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 2022

Supervised Anomaly Detection in Crude Oil Stabilization

Mattia Silvestri
Primo
Software
;
Michele Lombardi
Secondo
Conceptualization
;
2022

Abstract

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 2022
2022
Proceedings of PAIS 2022
114
127
Mattia Silvestri, Michele Lombardi, Emiliano Mucchi, Luca Cadei, Giovanna Magnago, Marco Piantanida, Valentina D'Ottavio, Nguyen Van Tu, Simona Duma, Silvia Taddei, Annagiulia Tiozzo, Andrea Corneo, Lorenzo Lancia, Laura Rocchi, Pietro Coffari di Gilferraro
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/907948
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