Efficient global sensitivity-based model calibration of a high-shear wet granulation process

Model-driven design requires a well-calibrated model and therefore needs efficient workflows to achieve this. This efficiency can be achieved with the identification of the critical process parameters (CPPs) and the most impactful modelling parameters followed by a targeted experimental campaign to prioritise the calibration of these. To identify these parameters it is essential to perform a global sensitivity analysis (GSA).

Here, an efficient GSA is applied to a wet granulation case study with the Sobol’ indices used to identify the CPPs and impactful modelling parameters. The population balance, mechanistic model that is used requires considerable computational effort for a GSA so a Gaussian Process surrogate is utilised to interrogate the underlying model. These key results reduce the input-space by 80% enabling the proposal of a targeted experimental design and model calibration workflow. This substantially improves the ability to deploy model-based design to determine the impactful parameter values, reducing the experimental effort by 42.1% compared to a conventional experimental design.