Design of Dynamic Experiments Versus Model-Based Optimization of Batch Crystallization Processes
Design of Dynamic Experiments Versus Model-Based Optimization of Batch Crystallization Processes
复制标题
动态实验设计与基于模型的间歇结晶过程优化
DOI：
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发表时间：
2011
期刊：
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通讯作者：
C. Georgakis
中科院分区：
文献类型：
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作者：
A. Fiordalis;C. Georgakis
摘要将一种新的数据驱动的优化方法应用于间歇冷却结晶模拟，并与基于模型的优化方法进行比较。动态实验设计方法[Georgakis，2009]是经典实验设计方法的扩展，可应用于时变轮廓对于优化过程关键目标非常重要的任何过程。作为一种数据驱动的方法，流程优化不需要第一原理模型，这种方法对于不存在知识驱动模型的复杂流程或目标函数无法建模的复杂流程可能特别有用。
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.