  Predicting drop coalescence based on process parameters is crucial for
experiment 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 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 on real experimental data. Numerical
results show that considerable improvement in prediction accuracy can be
achieved by using synthetic data and the proposed DSCVAE clearly outperforms
the standard CVAE. This research clearly brings more insight into handling
imbalanced data for classification problems, especially in chemical engineering
