Causal and Interpretable Rules for Time Series Analysis

International audienc

Prediction of flooding in distillation columns using machine learning

This work presents a new data-driven approach for early detection of anomalies, namely flooding, in distillation columns. The main contribution of this approach is that it does not rely on direct pressure measurements of the flooded sections to detect flooding. Instead, it relies on real-time measurements such as flow rates, liquid levels, temperatures, top and bottom column pressure and domain indicators. These measurements and indicators are used to train a binary classification random forest model to predict the risk of reaching a pre-flooding operation state. Results from the application of this machine learning model in a TotalEnergies refinery located in France, show that flooding events can be detected, in average, 37 min in advance, whilst keeping a low number of false negative predictions. The model, deployed in the refinery in 2019, has allowed the process engineers to anticipate and prevent flooding events by taking corrective measures well in advance. The work is patented by the authors under patent number WO2021185889