Pipeline Network Monitoring With Virtual Sensors and RL Control
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Solution Overview
Problem
Existing systems for monitoring and controlling dynamic networks, such as pipelines, rely heavily on deterministic models and human intervention, which are inadequate for capturing the dynamic characteristics and behavior of these systems, especially in real-time scenarios.
Innovation Solution
A system utilizing reinforcement learning artificial neural networks (ANNs) with virtual sensors and a network topology processor to model and estimate the state of the network, allowing for autonomous monitoring and control by learning from sensor measurements and adapting to changes in the network topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deterministic models and equations are used to monitor the network state, then manufacturing precision is improved, but adaptability deteriorates because the models cannot capture dynamic characteristics and changing boundary conditions
Solution Approach 1:
The patent applies dynamics by transitioning from static deterministic models to dynamic neural network models that can adapt to changing network conditions. The neural networks are trained on historical data and continuously updated to reflect the dynamic behavior of the pipeline network, including changing boundary conditions and operational states.
Solution Approach 2:
The patent changes the fundamental parameters of the monitoring system by replacing traditional deterministic equations with machine learning models. This involves changing from fixed mathematical relationships to adaptive models that learn patterns from data, enabling the system to capture complex dynamic characteristics that cannot be represented by simple equations.
2Measurement precision
If human staff monitor and interpret SCADA system feedback, then measurement precision is improved through expert judgment, but productivity deteriorates due to reliance on human availability and response time
Solution Approach 1:
The patent implements self-service by enabling the monitoring system to automatically detect, interpret, and respond to network anomalies without human intervention. The neural networks autonomously analyze sensor data, identify faults, and generate alerts, replacing the need for human staff to continuously monitor SCADA systems while maintaining or improving detection accuracy.
Solution Approach 2:
The patent enhances feedback mechanisms by implementing continuous automated monitoring where the neural networks constantly analyze network state data and provide real-time feedback on system health. This automated feedback loop eliminates delays associated with human response and enables immediate detection and notification of faults.
3Device complexity
If traditional SCADA systems are used with human intervention, then device complexity is reduced, but extent of automation deteriorates as the system cannot operate autonomously
Solution Approach 1:
The patent applies mechanics substitution by replacing the mechanical human-in-the-loop control system with an automated neural network-based system. The neural networks perform the cognitive functions previously requiring human intelligence, such as pattern recognition, decision-making, and fault diagnosis, thereby enabling autonomous operation while managing complexity through software-based intelligence.
4Manufacturing precision
If deterministic equations are solved to calculate energy and flow rates, then manufacturing precision is improved, but loss of time increases due to computational requirements in real-time scenarios
Solution Approach 1:
The patent applies preliminary action by pre-training the neural networks on extensive historical data before deployment. This preliminary training phase allows the models to learn complex relationships offline, so that during real-time operation, they can rapidly infer network state without requiring intensive computational solving of equations, thus reducing real-time processing time while maintaining accuracy.
Data Source
AI summary
The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.