Fugitive Gas Leak Localization Using Wind-Binned Sensor Grids
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Solution Overview
Problem
Current manual methods for monitoring fugitive gas emissions in industrial facilities are time-consuming, expensive, and inaccurate, failing to provide precise quantification of emissions due to their error-prone nature.
Innovation Solution
A method and system utilizing geospatially distributed gas sensors and weather stations to collect and process data, employing wind direction binning and grid cell analysis to identify and quantify fugitive gas leaks, incorporating data validation and triangulation techniques to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring methods are used, then equipment simplicity is maintained, but measurement precision and productivity deteriorate
Solution Approach 1:
The monitoring area is divided into multiple grid cells, and gas sensor data is segmented by wind direction bins. This segmentation allows complex spatial and temporal patterns to be analyzed systematically, improving leak localization accuracy while managing computational complexity through structured data organization.
Solution Approach 2:
A data processing system acts as an intermediary between gas sensors/weather stations and the final leak analysis. This intermediary automatically validates data, merges measurements, performs wind direction binning, and conducts grid cell analysis, thereby improving measurement precision without requiring complex manual intervention.
2Productivity
If manual monitoring is used, then system cost is reduced, but productivity and time efficiency deteriorate
Solution Approach 1:
The automated system continuously collects, validates, and processes gas sensor and weather station data without interruption. This continuous operation enables real-time leak detection and quantification, dramatically improving productivity compared to periodic manual inspections while eliminating time losses associated with manual data collection and analysis.
Solution Approach 2:
The system performs self-validation of data, automatically merging sensor readings with weather data and identifying leaks through algorithmic analysis. This self-service capability eliminates the need for manual intervention in data processing, significantly reducing the time required for emission inventories while maintaining high monitoring productivity.
3Reliability
If manual monitoring methods are used, then operational simplicity is maintained, but reliability and measurement precision deteriorate
Solution Approach 1:
The system incorporates data validation that provides feedback on data quality, automatically identifying and removing erroneous values. This feedback mechanism ensures reliable measurements by continuously monitoring data integrity, while the automation handles the operational complexity so that users simply initiate monitoring without managing intricate manual procedures.
Solution Approach 2:
Manual operational procedures are replaced with automated computational processes. The system automatically validates data, merges measurements from multiple sensors, performs wind direction binning, and identifies leaks through algorithmic analysis. This substitution maintains high reliability through consistent automated execution while preserving ease of operation through user-friendly interfaces that require minimal manual intervention.
Data Source
AI summary
A method and system for locating and quantifying fugitive gas emission leaks includes obtaining gas sensor data and wind direction data from a plurality of sensors and weather stations located proximate a given area of interest. The gas sensor data and the wind direction data is validated to remove erroneous values and to merge the gas sensor data with the wind direction data to provide time synchronized gas sensor data and wind direction data over a given time interval. The time synchronized gas sensor data and wind direction data is segmented for each gas sensor location into wind direction bins containing a concentration of the gas levels in each bin. The area of interest is divided into a grid of cells and the bins projected on the grid cells for each gas sensor location along with the level of gas contained in the bins. The grid cells are then grouped into one or more contiguous grid cells having gas levels above a predefined level and a boundary area is calculated containing the grid cells with a gas level above a threshold to identify a potential leak area. The potential leak area is matched with a prior calculated leak area to identify the source location of the emission leak.