Evaluation of Machine Learning Algorithms in Predicting $${\text {CO}}_2$$ Internal Corrosion in Oil and Gas Pipelines-Reference-Cited by-同舟云学术

Evaluation of Machine Learning Algorithms in Predicting $${\text {CO}}_2$$ Internal Corrosion in Oil and Gas Pipelines

Published:2018-08-30 Issue: Volume: Page:236-254
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Mohammad Zubir Wan Mohammad Aflah,Abdul Aziz Izzatdin,Jaafar Jafreezal

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-00211-4_22

Reference42 articles.

1. Kennedy, J.L.: Oil and Gas Pipeline Fundamentals. Pennwell Books, Houston (1993)

2. Papavinasam, S.: Corrosion Control in the Oil and Gas Industry. Elsevier, New York (2013)

3. Naylor, B.: Burst oil pipeline in California severely corroded, investigators say (2015)

4. Cart, J.P.: Ruptured oil pipeline was corroded, federal regulators say. Los Angeles Times (2015)

5. Papavinasam, S., Revie, R.W., Friesen, W.I., Doiron, A., Panneerselvan, T.: Review of models to predict internal pitting corrosion of oil and gaspipelines. Corros. Rev. 24(3–4), 173–230 (2006)

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