Towards Autonomous Crystallization Systems Through Advanced Process Modelling and Control

This thesis aims to improve our current understanding and ability to control complex batch (cooling, antisolvent and/or combinations thereof) crystallization processes by developing (a) novel predictive crystallization process model(s) which link the adjustable process input parameters to the critical output crystal product specifications (e.g., particle size, yield, morphology). The developed process model(s) will then be used to build real-time advanced (automatic) control strategies to ensure that product specifications are robustly met across different scales regardless of process disturbances. The results are expected to reduce the laborious experimental processes currently required to iteratively optimize crystallization processes. By combining advanced process monitoring, (model-based and model-free) system representations and experimental techniques for complex cooling / antisolvent crystallization processes, this thesis aims to enable robust control strategies for diverse chemical compounds at different scales, with easy generalization to different modes of operation and varied process chemistry.