The standard operation of a batch freeze-dryer is protocol driven. All freeze-drying phases (i.e., freezing, primary and secondary drying) are programmed sequentially at fixed time points and within each phase critical process parameters (CPPs) are typically kept constant or linearly interpolated between two setpoints. This way of operating batch freeze-dryers is shown to be time consuming and inefficient. A model-based optimisation and real-time control strategy that includes model output uncertainty could help in accelerating the primary drying phase while controlling the risk of failure of the critical quality attributes (CQAs). In each iteration of the real-time control strategy, a design space is computed to select an optimal set of CPPs. The aim of the control strategy is to avoid product structure loss, which occurs when the sublimation interface temperature ( $T_i$ ) exceeds the the collapse temperature ( $T_c$ ) common during unexpected disturbances, while preventing the choked flow conditions leading to a loss of pressure control. The proposed methodology was experimentally verified when the chamber pressure and shelf fluid system were intentionally subjected to moderate process disturbances. Moreover, the end of the primary drying phase was predicted using both uncertainty analysis and a comparative pressure measurement technique. Both the prediction of $T_i$ and end of primary drying were in agreement with the experimental data. Hence, it was confirmed that the proposed real-time control strategy is capable of mitigating the effect of moderate disturbances during batch freeze-drying.

During the spin freezing step of a recently developed continuous spin freeze-drying technology, glass vials are rapidly spun along their longitudinal axis. The aqueous drug formulation subsequently spreads over the inner vial wall, while a cold gas flow is used for cooling and freezing the product. In this work, a mechanistic model was developed describing the energy transfer during each phase of spin freezing in order to predict the vial and product temperature change over time. The uncertainty in the model input parameters was included via uncertainty analysis, while global sensitivity analysis was used to assign the uncertainty in the model output to the different sources of uncertainty in the model input. The model was verified, and the prediction interval corresponded to the vial temperature profiles obtained from experimental data, within the limits of the uncertainty interval. The uncertainty in the model prediction was mainly explained (>96% of uncertainty) by the uncertainty in the heat transfer coefficient, the gas temperature measurement, and the equilibrium temperature. The developed model was also applied in order to set and control a desired vial temperature profile during spin freezing. Applying this model in-line to a continuous freeze-drying process may alleviate some of the disadvantages related to batch freeze-drying, where control over the freezing step is generally poor.