Academic Paper

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

AiChE Journal (February 2022)

John A. Thomas, Brian DeVincentis, Johannes Wutz, Francesco Ricci

Liquid-liquid extraction processes leverage the differences in the solubilities of dissolved chemical species between the two liquid phases in order to effectively separate them. In many applications extraction is used to affect purification, whereby a desired product preferentially partitions to the opposite phase of the undesired impurities.

In addition to its utility as a means of chemical separations, other processes such as chemical reactions can occur in liquid-liquid systems. Such chemical reactions may often involve the transport of reactants and/or products between the liquid phases due to the partitioning. A critical factor impacting the rate of this mass transfer (and hence, perhaps the rate of the reaction) is the interfacial area between the liquid phases which, in the case of an agitated system, is the surface area of the dispersed droplets. While correlations exist, mechanistic models capable of predicting the droplet size distribution and the related interfacial area can be important tools in process optimization.

In this paper we present a set of unified, time-accurate, physics-based modeling approaches for predicting the dispersion properties of immiscible fluids in dynamic systems. Of particular interest are turbulent, two-fluid systems in agitated tanks with volumes ranging from 0.1 L to 1000 L and dispersed-phase droplet diameters between 10-1000 micrometers. Using these models, we seek to predict the dispersed phase droplet size distribution as a function of fluid properties, operating conditions, and system scale with no reparameterization or model tuning between scales or operating conditions. We also seek to eliminate user-defined parameters, using only physical properties and expectations from first principles turbulence theory as model input.

The model can be used as a digital scale-up and tech-transfer strategy for bioreactors used in the pharmaceutical industry. This approach is a key reason why drug products come to market faster now—especially pertinent today. 

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