Over the past decades our understanding of the wet granulation process and our ability to predict it computationally have made significant strides. However, despite these advancements we have yet to fully leverage models for granulation and other particulate processes to optimize the predictive design of granular product performance.
The goal of this research is to bridge this gap by linking process models with product performance models for wet granulation. To this end, a novel granule performance model has previously been developed within this project. This multi-scale model, which simulates swelling-driven granule disintegration and dispersion, has been specifically designed to integrate with existing wet granulation process models.
To validate these models, novel experiments were conducted in collaboration with the University of Strathclyde. The experimental results provided essential data for parameterizing the models, and have also offered deeper insights into the rate processes governing granule disintegration and dispersion.
Recent work has focussed on the recruitment of a new researcher who will support the project, Amir Arjmandi-Tash. The development of a surrogate model using Gaussian Process regression has commenced, to enable the eventual solution of the inverse problem.