Gas Emission Detection Using Sensor Grid and Plume Model
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Solution Overview
Problem
Current methods for detecting gas leaks in industrial gas production and distribution environments are inefficient, as they struggle to accurately determine emission rates and source locations due to prevailing weather conditions and the lack of commercially available systems that can pinpoint leaks from specific equipment under varying wind conditions.
Innovation Solution
A system utilizing Near-Field and Far-Field sensors deployed in a grid-like arrangement to collect and communicate data, including wind and gas concentration data, which uses a plume advection-diffusion model to determine emission rates and generate emission data, thereby automating the detection of gas leaks and improving maintenance operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gas leak detection methods are used, then operational safety can be maintained to some extent, but measurement precision of emission rates and source locations deteriorates under varying weather conditions
Solution Approach 1:
The system segments the detection area into multiple zones with strategically positioned sensors (upstream, downstream, crosswind locations) to capture gas plume characteristics from different angles. This segmentation enables accurate source location determination and emission rate calculation even when wind conditions change, as multiple sensor readings provide redundant measurement paths.
Solution Approach 2:
The system continuously monitors and adapts to changing weather parameters (wind speed, wind direction, temperature) by adjusting detection algorithms and sensor activation patterns. This parameter adaptation allows the system to maintain measurement precision across varying environmental conditions by compensating for plume dispersion changes.
2Productivity
If manual inspection methods are used, then equipment complexity is reduced, but productivity of gas leak detection and response time deteriorates
Solution Approach 1:
The system performs self-monitoring and automated detection of gas leaks without requiring continuous manual inspection. Sensors automatically detect gas concentrations, the system autonomously processes data to determine source locations and emission rates, and generates alerts without human intervention, thereby significantly improving detection productivity and response time.
Solution Approach 2:
The system replaces manual mechanical inspection methods with automated electronic sensor networks and computational analysis. This substitution eliminates the need for personnel to physically inspect equipment, enabling continuous monitoring and rapid detection of gas leaks, thus improving productivity while the automated system handles the complexity.
3Measurement precision
If comprehensive sensor deployment is implemented, then measurement precision of gas emissions is improved, but loss of time for data processing and analysis increases
Solution Approach 1:
The system pre-calculates and stores dispersion model parameters and processing algorithms before actual gas leak detection occurs. When gas emissions are detected, the pre-prepared computational frameworks enable rapid processing of sensor data to determine source locations and emission rates, minimizing data processing time while maintaining high measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate emission rate and source location data, enhancing operational safety and production efficiency by automating the detection of gas leaks and improving maintenance planning in gas production and distribution environments.
Implementation Method 1
plume advection-diffusion model to determine emission rates
Implementation Method 2
plume advection-diffusion model to determine emission rates
Data Source
AI summary
Systems, methods, and a computer readable medium are provided for monitoring and detecting a gas emission. Sensor data including gas concentration and wind data associated with a gas emission from an emission source is received from Near-Field and Far-Field sensors configured within a gas production and distribution environment. The sensor data can be provided as inputs to a Near-Field dispersion model to determine an emission rate associated with the gas emission and one or more source locations associated with the gas emission. The emission rate can be included in emission data and provided for output. Related apparatus, systems, techniques, and articles are also described.