Supervised Anomaly Detection in Crude Oil Stabilization

In recent years, pervasive digitalization has affected the industrial world, including the oil and gas sector. With more and more data becoming available, Machine Learning algorithms have become a promising tool to improve Predictive Maintenance operations. In this work, we have designed an alerting system that notifies the site operator with an adequate advance when an anomaly is going to occur. In particular, we focus our analysis on the stabilization column of an Oil Stabilization Facility to prevent the column bottom temperature to overcome safety boundaries. The experimental analysis demonstrates that our system provides reliable results, in terms of both identified anomalies and false alarms. In addition, the system is currently under deployment on the company computing infrastructure and the first working version will be available by the end of May 202