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Summary

This study introduces a model-based, real-time control strategy for batch freeze-drying. It accelerates primary drying and prevents product collapse by optimizing critical process parameters, even during disturbances.

Keywords:

dynamic design space freeze-drying primary drying supervisory process control uncertainty analysis

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Area of Science:

Pharmaceutical Sciences
Chemical Engineering
Process Control

Background:

Batch freeze-dryers traditionally operate on fixed protocols, leading to inefficiencies and extended processing times.
Current methods often maintain constant critical process parameters (CPPs), which can be suboptimal and time-consuming.
Product structure loss due to exceeding collapse temperature (Tc) and pressure control loss are significant risks.

Purpose of the Study:

To develop and validate a model-based optimization and real-time control strategy for batch freeze-drying.
To accelerate the primary drying phase while ensuring critical quality attributes (CQAs) are maintained.
To mitigate risks of product collapse and pressure control loss during process disturbances.

Main Methods:

Implementation of a model-based control strategy incorporating model output uncertainty.
Real-time computation of a design space to select optimal CPPs in each iteration.
Experimental validation under intentional process disturbances in chamber pressure and shelf fluid system.
Prediction of the primary drying phase end using uncertainty analysis and comparative pressure measurement.

Main Results:

The real-time control strategy successfully accelerated primary drying while preventing product structure loss.
The strategy effectively mitigated the impact of moderate process disturbances on freeze-drying.
Predictions for sublimation interface temperature (Ti) and the end of primary drying aligned well with experimental data.
The method demonstrated the capability to avoid exceeding collapse temperature (Tc) and maintain pressure control.

Conclusions:

The proposed model-based real-time control strategy enhances batch freeze-drying efficiency and robustness.
This approach allows for faster processing while safeguarding product quality and process stability.
The methodology provides a reliable means to manage process disturbances and predict critical endpoints.