Gas Flare Discrimination Using Image-Based Leak Identification
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Solution Overview
Problem
Existing gas analysis systems mistakenly identify gas flares as gas leaks, leading to inefficient asset operations and resource waste due to unnecessary cessation and remediation efforts.
Innovation Solution
A gas flare discrimination system comprising a gas detection sensor and a controller component that processes image data through acquisition, feature extraction, and discrimination models to accurately differentiate between gas leaks and flares, initiating appropriate responses based on identification flags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gas detection systems use simple detection methods, then the system complexity is low, but the measurement precision is insufficient leading to false identification of gas flares as leaks
Solution Approach 1:
The detection system is segmented into multiple specialized components: hyperspectral imaging subsystem for spectral data collection, machine learning processing subsystem for pattern recognition, and control subsystem for coordinate mapping. This segmentation allows each component to specialize in specific tasks, improving overall identification accuracy while distributing system complexity across modular units.
Solution Approach 2:
A coordinate mapping mechanism serves as an intermediary between the imaging system and the detection algorithms. The controller component maps image coordinates to real-world coordinates, enabling precise location identification of gas flares. This intermediary layer bridges the gap between raw spectral data and actionable detection results, improving measurement precision without requiring direct complex interactions between all system components.
2Reliability
If gas detection systems respond to all detected gas events, then the reliability of safety response is high, but the loss of time and resources occurs due to unnecessary cessation for false leak identifications
Solution Approach 1:
The system incorporates feedback mechanisms where the controller component receives identification results from the machine learning model and uses coordinate mapping to verify the location and context of detected events. This feedback loop allows the system to distinguish between actual gas leaks and controlled flares by analyzing spectral signatures and spatial information, ensuring reliable safety responses only when necessary and preventing unnecessary operational downtime.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products for gas flare discrimination are provided. An example gas flare discrimination system may comprise at least one gas detection sensor and at least one controller component. In some embodiments, the controller component is configured to obtain image data of a target area. In some embodiments, the controller component is configured to generate, by applying the image data to an acquisition model, gas channel data. In some embodiments, the controller component is configured to generate, by applying the gas channel data to a feature extraction model, gas feature data. In some embodiments, the controller component is configured to generate, by applying the gas feature data to a gas flare discrimination model, a gas flare identification flag. In some embodiments, the controller component is configured to initiate performance of one or more gas flare response actions based at least in part on the gas flare identification flag.