insights

09 Jun 2020 By Sumi

Machine learning techniques for fuel loss detection at service stations

Coupling industry expertise and university research to develop effective data analytic and machine learning techniques for identifying and quantifying the different sources of fuel losses from underground petroleum storage systems (UPSSs) at service stations.

EMS & RMIT SECURES ARC GRANT TO DESIGN THE NEXT GENERATION OF WETSTOCK MANAGEMENT

Australian Research Council (ARC) has announced that RMIT researchers in collaboration with industry partner Environmental Monitoring Solutions Pty Ltd (EMS) (specialists in Wetstock Management, SIR Leak Detection, Real-time Fuel Data analytics and Forecourt Automation for the past 25 years), has been one of the 5 organisations to receive the grant as part of the recent ARC Linkage Projects Grants Scheme funds for collaborative research and development projects.    

The Linkage Projects Grants Scheme funds collaborative research and development projects between higher education researchers and partner organisations. Deputy Vice-Chancellor Research and Innovation Professor Calum Drummond said RMIT was renowned for the strength of our national and international industry relationships. “These grants are a recognition of RMIT’s continued success in collaborative research partnerships,” he said.  “They’ll see our researchers continue to do what they do best – making a real and positive difference that will benefit our whole community.”  

EMS and partner RMIT will use the funds to develop data analytic practices and automation, as well as to enable better and faster decision making through harnessing effective machine learning methods.  

A COMPLEX CHALLENGE

Fuel losses such as leakage from underground fuel storage systems is a common problem globally. It causes significant contamination of soil and groundwater, with the resulting health implications, and often requires costly remediation. Traditional compliance-based tools and software used to monitor fuel losses rely heavily on conventional statistical methods and have limitations in practical use. Machine learning provides a viable and powerful alternative which can potentially revolutionize the business practice in this industrial sector.  

THE BEST OF RMIT & EMS LEADING THE PROJECT
The Chief Investigators Prof. Xiaodong Li and Dr. Jeffery Chan (RMIT) together with Partner Investigator Erica Scott (EMS) and Dr. Li Chik (EMS) will be mainly responsible for carrying out the project.
EMS Managing Director, Russell Dupuy said: “EMS is very excited to work in partnership with RMIT. Our research team has been modelling machine learning for a few years now, this collaboration will further enhance our technology solutions for the benefit of all clients and the market through proven science and research. We look forward to celebrating this outcome with all involved and contributing to improving current Fuel Loss detection techniques significantly.” 
The announcement underpins the work and research undertaken at RMIT to support initiatives in AI and machine learning. The successful outcomes of this research will bridge the gap between the research community and practitioners from the energy industry. It is expected to bring greater understanding underpinning UPSS problems and provide tangible benefits to the industry.

Checkout our full article in the latest issue of ACAPMA Mag: https://acapmag.com.au/2020/06/machine-learning-techniques-for-fuel-loss-detection-at-service-stations/

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