DASHBOARD FOR CORROSION PREDICTION ANALYTICS

In the oil and gas industry, pipelines have become crucial for enabling the transport of flammable and hazardous substances such as crude oil, natural gas, and refined petroleum products. Compared with trucks and trains, they hold fluids in greater volume, healthier way, and more environmental...

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Main Author: Uaciquete, Diva Flora
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.21838 /
Published: IRC 2020
Subjects:
Q Science (General)
Online Access: http://utpedia.utp.edu.my/21838/1/24074_Diva%20Flora%20Uaciquete.pdf
http://utpedia.utp.edu.my/21838/
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spelling utp-utpedia.218382021-09-24T09:55:25Z http://utpedia.utp.edu.my/21838/ DASHBOARD FOR CORROSION PREDICTION ANALYTICS Uaciquete, Diva Flora Q Science (General) In the oil and gas industry, pipelines have become crucial for enabling the transport of flammable and hazardous substances such as crude oil, natural gas, and refined petroleum products. Compared with trucks and trains, they hold fluids in greater volume, healthier way, and more environmentally friendly. However, as with any other equipment, to some extent pipelines can have different failures. Leakage in the pipelines can cause progressive accidents such as spillage of fluids, fire, and explosion. Exposure of such incidents results in casualties, even worse, deaths, damage to the environment and to properties, poor reputations, financial distress, and more negative impacts. Thus, risk-reducing initiatives that can avoid leakage of the pipelines are necessary because the interventions will ideally be able to control leakage root causes. Many accidents have proven that corrosion induces the leakage phenomena in the pipelines. Therefore, the commitment for safety measures to prevent leakage is crucial to carry out corrosion assessment. In order to promote decision-making in the prevention of pipeline leakage, this study analyzes the correlation of depth and length of corrosion within pipelines. The methodology used in the project includes the literature review and the study of neural long short term memory (LSTM) network model and how it behaves with sequential data. Thus, the results of this work can assist risk assessors in identifying the level of risk and prevent future leakage in the pipelines effectively. IRC 2020-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21838/1/24074_Diva%20Flora%20Uaciquete.pdf Uaciquete, Diva Flora (2020) DASHBOARD FOR CORROSION PREDICTION ANALYTICS. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic Q Science (General)
spellingShingle Q Science (General)
Uaciquete, Diva Flora
DASHBOARD FOR CORROSION PREDICTION ANALYTICS
description In the oil and gas industry, pipelines have become crucial for enabling the transport of flammable and hazardous substances such as crude oil, natural gas, and refined petroleum products. Compared with trucks and trains, they hold fluids in greater volume, healthier way, and more environmentally friendly. However, as with any other equipment, to some extent pipelines can have different failures. Leakage in the pipelines can cause progressive accidents such as spillage of fluids, fire, and explosion. Exposure of such incidents results in casualties, even worse, deaths, damage to the environment and to properties, poor reputations, financial distress, and more negative impacts. Thus, risk-reducing initiatives that can avoid leakage of the pipelines are necessary because the interventions will ideally be able to control leakage root causes. Many accidents have proven that corrosion induces the leakage phenomena in the pipelines. Therefore, the commitment for safety measures to prevent leakage is crucial to carry out corrosion assessment. In order to promote decision-making in the prevention of pipeline leakage, this study analyzes the correlation of depth and length of corrosion within pipelines. The methodology used in the project includes the literature review and the study of neural long short term memory (LSTM) network model and how it behaves with sequential data. Thus, the results of this work can assist risk assessors in identifying the level of risk and prevent future leakage in the pipelines effectively.
format Final Year Project
author Uaciquete, Diva Flora
author_sort Uaciquete, Diva Flora
title DASHBOARD FOR CORROSION PREDICTION ANALYTICS
title_short DASHBOARD FOR CORROSION PREDICTION ANALYTICS
title_full DASHBOARD FOR CORROSION PREDICTION ANALYTICS
title_fullStr DASHBOARD FOR CORROSION PREDICTION ANALYTICS
title_full_unstemmed DASHBOARD FOR CORROSION PREDICTION ANALYTICS
title_sort dashboard for corrosion prediction analytics
publisher IRC
publishDate 2020
url http://utpedia.utp.edu.my/21838/1/24074_Diva%20Flora%20Uaciquete.pdf
http://utpedia.utp.edu.my/21838/
_version_ 1741195793568628736
score 11.62408

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