Optimizing Batch Crystallization Cooling Profiles: The Design of Dynamic Experiments Approach

Abstract A new data-driven methodology for optimizing time-variant profiles in batch processes without the need for a first-principles model is applied to a batch cooling crystallization to find the optimum cooling trajectory that minimizes the total amount of nucleation during the crystallization. The method, Design of Dynamic Experiments (Georgakis, 2009), is an extension of the classical Design of Experiments approach and can be applied to any process where time-variant profiles, typically batch and semi-batch operations, are important for optimizing key aspects of the process. As a data-driven approach with no first-principles model required for process optimization, this methodology may be particularly useful for complex processes for which no knowledge-driven model exists.

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