This technology is a machine learning model that can detect nitrous oxide production and recommend mitigation strategies for emission reduction.
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.
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.
IR CU25437
Licensing Contact: Dovina Qu
