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

    Year: 2022

    Journal: Chem. Eng. Res. Des., Volume 187, NOV, page 584–597

    Authors: Lokanathan, Manojkumar; Buffington, Tyler; Wimalarathne, Sarith; Bahadur, Vaibhav

    Organizations: NSF; Vice President for Research Office at UT Austin; [CBET- 1805179]

    Keywords: Machine learning; Droplets; Coalescence; Electrowetting; Surfactant; Artificial neural network

    Electrowetting (EW) is a widely-studied microfluidic technique to promote coalescence of droplets and also to 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 ( i/ i)), to predict nine targets: uncovered area ratio ( f), final droplet density normalized with uncovered area ( f/ f), and seven droplet density distribution (radius) bins. ANN was the most accurate predictive tool among the three ML models with R2 of 0.89. ANN accurately predicted the droplet distribution bins for three distinct cases of good electrocoalescence, poor elec-trocoalescence 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 delta f/alpha f, 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 to 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. f/ f (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.
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