UAS Emissions Estimation Using Meteorological Data Fusion
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Solution Overview
Problem
Existing methods are inefficient in detecting and quantifying methane emissions from spatially distributed natural gas infrastructure, leading to undue product loss, environmental impact, and safety hazards.
Innovation Solution
An unmanned aerial system (UAS) equipped with gas concentration sensors and weather stations flies downwind of potential emission sources, combining data to determine trace-gas emission rates using a raster grid flight path perpendicular to wind direction and altitude, integrating with meteorological data to identify and quantify gas releases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If traditional ground-based methods are used to detect methane emissions, then detection capability is limited to accessible locations, but coverage area is restricted and detection efficiency is low
Solution Approach 1:
The patent transitions from ground-based two-dimensional detection to three-dimensional aerial detection using UAVs. The UAV flies at various altitudes (e.g., 50-150 feet) to capture methane emissions from spatially distributed sources across wide areas, enabling volumetric surveying of emission plumes and significantly expanding coverage while maintaining high detection efficiency through automated flight paths and real-time sensing.
2Productivity
If manual detection methods are used, then operational complexity is low, but time consumption is high and productivity is reduced
Solution Approach 1:
The UAV system performs autonomous operations including self-navigation along pre-programmed flight paths, self-monitoring of sensor data, and automated emission detection. The system requires minimal human intervention during surveys, with the UAV independently collecting and transmitting data, thereby increasing detection speed while the complexity is managed through automated control algorithms and pre-configured mission parameters.
3Measurement precision
If comprehensive meteorological data integration is implemented, then emission rate accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system integrates real-time meteorological data (wind speed, direction, temperature, humidity) with methane concentration measurements from the UAV sensors. This feedback loop allows the system to continuously adjust emission rate calculations based on atmospheric conditions, improving accuracy by accounting for plume dispersion and transport. The complexity is managed through automated data fusion algorithms that process multiple parameters simultaneously to derive accurate emission rates.
Data Source
AI summary
Systems, devices, and methods including a processor having addressable memory, the processor configured to: receive a trace-gas data packet, where the trace-gas data packet comprises a trace-gas concentration data from a trace-gas sensor and a location data for the trace-gas sensor from a location sensor, where the location data for the trace-gas sensor comprises a trajectory of the trace-gas sensor in space; receive at least one Meteorological data packet from one or more weather stations, where each weather station is distal from the trace-gas sensor, where each weather station generates a corresponding Meteorological data packet, where each Meteorological data packet comprises weather data; combine the trace-gas data packet with a selected spatial and temporal Meteorological data packet; and determine a trace-gas emission rate of a trace-gas source based on the combined trace-gas data packet and the selected Meteorological data packet.