Design of Dynamic Experiments Versus Model-Based Optimization of Batch Crystallization Processes

Abstract A new data-driven optimization methodology is applied to a batch cooling crystallization simulation to evaluate how it compares with a model-based optimization technique. 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 are important for optimizing key objectives 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 or where the objective function cannot be modeled.