Model-based scale-up and optimisation is a powerful technique for achieving the desired product quality and reducing the cost of experimentation and the time to market. For complex crystallization processes, population balance modelling is capable of predicting the effect of the batch recipe on the particle size distribution (PSD) of the final product. However, this technique is mathematically complex and has traditionally required deep knowledge of specialist areas. New tools allow engineers to apply population balance modelling without highly-specialised numerical skills.
This webinar will present a case study illustrating the application of mechanistic model-based crystallization process analysis, scale-up and optimisation.
- Experiments conducted at the lab and bench scale
- Choosing a model to describe the cooling crystallization process
- Using lab-scale data to estimate kinetic parameters for crystal growth and secondary nucleation
- Uncertainty analysis on the impact of the kinetic parameter uncertainty on the model predictions of the Critical Quality Attributes (CQAs)
- Sensitivity analysis to identify the Critical Process Parameters (CPPs) to control and/or optimise in the process to achieve CQAs
- Optimising the recipe followed by validation of the optimised recipe at lab scale
|Dr Niall Mitchell is a Senior Consultant and Product Manager of gPROMS FormulatedProducts. He is a Chemical Engineer with a PhD on the modelling and experimental aspects of crystallization from the University of Limerick and has several years of industrial experience in crystallization and solid processing.|