VL Energy is charting the future of emissions management with our AI-powered Predictive Emissions Monitoring System (PEMS). Our recent white paper, published in collaboration with experts from Suncor Energy and Petroleum Technology Alliance Canada, details this innovative leap.
The cogeneration unit at Suncor Firebag’s site in Alberta served as the proving ground for VL Energy's cloud-based PEMS. Demonstrating its prowess, PEMS provided accurate forecasts for emissions like NOₓ concentration, mass flow rate, and temperatures of the flue gas at the exhaust stack, meeting Alberta's regulatory standards with finesse.
The study underscores the advantages of PEMS over traditional Continuous Emissions Monitoring Systems (CEMS), notably in robustness and operational efficiency. PEMS, utilizing deep learning models, operates at about half the capital cost and only 10-20% of the operational expenses of CEMS. It also guarantees real-time monitoring and data availability during downtimes, ensuring uninterrupted compliance.
Our deep dive reveals the meticulous process of developing these deep learning models. It begins with extensive data collection from sensors, followed by robust data preprocessing to ensure model resilience. The process involves layers of model training, validation, and stringent testing to establish the most accurate and reliable system for deployment.
PEMS's architecture integrates cutting-edge neural networks and attention layers, analyzing both time-series and structured datasets from combustion devices. Additionally, it identifies and rectifies sensor drifts and failure, ensuring data integrity.
This tech leap is not just a stride in monitoring—it's a full-on sprint towards operational excellence. By providing a real-time dashboard, PEMS empowers operators to preemptively manage emissions, ensuring regulatory compliance and forging the path to net zero.
VL Energy's innovation is a beacon for the industry, illuminating the way to a sustainable future through smarter technology and environmental stewardship.
Read the Published Paper Here:
Stacked deep learning models predict cogeneration unit gas emissions | Oil & Gas Journal (ogj.com)