments are designed to verify critical modelling assumptions and determine the modelling parameters. Based on a generic sensitivity analysis approach, the impactful parameters to estimate are identified: critical pore saturation, and coefficients for consolidation, collision and breakage. The model is validated based on predictions of experiments across four different scales from 2L to 70L, which is a novelty.
A novel model-driven design approach for process scale-up is proposed and applied to a high-shear wet granulation process in a case study. The model predictions are used for process design at pilot scale by visualising the predicted process behaviour in new operating performance maps for the key granule properties like size and porosity. The optimum operating range is identified by predicting the required conditions to fulfil product specifications. For the industrial implementation, detailed guidelines are given for all essential model-driven design tasks that are required for scale-up. Using model-driven design, industrial scale-up is improved to significantly reduce the experimental effort.
