Multigas Sensor Mesh Network for Fugitive Emission Detection
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Solution Overview
Problem
Current gas detection systems in the oil and gas industry are limited by their inability to efficiently detect multiple flammable gases simultaneously, requiring multiple sensors and increasing costs and complexity, while also lacking in accuracy and ease of deployment for real-time monitoring and risk assessment.
Innovation Solution
A MEMS-based multigas sensor system with a cloud-connected wireless network, utilizing a local mesh network and Industrial Internet of Things (IIoT) platform for real-time data analysis and autonomous decision-making, capable of detecting methane, ethane, propane, butane, and acetone, with sensors deployed across various assets for immediate risk assessment and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate gas sensors are used to detect different flammable gases, then detection coverage is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple gas sensing elements into a single integrated sensor unit that can detect multiple flammable gases simultaneously. This merging approach maintains comprehensive detection coverage while reducing the number of separate sensor units needed, thereby simplifying the overall system architecture and reducing deployment complexity.
Solution Approach 2:
The sensor system is designed with multi-functional capability to detect various flammable gases (methane, ethane, propane, butane, acetone) using a unified sensing platform. This universal detection approach allows a single sensor type to perform multiple detection functions, eliminating the need for different specialized sensors for each gas type.
2Measurement precision
If more sensors are deployed for comprehensive monitoring, then detection accuracy is improved, but cost and system complexity increase
Solution Approach 1:
The sensor nodes are equipped with autonomous capabilities including self-diagnosis, self-calibration, and automatic data validation. Each node independently manages its own operation and communicates findings to the cloud platform, reducing the need for external monitoring and manual intervention while maintaining high detection accuracy through consistent, reliable measurements.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is validated against historical patterns and environmental conditions. The cloud platform analyzes incoming data in real-time, comparing it against known gas signatures and environmental baselines to filter false positives and confirm genuine detections, thereby maintaining high accuracy without requiring excessive sensor redundancy.
3Ease of operation
If wireless mesh network is used for sensor connectivity, then ease of deployment is improved, but reliability of data transmission may worsen in harsh conditions
Solution Approach 1:
The mesh network dynamically adapts its topology based on environmental conditions and node status. When transmission paths are compromised by harsh conditions, the network automatically reroutes data through alternative nodes, maintaining reliable communication without requiring manual reconfiguration. This dynamic adaptability ensures continuous data flow even when individual transmission paths fail.
Solution Approach 2:
The system incorporates redundant communication paths and buffer storage capabilities before deployment challenges arise. Each sensor node maintains local data buffering and can operate autonomously if communication is temporarily disrupted. The mesh architecture pre-establishes multiple potential transmission routes, cushioning against the impact of harsh environmental conditions on data transmission reliability.
4Measurement precision
If real-time data analysis is performed in the cloud, then risk assessment accuracy is improved, but data transmission requirements and system complexity increase
Solution Approach 1:
The system segments data processing functions between edge devices and cloud infrastructure. Sensor nodes perform initial data validation and preprocessing locally, filtering out obvious noise and formatting data before transmission. The cloud platform then performs sophisticated risk assessment algorithms on the pre-processed data, dividing the computational workload to maintain high accuracy while reducing transmission bandwidth requirements and simplifying the overall architecture.
Data Source
AI summary
A fugitive gas detection system is provided. The system includes a cloud service, a plurality of reach-based components, a plurality of wireless gas sensors. The reach-based components comprise backhauls and gateways. The wireless gas sensors are acted as nodes to acquire sensor data in a local mesh network and the nodes are connected to the cloud service through the reach-based components, one node can transmit the sensor data to other sensor nodes of the local mesh network. The system measures flammable gas levels with speed, economy and accuracy.