Issue 23, 2023

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Abstract

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.

Graphical abstract: Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Article information

Article type
Paper
Submitted
22 déc. 2022
Accepted
21 avr. 2023
First published
27 avr. 2023
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2023,25, 15744-15755

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

K. Zhu, S. Cheng, N. Kovalchuk, M. Simmons, Y. Guo, O. K. Matar and R. Arcucci, Phys. Chem. Chem. Phys., 2023, 25, 15744 DOI: 10.1039/D2CP05975D

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