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 critical process parameters (CPPs) most impactful modelling followed by targeted experimental campaign prioritise calibration these. To identify these it is essential perform global sensitivity analysis (GSA). Here, an GSA applied wet granulation case study Sobol’ indices used CPPs parameters. The population balance, mechanistic that considerable computational effort for so Gaussian Process surrogate utilised interrogate underlying model. These key results reduce input-space 80% enabling proposal workflow. substantially improves ability deploy model-based determine parameter values, reducing 42.1% compared conventional design.