Wet granulation is a key process used to make formulated particulate products across a wide range of industries. Granular products typically have at least one desired function, and in many cases there are several key performance characteristics which are required. Recent work has shown great improvement in the ability to model granulation process to predict granular properties such as size, however the ability to predict granular function is lacking, as is the ability to design processes to give desired granular function.
The primary aim of this work is to develop linked process and product performance models for wet granulation, and to initiate the inverse problem solving process, i.e. to investigate the ability to predict required process parameters to give desired performance characteristics. This is being performed for a case study of a high shear wet granulation process, coupled with a new model which describes granule disintegration.
Due to the relative immaturity of granular product performance models, much of the focus of this work has been on the development of a model to describe granule disintegration. Of particular importance is the suitability of this model for coupling with existing population balance models to enable model linking.
In this report, an improved model for granule disintegration is presented, which has been simplified to reduce the number of parameters required. A local sensitivity analysis is shown, which shows that decreasing granule porosity and constituent particle size contribute to smaller granule populations over time, due to an increased number of breakage events. Increasing the maximum absorption ratio of disintegrants in the model acts to decrease particle size. The effect of starting granule size is somewhat more complex, but indicates a potential threshold in normalized granule size behavior, above which the normalized size distribution becomes independent of the starting granule size. This however requires further research to confirm.
Initial experimental validation has been presented using a bespoke flow cell, optical microscopy and Optical Coherence Tomography (OCT), alongside a new image analysis app to provide data required for model parameterisation and validation. Preliminary parameterization has been performed, and a good fit to experimental data is demonstrated, however further work is required to verify, validate and parameterise the model.
A summary of the mechanistic high shear wet granulation model is presented, which is well developed and implemented in gPROMS FormulatedProducts. Tasks for the remainder of this project will focus on experimental validation of the new disintegration model, global sensitivity analysis, linking of the process and product performance models, and inverse problem solving. Additional resources at the University of Sheffield and the University of Strathclyde are being used to assist in the experimental validation, global sensitivity analysis and inverse problem solving.