AI-Controlled Gas Safety Valve Isolation for Threatened Sub-Networks
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Solution Overview
Problem
Manual shutdown of safety valves in hydrocarbon gas distribution networks is time-consuming and imprecise, posing risks due to environmental changes and tampering, which can lead to catastrophic events like gas line explosions.
Innovation Solution
A cloud-based server with AI tools automatically identifies impacted sub-networks and determines the necessary safety valve closures, using data from gas safety devices, including sensors and communication modules, to implement instantaneous and precise partial network shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual shutdown of safety valves is performed, then operators can respond to threatening conditions, but the response is time-consuming and imprecise
Solution Approach 1:
The patent replaces the manual mechanical operation of safety valves with an automated electronic control system. Sensors detect threatening conditions and transmit data to a server that automatically determines which safety valves to close, eliminating the need for manual scanning and decision-making. This substitution of mechanical manual operations with automated electronic systems directly reduces response time while improving shutdown precision through accurate sensor data and algorithmic decision-making.
Solution Approach 2:
The gas distribution network system performs self-diagnosis and self-protection by automatically detecting threatening conditions through sensors and autonomously shutting down affected sub-networks without human intervention. The system uses AI algorithms to analyze sensor data, identify impacted areas, and execute valve closures independently, enabling the network to protect itself from hazards such as floods, hurricanes, and pressure anomalies.
2Productivity
If manual scanning of large areas is performed, then operators can identify localized incidents, but the process is inefficient and time-consuming
Solution Approach 1:
The patent divides the gas distribution network into multiple sub-networks, each monitored by sensors and controllable through independently operable safety valves. When a threatening condition is detected in one segment, the system automatically identifies and isolates only the affected sub-network rather than requiring manual scanning of the entire network. This segmentation enables localized response, dramatically improving productivity and reducing the time to identify and respond to incidents.
Solution Approach 2:
The patent introduces sensors and an AI server as intermediaries between the physical gas network and the control system. Sensors continuously monitor parameters such as pressure, temperature, and environmental conditions, serving as intermediaries that detect threatening conditions and transmit data to the server. The server then processes this information and determines the appropriate valve closures, eliminating the need for manual network scanning and significantly improving incident response efficiency.
3Reliability
If safety valves are manually operated, then operators can control gas flow, but the system is vulnerable to tampering and environmental changes
Solution Approach 1:
The patent replaces manual mechanical operation of safety valves with an automated electronic control system that is not susceptible to human tampering. The system uses sensors to detect threatening conditions and an AI server to automatically control valve operations, eliminating the vulnerability to manual interference. This substitution with automated electronic controls significantly improves system reliability by removing human error and intentional tampering as failure modes.
Solution Approach 2:
The patent implements a feedback control system where sensors continuously monitor gas pressure, temperature, and environmental conditions, and this data is fed back to the AI server. The server processes the feedback information and automatically adjusts safety valve positions to maintain safe operating conditions. This closed-loop feedback mechanism improves reliability by continuously responding to changing conditions and automatically correcting potential safety issues before they become critical.
Data Source
AI summary
A system includes a gas distribution network having sub-networks including a first sub-network, each sub-network including a pipe section having a gas safety device coupled to a gas safety valve. The gas safety device includes a pressure sensor, a shut off controller configured to shut off the gas safety valve, a computing device, and a communications module. A server is communicably coupled to the communications modules of the gas safety devices for that implements artificial intelligence (AI) which based on a current threat condition to the gas distribution network determines affected sub-networks. The server is for sending of a valve closing instruction signal to the gas safety device in the first sub-network during the current threat condition when the current threat condition is determined to affect the first sub-network. Responsive to the valve closing instruction signal, the gas safety device shuts off the gas safety valve in the first sub-network.