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

In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplet 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 of 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. The better neural network architecture has 10 neurons in the hidden layer with a mean square error of 1.4 10−6 and a determination’s coefficient near 1 value. The relative importance of inputs on the size of the microdroplets has been determined using the Garson algorithm and the results are in good agreement with other works.