Available for download
Product Number:
51318-11223-SG
Author:
H. Li / K. M. Yap / S. Srinivasan
Publication Date:
2018
$20.00
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Product Number:
MPWT19-15162
Author:
Ayman Amer, Ali Al Shehri, Vincent Cunningham, Ihsan Taie
Publication Date:
2019
$0.00
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Product Number:
51322-17991-SG
Author:
Otavio Correa, Jorge Seleme Mariano
Publication Date:
2022
$20.00
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Product Number:
51322-17992-SG
Author:
Otavio Correa, Jorge Seleme Mariano
Publication Date:
2022
$20.00
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Product Number:
51322-17901-SG
Author:
Winston Robbins, Sridhar Srinivasan, Abbey Wing, Gerrit Buchheim
Publication Date:
2022
$20.00
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Product Number:
51324-20805-SG
Author:
Matt Barrett; Mill Jawed; Dylan Bolch; William Maize; Tony da Costa
Publication Date:
2024
$40.00
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Product Number:
51321-16812-SG
Author:
Y. Zheng; J. Sonke; W. M. Bos
Publication Date:
2021
$20.00
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Product Number:
51317--9419-SG
ISBN:
9419 2017 CP
Author:
Hui Li
Publication Date:
2017
$20.00
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Product Number:
51324-20803-SG
Author:
Emily Seto; Meifeng Li; Jing Liu
Publication Date:
2024
$40.00
The importance of effective corrosion management in carbon capture, utilization, and storage (CCUS) networks has significantly increased. Captured CO2 is often transported in the supercritical state (s-CO2) and can contain impurities like H2O, O2, SOx, or NOx. While repurposing existing oil and gas pipelines for s-CO2 transport has been suggested, further testing and risk assessment is required to validate this strategy and its associated risks. Given the substantial amount of corrosion data available from recent corrosion studies, machine learning (ML) has emerged as a promising tool for corrosion prediction and management. This study aims to utilize supervised ML techniques to predict the corrosion severity of pipeline steels operating in s-CO2 systems. The selected algorithms, random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) were trained on a comprehensive data set of X-series pipeline steels which includes corrosion rates, impurity levels, temperatures, pressures, and exposure times. Additional testing data set and error and accuracy scores were used to determine the most accurate algorithm. An additional experimental testing was performed to verify the predictions of the model. It was found that the RF model had the best accuracy of 65.3% out of the three tested models and KNN had the worst accuracy of 59.2%. In multiple impurity environments the RF model was able to accurately predict corrosion severity but overestimated corrosion severity in environments with short exposure times.
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Product Number:
51323-19105-SG
Author:
Winston Robbins, Sridhar Srinivasan, Gerrit Buchheim
Publication Date:
2023
$20.00
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Product Number:
21179-SG
ISBN:
1-57590-240-0
Publication Date:
2010
$179.00
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Product Number:
21161-SG
ISBN:
1-57590-240-0
Publication Date:
2010
$179.00
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