Predicting Microfluidic Droplet Diameters in Glass Capillary Devices Using Machine Learning

Abstract

I have successfully generated a graphic user interface that predicts microfluidic droplet diameters from a neural network. The neural network inputs are fluid properties and geometries of 3D glass capillary devices. For water-in-oil single emulsions, the mean-squared error at the end of 100 epochs for training and validation converged to 7.2% and 7.4%, respectively. The deep machine learning model provides an alternative method of predicting droplet size without the need for rigorous theory. Moreover, the model can be altered to predict other microfluidic parameters or properties and could be extended to other fluids as well.