Gas Network Obstruction Detection via Sensor Model Re-Estimation
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Solution Overview
Problem
Existing methods for monitoring gas networks under pressure are inadequate for detecting and quantifying obstructions in complex pipeline networks, as they are designed for long, straight pipelines and do not account for obstructions within the network itself.
Innovation Solution
A method involving sensors to determine physical parameters at various locations, using estimation algorithms to establish and re-establish a physical or mathematical model of the gas network, allowing for the detection and quantification of obstructions by comparing baseline and operational phase measurements, and generating alarms for obstruction detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing monitoring methods for gas networks are used, then monitoring of long straight pipelines is achieved, but detection of obstructions in complex pipeline networks is not possible
Solution Approach 1:
The gas network is divided into multiple segments or zones, each monitored by specific sensors. This segmentation allows the system to handle complex topologies by breaking them down into manageable sections, enabling obstruction detection in each segment while adapting to the overall network structure.
Solution Approach 2:
A computer system acts as an intermediary that receives data from multiple sensors, processes the information using algorithms, and generates obstruction alerts. This intermediary component enables the system to adapt to complex networks by centralizing the analysis of sensor data from various locations and configurations.
2Measurement precision
If sensors are installed at multiple locations to detect obstructions, then obstruction detection capability is improved, but system complexity increases
Solution Approach 1:
The sensors installed in the network serve multiple functions: monitoring pressure, detecting obstructions, and providing data for the physical model. This multi-functionality reduces the need for separate specialized sensors, thereby improving detection precision without proportionally increasing system complexity.
Solution Approach 2:
The system continuously monitors sensor data and provides feedback to the computer system, which updates the physical model and generates alerts when obstructions are detected. This feedback mechanism enables precise obstruction detection through continuous monitoring while managing complexity through automated processing and decision-making algorithms.
3Measurement precision
If a physical model is continuously updated to detect obstructions, then detection accuracy is improved, but computational requirements and time increase
Solution Approach 1:
The physical model is updated at regular intervals or when significant changes are detected, rather than continuously. This periodic updating maintains sufficient detection accuracy while reducing computational burden and time loss compared to continuous model re-estimation.
Solution Approach 2:
The system performs partial model updates by focusing computational resources on critical parameters or segments where obstructions are most likely to occur, rather than completely re-estimating the entire physical model. This approach maintains detection accuracy for critical areas while reducing overall computational requirements and time consumption.
Data Source
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AI summary
Method for detecting obstructions (12) in a gas network (1) comprising : - sources (6) of; - consumers (7); - sensors (9a, 9b); characterized in that the method comprises the following phases: - a baseline or zero phase (15) determining an initial physical model between measurements from a first set and a second set of sensors (9a, 9b); - an operational phase (16) in which the physical model between the measurements of the first set and the second set of sensors (9a, 9b) is re-established at regular time intervals by means of estimation algorithms to predict gas network obstructions (1); wherein the operational phase (16) comprises the following steps: re-estimating the physical model on the basis of the read measurements from the sensors (9a, 9b,9c); determining or calculating of the existence of an obstruction (12) in the system,based on the difference between the parameters of the physical model as determined during the baseline or zero phase (15) and the operational phase (16),