Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation

What is it about?

In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplets dimensionless size in a microfluidic system in order to formulate a water in oil emulsion. The various parameters that affect the size of microdroplets (flow rates, viscosities, surface tensions both the two phases and the diameter of the microchannel) are studied and further grouped into dimensionless numbers; we used these numbers as input to the neural network and the dimensionless length as output.

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Why is it important?

This is the first study concerning modeling the size of droplets in T-junction for a range of Capillary number from 2.2 10-4 to 0.24 in one model. This concept is very important in encapsulation of the active ingredient (pharmaceutical, cosmetic or food) and for the modeling of transport phenomena in the microdroplet.