Gas Network Leak And Obstruction Detection Without Topology Mapping
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Solution Overview
Problem
Existing methods are inadequate for detecting and quantifying leaks and obstructions in complex gas networks under pressure or vacuum, as they are designed for long, straight pipelines and do not account for the complexities of real-world gas networks.
Innovation Solution
A method that utilizes a network of sensors to detect physical parameters of gas at different locations, combined with controllable relief and throttle valves, to create a mathematical model that can identify and quantify leaks and obstructions by simulating scenarios and comparing sensor readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing leak detection methods are used in complex gas networks, then detection capability in simple pipelines is maintained, but detection accuracy and reliability deteriorate due to network complexity
Solution Approach 1:
The complex gas network is segmented into multiple analysis zones or sections, each monitored independently. The system divides the network topology into manageable segments, allowing leak detection algorithms to process each segment separately while maintaining overall network coverage. This segmentation reduces the computational complexity and improves detection reliability by focusing analysis on specific network portions.
Solution Approach 2:
A central control unit or software platform acts as an intermediary between the distributed sensors/valves and the leak detection algorithm. This intermediary processes raw sensor data, coordinates valve operations, and synthesizes information from multiple sources to generate leak detection decisions, thereby managing the complexity of coordinating multiple network components.
2Measurement precision
If traditional leak detection methods are applied, then detection in long straight pipelines is effective, but detection capability deteriorates in complex network topologies
Solution Approach 1:
The system dynamically adapts its detection strategy based on the specific network topology being monitored. The control unit adjusts measurement intervals, valve operation sequences, and analysis parameters according to the complex network configuration, allowing the same system to maintain high detection precision across diverse topology types rather than relying on fixed detection methods.
Solution Approach 2:
The system changes operational parameters such as pressure differential thresholds, flow rate monitoring levels, and valve positioning sequences to match the specific characteristics of the gas network topology. By adjusting these parameters based on network complexity and configuration, the system maintains measurement precision across different network types.
3Reliability
If multiple sensors and valves are deployed in complex networks, then detection coverage is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The system performs self-diagnosis and automatic calibration by having the control unit automatically coordinate sensor readings and valve operations without requiring manual intervention. The system self-adjusts based on baseline measurements taken during normal operation, reducing the operational burden on users while maintaining comprehensive detection coverage across the complex network.
Solution Approach 2:
The control unit continuously receives feedback from sensors and automatically adjusts valve positions and measurement parameters to optimize detection performance. This closed-loop feedback mechanism simplifies operation by allowing the system to self-regulate based on real-time network conditions, eliminating the need for complex manual control procedures.
4Productivity
If mathematical modeling is used to detect leaks and obstructions simultaneously, then detection capability is improved, but computational complexity increases
Solution Approach 1:
The system applies mathematical modeling selectively to specific network segments or only when anomalies are detected, rather than continuously processing the entire network. This partial application of complex computational models maintains high detection efficiency for critical issues while reducing overall computational burden during normal operation.
Solution Approach 2:
The system replaces complex physical measurement devices with mathematical models that compute leak and obstruction detection based on sensor data. By using computational algorithms to substitute for additional physical sensors and actuators, the system achieves simultaneous detection capability while managing computational complexity through software-based solutions.
Data Source
AI summary
A method is provided for the simultaneous detection, localization, and quantification of leaks and obstructions in a gas network under pressure or vacuum. The gas network includes: one or more sources of compressed gas or vacuum; one or more consumers or consumer areas of compressed gas or vacuum applications; pipelines or a network of pipelines to transport the compressed gas or vacuum from the sources to the consumers, consumer areas or applications; a plurality of sensors providing one or more physical parameters of the gas at different times and locations within the gas network. The gas network is further provided with controllable or adjustable relief valves, controllable or adjustable throttle valves and possibly one or a plurality of sensors capable of monitoring the status or state of the relief valves and/or throttle valves.