Machine learning techniques for fuel loss detection at service stations

Working with Environmental Monitoring Solutions (EMS), this project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations.

Project title: Machine learning techniques for fuel loss detection at service stations

Project dates: ​2020-2022

Grants and funding: ​ARC Linkage Project 19

Description

Working with Environmental Monitoring Solutions (EMS), this project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. 

Research strategy

The project expects to use machine learning, signal processing and anomaly detection to develop the techniques and test them with live data at service stations. The expected outcomes are a set of tailor-made machine learning techniques for effective fuel loss detection and a software suite that can be easily incorporated into the normal operation of service stations. 

Rationale

Early detection of leaks reduces the environmental damage and the financial costs to the petroleum industry in waste and fines.

Key people

  • Xiaodong Li
  • Jeffrey Chan
  • Erica Scott
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Acknowledgement of country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business. - Artwork created by Louisa Bloomer