Gas Network Leak Detection Using Sensor Residual Analysis
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Solution Overview
Problem
Existing methods are inadequate for detecting and quantifying leaks in complex gas networks under pressure or vacuum, as they are designed for long, straight pipelines and do not account for the complexities of compressor plants and consumer networks.
Innovation Solution
A method involving a network of sensors that measure physical parameters of gas at different times and locations, coupled with additional sensors indicating the state of sources and consumers, uses a physical model or mathematical relationship to predict leaks by comparing sensor readings and generating alarms or quantifying leakage rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing leak detection methods are used, then leaks in simple pipelines can be detected, but leaks in complex gas networks with compressor plants cannot be detected
Solution Approach 1:
The complex gas network is divided into multiple segments or zones, each monitored by dedicated sensors and analyzed using localized physical models. This segmentation allows the system to handle complexity by breaking down the overall network into manageable sections while maintaining comprehensive leak detection coverage across the entire system.
Solution Approach 2:
A computer system acts as an intermediary that receives data from multiple sensors, applies physical models and mathematical relationships, and generates leak detection results. This intermediary processing layer enables the system to handle complex network configurations by mediating between raw sensor data and leak detection decisions.
2Productivity
If physical models are trained during operational phase, then continuous monitoring is maintained, but model accuracy may drift over time
Solution Approach 1:
The system performs periodic retraining of physical models during operational phases, interrupting continuous monitoring temporarily to update model parameters. This periodic retraining maintains measurement precision by refreshing the models with current system conditions while minimizing disruption to overall monitoring operations.
Solution Approach 2:
The system uses feedback from sensor measurements to continuously refine and update physical models. By comparing predicted values with actual sensor readings, the model parameters are adjusted to maintain accuracy over time, ensuring that the models remain valid as system conditions change during operational phases.
Data Source
AI summary
A method is provided for detecting and quantifying leaks in a gas network under pressure or vacuum. The gas network may have a sensor(s) capable of recording the status of a source(s), consumers, consumer areas or applications. The method includes: a start-up phase; a training or estimation phase; and an operational phase. The operational phase includes: reading out the first group of sensors; calculating or determining the value of a second group of sensors from the readings from the first group of sensors; comparing the calculated or determined values of the second group of sensors with the read values of the second group of sensors and determining the difference between them; determining, on the basis of a residual value analysis, whether there is a leak in the gas network; generating an alarm and/or generating a leakage rate and/or generating the corresponding leakage cost if a leak is detected.