Gas Density Imaging for Adaptive Fugitive Emission Rate Measurement
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Solution Overview
Problem
Existing gas imaging systems struggle with false positives and negatives, limited accuracy in leak source attribution, and inefficient quantification of emission rates due to fixed scanning patterns and noise interference, especially in detecting fugitive gas emissions from mining activities.
Innovation Solution
An adaptive gas imaging system that dynamically adjusts camera bearing and zoom based on fugitive gas presence in the field of view, using a computing device to optimize image capture and stitch multiple images together for accurate emission rate calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed scanning pattern is used to detect gas plumes, then the scanning process is simple and systematic, but false positives from noise increase and detection accuracy decreases
Solution Approach 1:
The patent implements dynamic scanning patterns that adapt in real-time based on detected gas plume characteristics. The system adjusts scanning parameters such as frame rate, field of view, and scanning speed dynamically, transitioning from systematic fixed patterns to adaptive dynamic patterns that focus computational and sensing resources on regions with actual emissions, thereby improving detection accuracy while managing system complexity
Solution Approach 2:
The system employs feedback mechanisms where detection results from initial scanning inform subsequent scanning behavior. When gas plumes are detected, the system feeds this information back to adjust scanning parameters, recenter on detected sources, and allocate resources preferentially to areas with emissions. This feedback loop enables the system to distinguish true positives from noise by iteratively refining detection based on observed patterns
2Measurement precision
If the imager scans a finite field of view at predetermined frames, then the scanning process is efficient and systematic, but large plumes spread across multiple frames are misattributed and leak rate quantification accuracy is reduced
Solution Approach 1:
The patent implements continuous tracking of detected plumes across multiple frames rather than treating each frame as independent. The system maintains continuous observation of plume evolution, source location, and spatial extent over time, enabling accurate attribution even when plumes span multiple predetermined frames. This continuous observation approach preserves measurement precision while optimizing the time required for complete plume characterization
Solution Approach 2:
The system performs preliminary detection at standard scanning rates, then activates enhanced continuous tracking and monitoring modes when emissions are detected. This preliminary action approach allows the system to efficiently scan large areas initially, then concentrate resources on areas with actual emissions, achieving both efficiency and precision without requiring continuous high-resource operation throughout the entire scanning process
3Reliability
If recentering and zooming is performed upon plume detection, then the system can focus on the plume origin, but the likelihood of attributing emission to incorrect source increases and false negatives occur
Solution Approach 1:
The patent implements dynamic adjustments to bearing and zoom parameters based on real-time analysis of gas density image characteristics. Rather than automatic recentering and zooming upon any plume detection, the system dynamically evaluates whether adjustments are warranted based on plume characteristics, signal-to-noise ratio, and confidence metrics. This dynamic approach improves source attribution accuracy while avoiding premature or incorrect recentering actions that could lead to false attribution
Solution Approach 2:
The system employs feedback mechanisms where the results of recentering and zooming operations are evaluated to determine whether the detected plume origin is genuine or spurious. The system monitors whether plumes persist after recentering, whether zoomed views confirm the source, and whether the detected features are consistent with actual emissions. This feedback evaluation reduces false attribution while maintaining the benefits of focused imaging when appropriate
Data Source
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AI summary
Systems and methods are described for calculating an emission rate of a fugitive gas based on a gas density image of the fugitive gas. In an example, a computing device receives a gas density image of a fugitive gas from a camera. The computing device determines how to optimize the fugitive gas in the camera's field of view and instructions the camera to adjust its bearing and zoom accordingly. The camera captures one or more additional images of the fugitive gas, and the computing device stitches the images together where appropriate. The computing device then calculates the emission rate by delineating the fugitive gas in the image and determining a flux of the gas using one of various calculation methods.