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A corrosion prediction model for oil and gas pipeline using CMARPGA

Pipelines are used as a medium to transport the oil, however, low maintenance causing not only the loss of the material itself but as well to the surrounding people and environment. In order to tackle the incidents, experts are assigned and experiments are conducted to analyze the source of the leak...

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Main Authors: Chern-Tong, H., Aziz, I.B.A.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010378854&doi=10.1109%2fICCOINS.2016.7783249&partnerID=40&md5=f17c3cdef3ea9a8150abfe49150d12ad
http://eprints.utp.edu.my/30507/
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spelling my.utp.eprints.305072022-03-25T07:09:30Z A corrosion prediction model for oil and gas pipeline using CMARPGA Chern-Tong, H. Aziz, I.B.A. Pipelines are used as a medium to transport the oil, however, low maintenance causing not only the loss of the material itself but as well to the surrounding people and environment. In order to tackle the incidents, experts are assigned and experiments are conducted to analyze the source of the leakage. The leakage is often triggered by either natural disaster such as earthquake or human negligence such as low maintenance of oil pipeline. Natural disaster is unpredictable and it is difficult to prevent; therefore, researches are carried out in detecting corrosion of transmission pipelines. In this research, a new oil pipeline corrosion prediction model is proposed. An associative classification technique named classification based on multiple association rules is applied in the proposed prediction model. This proposed prediction model named CMARGA is then enhanced by using genetic algorithm in order build an optimum decision tree. The decision tree is said optimum in term of the genetic algorithm is used to examine the correlation between a group of association rules instead of using one single rule in predicting a case. The prediction model, CMARGA is tested against 15 datasets from UCI machine learning which yielded average accuracy of 80.2041. After the validation, CMARGA is then tested against a simulated oil pipeline corrosion dataset consist of partial pressure carbon dioxide, velocity, and temperature. A good result of 96.6667 accuracy as single run validation is achieved; while, 96.0 accuracy obtained when runs through tenth cross validation. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010378854&doi=10.1109%2fICCOINS.2016.7783249&partnerID=40&md5=f17c3cdef3ea9a8150abfe49150d12ad Chern-Tong, H. and Aziz, I.B.A. (2016) A corrosion prediction model for oil and gas pipeline using CMARPGA. In: UNSPECIFIED. http://eprints.utp.edu.my/30507/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Pipelines are used as a medium to transport the oil, however, low maintenance causing not only the loss of the material itself but as well to the surrounding people and environment. In order to tackle the incidents, experts are assigned and experiments are conducted to analyze the source of the leakage. The leakage is often triggered by either natural disaster such as earthquake or human negligence such as low maintenance of oil pipeline. Natural disaster is unpredictable and it is difficult to prevent; therefore, researches are carried out in detecting corrosion of transmission pipelines. In this research, a new oil pipeline corrosion prediction model is proposed. An associative classification technique named classification based on multiple association rules is applied in the proposed prediction model. This proposed prediction model named CMARGA is then enhanced by using genetic algorithm in order build an optimum decision tree. The decision tree is said optimum in term of the genetic algorithm is used to examine the correlation between a group of association rules instead of using one single rule in predicting a case. The prediction model, CMARGA is tested against 15 datasets from UCI machine learning which yielded average accuracy of 80.2041. After the validation, CMARGA is then tested against a simulated oil pipeline corrosion dataset consist of partial pressure carbon dioxide, velocity, and temperature. A good result of 96.6667 accuracy as single run validation is achieved; while, 96.0 accuracy obtained when runs through tenth cross validation. © 2016 IEEE.
format Conference or Workshop Item
author Chern-Tong, H.
Aziz, I.B.A.
spellingShingle Chern-Tong, H.
Aziz, I.B.A.
A corrosion prediction model for oil and gas pipeline using CMARPGA
author_facet Chern-Tong, H.
Aziz, I.B.A.
author_sort Chern-Tong, H.
title A corrosion prediction model for oil and gas pipeline using CMARPGA
title_short A corrosion prediction model for oil and gas pipeline using CMARPGA
title_full A corrosion prediction model for oil and gas pipeline using CMARPGA
title_fullStr A corrosion prediction model for oil and gas pipeline using CMARPGA
title_full_unstemmed A corrosion prediction model for oil and gas pipeline using CMARPGA
title_sort corrosion prediction model for oil and gas pipeline using cmarpga
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010378854&doi=10.1109%2fICCOINS.2016.7783249&partnerID=40&md5=f17c3cdef3ea9a8150abfe49150d12ad
http://eprints.utp.edu.my/30507/
_version_ 1738657117435330560
score 13.211869

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