Freeze drying (lyophilization) offers an attractive dehydration method for valuable food and biological products, because it is capable of preserving product quality and biological activity while extending their shelf life. However, despite these benefits in terms of product quality, freeze drying is also a notoriously energy-intensive and time-consuming process. This requires an expensive operation to construct an efficient optimal decision-making tool able to drive the operation through the most effective paths that minimize time and maximize product quality. Here we propose an integrated approach to operational design and control of the freeze-drying process that combines dynamic modeling with efficient optimized off-line and on-line control. The required mass and energy balance equations still contain inherent nonlinearity, even in their lumped parameter version. This results in a set of complex dynamic, computationally costly optimization problems solved by selected global stochastic optimization algorithms. Real-time disturbances and model uncertainties are addressed via the proposed hierarchical multilevel approach, allowing recalculation of the required control strategies. The framework developed has been revealed as a useful tool to systematically define off-line and on-line optimal operation policies for many food and biological processing units.
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