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Field Demonstration of Artificial Intelligence Powered Predictive Emissions Monitoring Systems
Solution Developer
VL Energy Ltd.
Project Description
VL Energy has developed a first-of-its-kind artificial intelligence (AI) powered Predictive Emissions Monitoring Systems (PEMSs). Using cloud computing, the PEMSs utilize the operating parameters of combustion equipment through AI methods to power predictive models for emission monitoring.
Industrial facilities with large stationary combustion sources are required to equip with one or more Continuous Emissions Monitoring Systems (CEMSs) to monitor air emissions for compliance with regulatory emission limits. However, CEMSs require high initial capital and operating costs, as well as frequent maintenance and operator training.
VL Energy’s monitoring systems can be used for process optimization, air emissions (NOx, SO2) monitoring, and preventative and predictive maintenance to reduce GHG emissions from non-routine venting, flaring and fugitives. In addition, this technological solution eliminates the health and safety hazards that are typical in CEMSs sampling processes.
Compared to existing CEMSs, VL Energy’s systems have the potential to reduce direct and indirect maintenance costs by 80% per year and have almost no engineering, procurement and construction (EPC) costs.
Project Resources
Project Video
https://www.youtube.com/embed/CPJPGMrPp0Q
Maximum Funding from CRIN
$ 1,000,000
Collaborators
Suncor
Mitacs
Alberta Innovates
The National Research Council of Canada Industrial Research Assistance Program (NRC IRAP)
University of Calgary
Queens University
Petroleum Technology Alliance of Canada (PTAC)
Main Project Contact
Ling Bai
[email protected]
Technology Readiness Level
TRL 9
