Model-driven design using population balance modelling for high-shear wet granulation

Abstract

Model-driven design approaches have great potential to improve current engineering workflows for wet granulation and other particulate processes. The key to model-driven design is a predictive process model. In this paper, a novel predictive model is proposed for high-shear wet granulation 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 the Sobol’ indices results from a parameter sensitivity analysis, the impactful parameters to estimate are identified: critical pore saturation, and coefficients for consolidation, collision and breakage. Only impactful parameters that cannot be measured are estimated to reduce the experimental effort and improve the model's predictive power. Lab-scale experiments are designed to estimate parameters individually before fine-tuning the results. The model is assessed using a novel model validation workflow, which is based on predictions of experiments at four different scales from lab scale to pilot plant: 2 L to 70 L.