Abstract
In 2023, the International Council for Harmonisation (ICH) guideline for the development, implementation, and lifecycle management of pharmaceutical continuous manufacturing (PCM), was implemented in Europe. It promotes quality-by-design (QbD) and quality by control (QbC) strategies as well as the appropriate use of mathematical modelling. This development urges a harmonizing understanding across academia and industry for adoption of interpretable models instead of black-box models for advanced control strategies such as model predictive control (MPC), especially when applied in Good Manufacturing Practice (GMP) regulated areas. To this end, we first propose a comprehensive model development using Dynamic Mode Decomposition with Control (DMDc)to represent complex dynamics in a lower-dimensional space, disambiguating between underlying dynamics and actuation effects. Using data from a digital twin of PCM, our model demonstrates low computational complexity while effectively capturing nonlinear dynamics with significant improvements observed in the performance metrics. Additionally, we demonstrate enhanced uncertainty propagation performance when compared to state-space models obtained with N4SID and Sparse Identification of Nonlinear Dynamical systems with control algorithms. Finally, we develop a closed-loop workflow that seamlessly connects data exchanges between Python (DMDc), GAMS (MPC optimisation) & gPROMS to evaluate the controller performance with setpoint tracking and disturbance rejection studies achieving high accuracy in real-time monitoring and control of granule size. This study offers a novel, interpretable control strategy for PCM. The results demonstrate the potential for real-time release testing, reduced reliance on end-product testing, and improved process control in the pharmaceutical industry.