Gas Network Leak Detection Using Relief Valve Training
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Solution Overview
Problem
Current methods are inadequate for detecting and quantifying leaks in complex gas networks between sources and consumers, as they are designed for long, straight pipelines and do not account for the complexities of pressurized or vacuum gas networks.
Innovation Solution
A method involving controllable relief valves and sensors that establish a mathematical model through a training phase to predict and quantify leaks by controlling relief valves in predetermined scenarios, allowing for leak detection and quantification in the entire gas network without requiring knowledge of its exact topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional leak detection methods are used, then they work for long straight pipelines, but they fail for complex gas networks with multiple compressors and consumers
Solution Approach 1:
The complex gas network is segmented into multiple zones or sections, each monitored by specific sensors. The network topology is divided into manageable segments that can be independently analyzed, allowing the system to handle complexity while maintaining detection accuracy in each segment.
Solution Approach 2:
A mathematical model acts as an intermediary between sensor measurements and leak detection. The model transforms raw sensor data into meaningful leak indicators, bridging the gap between complex network conditions and reliable leak identification.
2Measurement precision
If relief valves are controlled in predetermined scenarios, then leak detection accuracy improves, but system operational complexity increases
Solution Approach 1:
Relief valves are controlled in predetermined scenarios and sequences before actual leak detection begins. This preliminary control establishes baseline conditions and sensor relationships that simplify subsequent leak detection operations, reducing the complexity of real-time decision-making.
Solution Approach 2:
The system employs periodic control of relief valves in predetermined scenarios, cycling through different valve states to gather comprehensive sensor data. This periodic action builds a robust mathematical model without requiring continuous complex control, balancing precision with operational simplicity.
3Productivity
If the gas network operates continuously, then productivity is maintained, but leak detection requires system shutdown in traditional methods
Solution Approach 1:
The leak detection method enables continuous operation of the gas network by performing detection during normal operational phases. Sensors continuously monitor gas flow and pressure, and the mathematical model continuously evaluates leak conditions, eliminating the need for shutdowns while maintaining productivity.
Solution Approach 2:
The system adapts dynamically to changing operational conditions by adjusting the mathematical model parameters in real-time. This dynamic approach allows leak detection to function effectively whether the network is operating at high or low capacity, maintaining ease of operation across all productivity levels.
4Measurement precision
If historical data is required for model creation, then model accuracy improves, but detection time and data collection requirements increase
Solution Approach 1:
The system performs preliminary control actions with relief valves to generate training data quickly during a short initialization phase. This preliminary data collection establishes the mathematical model without requiring extensive historical data accumulation, reducing time loss while achieving sufficient model accuracy for operational use.
Data Source
AI summary
A method is provided for detecting and quantifying leaks 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 gas or vacuum; a plurality of sensors which determine one or a plurality of physical parameters of the gas in the gas network. The gas network has controllable or adjustable relief valves and the method involves a training phase and an operational phase.