封面图片

Forecasting Corrosion Rate on Pipeline using Artificial Neural Network

Large amounts of oil and gas are transported every day and distributed throughout the world through pipelines. They are considered the safest method of transporting oil and gas because they rarely fail. Nevertheless, pipelines could lead to deterioration and degradation. Therefore, it is crucial to...

全面介绍

Saved in:
书目详细资料
主要作者: Zolkifli, Nur Anissa
格式: Final Year Project
语言:English
出版: 2022
主题:
T Technology (General)
在线阅读:http://utpedia.utp.edu.my/id/eprint/24539/1/Forecasting%20Corrosion%20Rate%20on%20Pipeline%20using%20Artificial%20Neural%20Network.pdf
http://utpedia.utp.edu.my/id/eprint/24539/
标签: 添加标签
没有标签, 成为第一个标记此记录!
id oai:utpedia.utp.edu.my:24539
record_format eprints
spelling oai:utpedia.utp.edu.my:245392023-05-18T08:22:13Z http://utpedia.utp.edu.my/id/eprint/24539/ Forecasting Corrosion Rate on Pipeline using Artificial Neural Network Zolkifli, Nur Anissa T Technology (General) Large amounts of oil and gas are transported every day and distributed throughout the world through pipelines. They are considered the safest method of transporting oil and gas because they rarely fail. Nevertheless, pipelines could lead to deterioration and degradation. Therefore, it is crucial to frequently monitor pipelines in order to improve performance and decrease their failure rates down to a safe level. There are various models have been created to forecast pipeline conditions. Therefore, this paper describes the development of models that forecast the corrosion rate on pipeline using artificial neural network (ANN) for oil and gas refinery. 2022-09 Final Year Project NonPeerReviewed text en http://utpedia.utp.edu.my/id/eprint/24539/1/Forecasting%20Corrosion%20Rate%20on%20Pipeline%20using%20Artificial%20Neural%20Network.pdf Zolkifli, Nur Anissa (2022) Forecasting Corrosion Rate on Pipeline using Artificial Neural Network. [Final Year Project] (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Zolkifli, Nur Anissa
Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
description Large amounts of oil and gas are transported every day and distributed throughout the world through pipelines. They are considered the safest method of transporting oil and gas because they rarely fail. Nevertheless, pipelines could lead to deterioration and degradation. Therefore, it is crucial to frequently monitor pipelines in order to improve performance and decrease their failure rates down to a safe level. There are various models have been created to forecast pipeline conditions. Therefore, this paper describes the development of models that forecast the corrosion rate on pipeline using artificial neural network (ANN) for oil and gas refinery.
format Final Year Project
author Zolkifli, Nur Anissa
author_facet Zolkifli, Nur Anissa
author_sort Zolkifli, Nur Anissa
title Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
title_short Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
title_full Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
title_fullStr Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
title_full_unstemmed Forecasting Corrosion Rate on Pipeline using Artificial Neural Network
title_sort forecasting corrosion rate on pipeline using artificial neural network
publishDate 2022
url http://utpedia.utp.edu.my/id/eprint/24539/1/Forecasting%20Corrosion%20Rate%20on%20Pipeline%20using%20Artificial%20Neural%20Network.pdf
http://utpedia.utp.edu.my/id/eprint/24539/
_version_ 1768010138501775360
score 13.251813

相似书籍