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Products tagged with 'supercritical co2'

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Picture for Experimental Evaluation Of Corrosion Modeling On Carbon Steel In Sub-Critical And Supercritical CO2 Environments
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Experimental Evaluation Of Corrosion Modeling On Carbon Steel In Sub-Critical And Supercritical CO2 Environments

Product Number: 51321-16750-SG
Author: Chin-Hua “Jim” Cheng; Raymundo Case
Publication Date: 2021
$20.00
Picture for Influence of High CO2 Partial Pressure on Top-of-the-Line Corrosion
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Influence of High CO2 Partial Pressure on Top-of-the-Line Corrosion

Product Number: 51324-21220-SG
Author: Maryam Eslami; Bernardo Augusto Farah Santos; David Young; Sondre Gjertsen; Marc Singer
Publication Date: 2024
$40.00
Top-of-the-line corrosion (TLC) is an important type of material degradation that occurs due to the heat exchange between the pipeline and its surroundings, which results in water condensation on the internal surface of the pipe. This type of corrosion is specific to wet gas pipelines with stratified flow regimes. In this research, the effect of high CO2 partial pressure (pCO2) on TLC rate and mechanism was studied. The experiments were conducted in a high-pressure TLC autoclave with pCO2 ranging from 20 to 100 bar, solution temperatures of 30 and 50 °C, and different water condensation conditions (0.001-0.1 ml/m2.s). The experimental conditions covered environments where CO2 was either gaseous or supercritical. The results revealed that uniform and localized TLC rates increase with water condensation rate and solution temperature. However, as long as CO2 remained gaseous, pCO2 showed a negligible influence on both uniform and localized TLC rates. At a high CO2 content, the formation of a protective FeCO3 layer decreased the TLC rate, especially at lower water condensation rates. Nevertheless, the risk of localized corrosion at high and medium water condensation rates remained an issue. In the supercritical CO2 environment (pCO2 of 100 bar and solution temperature of 50 °C), the difference in temperature between the CO2 dense phase and the specimens caused water drop out and corrosion. In this environment, the high pCO2 and low pH of the dropped-out water led to high uniform and localized corrosion rates. However, under this condition, the difference in corrosion rates of specimens with different cooling rates was negligible due to their similar surface temperature.
Picture for Oxidation Of Welded Materials In High Temperature Supercritical Carbon Dioxide
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Oxidation Of Welded Materials In High Temperature Supercritical Carbon Dioxide

Product Number: 51321-16961-SG
Author: Florent Bocher
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.