OpenPEMS™ is a free and open source predictive emissions monitoring system that trains models to predict air emissions, such as NOx and SO2, which are typically monitored by Continuous Emissions Monitoring Systems (CEMSs). OpenPEMS™ is inspired by OpenAI's ethos of accessibility and empowerment. Our vision is to make Artificial Intelligence (AI) and Machine Learning (ML) technologies accessible to a wider industrial audience, thereby reducing costs associated with air emissions monitoring.

The roots of PEMSs development trace back to my  Ph.D. study  conducted in collaboration with industry partner Cenovus Energy. This work culminated in an IEEE publication in 2019, co-authored by Cenovus Energy. In 2022, the model we developed received approval from the Alberta Energy Regulator and Alberta Environment and Protected Areas (AEPA) for regulatory reporting. Since then, several Calgary-based companies tried to copycat the success. Check out who they are here.

Learn more about this project.

OpenPEMS™ is an ideal solution for companies seeking backup systems or alternatives to CEMSs. For facilities in need of integrated PEMS solutions, major players like ABB, Rockwell, and Honeywell offer comprehensive PEMS solutions that seamlessly integrate with Distributed Control Systems (DCS), data storage, and reporting systems.