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Uaciquete, Diva Flora (2020) DASHBOARD FOR CORROSION PREDICTION ANALYTICS. IRC, Universiti Teknologi PETRONAS. (Submitted)

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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.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 24 Sep 2021 09:55
Last Modified: 24 Sep 2021 09:55
URI: http://utpedia.utp.edu.my/id/eprint/21838

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