Research Projects

Wet granulation is a critical process in industries such as pharmaceuticals, chemicals, and food, where it transforms fine powders into larger granules to improve properties like flowability, compressibility, and uniformity. The process involves complex mechanisms such as agitation of granules, liquid binder addition, aggregation, breakage, and heat transfer. These mechanisms significantly influence bulk production quality and process efficiency. Despite its importance, current modeling approaches face challenges in capturing the multi-scale dynamics of wet granulation, limiting their applicability to industrial-scale systems. This project seeks to address these limitations by developing advanced bi-directional, multi-component coarse-graining (CG)-enabled PBM-DEM frameworks for wet granulation processes. By integrating the micro-scale dynamics of discrete element method (DEM), such as particle collisions and heat transfer, with the macro-scale insights of population balance models (PBM), such as size and composition evolution, the proposed framework will bridge the existing gaps in modeling granulation at industrial scales. A novel aspect of this project is analysing the effects of heat transfer and chemical composition into the PBM-DEM framework, which remains largely unexplored in current studies. To achieve these goals, the project will first develop advanced numerical and semi-analytical methods for solving multivariate population balance equations (PBEs), particularly those involving nonlinear collisional breakage and aggregation mechanisms. Simultaneously, recent coarse-graining techniques will be integrated into DEM simulations to accelerate heat transfer modeling and collision predictions, enabling the framework to handle large-scale systems with large number of particles. These advancements will form the foundation for creating a bi-directional coupling mechanism between PBM and DEM, ensuring smoother data exchange and feedback between micro-scale and macro-scale phenomena. Our objective is to create a computationally efficient and highly accurate simulation toolbox that captures the dynamics of wet granulation processes at industrial scales. This framework will provide detailed insights into process optimization, sustainable production, reducing wastage, and improving product quality.