Machine learning for early detection of distillation column flooding

• Demonstrates effectiveness in realistic navigation scenarios with obstacles via simulation. • The issue with data scarcity surrounding the application of supervised ML for flooding detection is addressed. • The time-series generative adversarial network is used to generate synthetic data while preserving the temporal order. • Sets of industrial data are used to demonstrate the efficacy of the algorithm. Flooding in a distillation column is an abnormal event that limits the operations of the column and eventually leads to plant shutdown if not prevented. Recently, machine learning (ML) has been widely employed in process engineering to uncover critical patterns in data. Supervised ML methods can predict flooding by monitoring pressure changes in the column. However, one of the challenges of applying supervised ML methods for predicting flooding in distillation columns is the need for large volumes of flooding data. Flooding events are rare compared to normal operations, resulting in an imbalanced dataset. To address this, we used a time-series generative adversarial network to generate synthetic flooding data by preserving the temporal patterns of the original dataset. With this extra data, we trained supervised ML models to predict flooding by forecasting the pressure drops. Our results show that flooding can be detected 19 min in advance, and supervised ML methods outperformed unsupervised ML methods like PCA and Autoencoders in early detection.