This model captures critical phenomena in each unit operation, including colloidal silica (de)polymerization, gelling and crystal growth via Ostwald ripening and aggregation. It also predicts mass transfer during the solid–liquid protonation step under various sparging and recirculation conditions. In the final drying stage, the model combines mass and population balance equations to describe moisture evolution and particle size distribution.
By combining mechanistic descriptions with empirical data through parameter estimation, the model enables prediction of critical quality attributes (CQA) such as particle size distribution and cation exchange capacity (CEC)s. Model validation against experimental data shows predictions within acceptable error margins, confirming its accuracy and value for process development and scale-up.
Furthermore, we created a web application to democratize model usage, allowing scientists to leverage this “digital-twin” for designing more targeted experiments and reducing costly large-scale trials. The Python-based web application allows performing end-to-end “what-if-analysis” and Global Sensitivity Analysis (variance-based sensitivity analysis). Overall, this digital framework complements design-of-experiments approaches by providing a virtual DoE playground, minimizes trial-and-error experimentation, accelerates process optimization and scale-up, and advances Quality by Digital Design in pharmaceutical manufacturing.