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Title: Anomaly detection in gas pipelines using flow and pressure
Authors: Kumar, Abhishek
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: Gas supply is a process of supreme importance in Singapore because it is also used as a raw material for power generation. Majority of gas pipes in Singapore are underground and is made of iron. Gas Pipelines suffer from worsening due to aging and nosedive to satisfy the stated carrying capacities [1]. It also results in problem for customers, and reasons power disruptions with loss in industrial productions and occasionally even detonation hazards. Thus, it is imperious to reduce its total cost to make it inexpensive. Malfunction in gas pipelines system are related with various causes. (e.g., leaks, Water Ingression, & worsening of pipes). Approaches for the recognition of pipelines fault were solely established for three circumstances: leaks of gas, obstruction due to water ingress and corroding in the pipes because of aging. Therefore, Multi- Sensor Real Time Monitoring of gas pipeline for leakage, ingression, and disturbance recognition are part of new pipeline projects. Leakage recognition using distributed fiber-optic sensors can be a inclusive solution for unremitting, in-line, real-time monitoring of various pipelines. The scope of this project is to explore the glitches in gas pipeline, foremost signature parameters like the flow rate, change in pressure, etc. by obtaining data using flow and pressure transmitters at the specified locations. Furthermore, it is needed to explore multi-sensor based discovery of anomaly, leak, and linked problems like water ingress and gas leakage. This will help the manoeuvre team in anticipating failure in anomaly recognition. Real-time experimental data was collected from the live test bed at SP Power grid to simulate the results of the acquired data for limiting and tracking the leakage in gas pipelines. Also, comparison experimental data of gas pipelines above the ground with that of test data of the underground pipelines was analysed on pipelines DI 150 and DI 100.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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