Different methods are used at Corteva^® Agriscience to improve our understanding of mixing in large-scale mechanically agitated fermentors. These include (a) use of classical empirical correlations, (b) use of small-scale models, and (c) computational fluid dynamics (CFD). Each of these approaches has its own inherent strengths and limitations. Classic empirical or semi-empirical correlations can provide insights into mass transfer, blending, shear, and other important factors but are dependent on the geometry and condition used to develop the correlations. Laboratory-scale modelling can be very useful to study mixing and model the effect of heterogeneity on the culture, but success is highly dependent on the methodology applied. CFD provides an effective means to accelerate the exploration of alternative design strategies through physics-based computer simulations that may not be adequately described by existing knowledge or correlations. However, considerable time and effort is needed to build and validate these models. In this paper, we review the various approaches used at Corteva Agriscience to deepen our understanding of mixing in large-scale fermentation processes.

不同的方法在Corteva使用^®农业科学，以提高我们对大型机械搅拌发酵罐中混合的理解。其中包括（a）使用经典经验相关性，（b）使用小规模模型，以及（c）计算流体动力学（CFD）。这些方法中的每一种都有其固有的优势和局限性。经典的经验或半经验相关性可以提供有关传质，混合，剪切和其他重要因素的见解，但取决于用于建立相关性的几何形状和条件。实验室规模的建模对于研究混合和建模异质性对培养物的影响非常有用，但是成功与否很大程度上取决于所采用的方法。CFD提供了一种有效的手段，可以通过基于物理学的计算机模拟（可能无法通过现有知识或相关性充分描述）来加快探索替代设计策略的速度。但是，建立和验证这些模型需要大量的时间和精力。在本文中，我们回顾了Corteva Agriscience所使用的各种方法，以加深我们对大规模发酵过程中混合的理解。