Machine learning model for nitrous oxide detection
This technology is a machine learning model that can detect nitrous oxide production and recommend mitigation strategies for emission reduction.
Unmet Need: An inexpensive and continuous method for detecting nitrous oxide emissions
Current methods for detecting and reducing greenhouse gases are costly and lack the capacity for continuous, real-time monitoring. Although several mathematical models have been developed to predict emissions, they are constrained by the limitations of their underlying equations. Moreover, these systems require extensive calibration and maintenance, further driving up the cost. As a result, there are currently no affordable solutions on the market that offer continuous detection of greenhouse gases.
The Technology: A predictive, real-time machine learning model for detection of nitrous oxide emissions
This technology is a machine learning model designed to predict and mitigate nitrous oxide emissions. It leverages flux measurements of nitrous oxide to train the model in detecting emission events based on nitrogen compound dynamics, and it recommends strategies to reduce emissions and optimize system performance. Additionally, the model enables visual analysis to help users understand the biochemical transformations associated with changes in process operation and how operational adjustments can directly improve their system.
Applications:
- Predictive tool for reducing greenhouse gas emissions
- Research platform for studying greenhouse gas dynamics
- Early detection of emission spikes
- Decision-support system for nitrogen management
Advantages:
- Predictive capability
- Real-time detection
- Cost-effective
- Offers actionable mitigation strategies
- High-speed processing
Lead Inventor:
Patent Information:
Related Publications:
Tech Ventures Reference:
IR CU25437
Licensing Contact: Dovina Qu