eprintid: 16949
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/69/49
datestamp: 2023-12-19 03:23:26
lastmod: 2023-12-19 03:23:26
status_changed: 2023-12-19 03:07:10
type: article
metadata_visibility: show
creators_name: Mujtaba, S.M.
creators_name: Lemma, T.A.
creators_name: Vandrangi, S.K.
title: Gas pipeline safety management system based on neural network
ispublished: pub
keywords: Compressibility of gases; Gases; Leak detection; Natural gas; Natural gas pipelines; Network layers; Safety engineering, Case-studies; Detection and diagnostics; Gas leaks; Gases mixture; Leaks detections; Natural gas flow; Neural networks classifiers; Neural-networks; Pipeline safety; Safety management systems, Flow of gases
note: cited By 4
abstract: The risk of leakage poses a grave threat to natural gas pipeline safety. The high compressibility of gases combined with unsteady boundary conditions makes detecting leaks in pipelines a challenging endeavor. To date, in the literature, only a limited number of studies have focused on leak detection and diagnostics in gas mixture pipelines. The present study provides a system for detecting, locating, and estimating the size of small gas leaks from a compressible and dynamic natural gas flow in pipelines with improved accuracy. As a case study, a long natural gas pipeline of 80 km is simulated with leak sizes of 0, 2, and 5. The safety system is developed using mass flow rate, temperature, and pressure measurements. Six classes for faulty cases and one class for no fault case were considered for the study. A shallow neural network classifier (SNNC) is trained to identify a specific fault class. The SNNC is based on a two-layered network with 20 and 7 neurons. An input vector of 15 variables is provided to the system, and the output is one of the seven possible classes. Leakage as low as 2 at various locations are correctly diagnosed with more than 99 correct classification rate. © 2022 American Institute of Chemical Engineers.
date: 2022
publisher: John Wiley and Sons Inc
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122650664&doi=10.1002%2fprs.12334&partnerID=40&md5=f3ccc0bf7d7abfcb2854c621385d73ad
id_number: 10.1002/prs.12334
full_text_status: none
publication: Process Safety Progress
volume: 41
number: S1
pagerange: S59-S67
refereed: TRUE
issn: 10668527
citation:   Mujtaba, S.M. and Lemma, T.A. and Vandrangi, S.K.  (2022) Gas pipeline safety management system based on neural network.  Process Safety Progress, 41 (S1).  S59-S67.  ISSN 10668527