Artifical neural network-based predictions of surface electrocoalescence of water droplets in hydrocarbon media


Electrowetting (EW) is a widely-studied microfluidic technique to promote coalescence of droplets as well as generate droplets. However, analytical or computational modeling of the intricate phenomena associated with electrocoalescence of water droplets in hydrocarbon media is very challenging. Presently, EW-assisted surface electrocoalescence experiments are used to train machine learning (ML) algorithms. These comprise an artificial neural network (ANN), eXtreme gradient boosting (XGBoost) and polynomial regression. These models are then used to predict the influence of parameters such as applied voltage, frequency, electrode spacing, concentration and initial droplet density normalized with uncovered area ratio (δii), to predict nine targets: uncovered area ratio (αf), final droplet density normalized with uncovered area (δff), and seven droplet density distribution (radius) bins.

ANN was the most accurate predictive tool among the three ML models with R 2 of 0.89. ANN accurately predicted the droplet distribution bins for three distinct cases of good electrocoalescence, poor electrocoalescence and satellite droplet ejection (droplet generation). SHAP (Shapley Additive exPlanations) dependence plots were used to quantify the parametric influence of various parameters on each output. For δff, frequency and electrode spacing were the most and least influential, respectively. Interestingly, the feature influence on droplet density distributions was observed to reverse (magnitude and direction) with droplet radius. The key novelty of this study is the coupling of data from a multifunctional microfluidic device in an ANN model to accurately predict complex stochastic droplet-related phenomena (coalescence, generation). As such, the framework developed here can be utilized for other data-rich droplet-based microfluidic systems.

Chemical Engineering Research and Design
Manojkumar Lokanathan
Manojkumar Lokanathan
Research Scientist in Thermal Fluids Sciences

Manoj is a research scientist at C-Crete Technologies and has a doctorate from the University of Texas at Austin. His research expertise encompasses the fields of power electronics management, two-phase flow, thermal sciences, microfluidics, emulsion chemistry, surface sciences and machine learning.