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Picture for A Prediction and Inspection Technology for Detecting the Corrosion Under Insulation
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A Prediction and Inspection Technology for Detecting the Corrosion Under Insulation

Product Number: MPWT19-15162
Author: Ayman Amer, Ali Al Shehri, Vincent Cunningham, Ihsan Taie
Publication Date: 2019
$0.00
Picture for Development Of A New Top-Of-Line Corrosion Model For Sweet Wet Gas Pipelines
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Development Of A New Top-Of-Line Corrosion Model For Sweet Wet Gas Pipelines

Product Number: 51321-16812-SG
Author: Y. Zheng; J. Sonke; W. M. Bos
Publication Date: 2021
$20.00
	Picture for Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning
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Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning

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.