Multi-factor mining and corrosion rate prediction model construction of carbon steel under dynamic atmospheric corrosion environment

Song, Xiaoxiao; Wang, Keyu; Zhou, Lv; Chen, Yajun; Ren, Kaixu; Wang, Jiayu; Zhang, Chao

doi:10.1016/j.engfailanal.2021.105987

Cited by 22 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, many scholars around the world have been actively pursuing corrosion prediction models, which involve atmospheric corrosion, marine corrosion, microbial corrosion, etc. [9][10][11][12][13] . To predict the corrosion development of pipelines accurately, scientists are committed to constructing corrosion models from multidisciplinary knowledge.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

View full text Add to dashboard Cite

We have employed interpretable methods to uncover the black-box model of the machine learning (ML) for predicting the maximum pitting depth (dmax) of oil and gas pipelines. Ensemble learning (EL) is found to have higher accuracy compared with several classical ML models, and the determination coefficient of the adaptive boosting (AdaBoost) model reaches 0.96 after optimizing the features and hyperparameters. In this work, the running framework of the model was clearly displayed by visualization tool, and Shapley Additive exPlanations (SHAP) values were used to visually interpret the model locally and globally to help understand the predictive logic and the contribution of features. Furthermore, the accumulated local effect (ALE) successfully explains how the features affect the corrosion depth and interact with one another.

show abstract

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning for maximum corrosion depth and influence factor analysis

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Panchenko et al [ 41 ] developed other statistical indicators for this purpose and used data on steel and zinc corrosion obtained in continental territories. Several authors [ 42 ], [ 43 ] have pointed out that, due to the complexity of the relationship between different factors influencing atmospheric corrosion, DRFs are useful to a certain extent.…”

Section: Constructing Atmospheric Corrosion Mapsmentioning

confidence: 99%

“…In this sense and thanks to current technological advances, machine learning models are being used more frequently to construct corrosion maps employing the large amount of geographic information that is collected daily at different locations. Machine learning uses statistical models—i.e., regression vector models, RFAs, ANNs, Support Vector Machines (SVMs), Spearman’s correlation analysis, and simple and multiple regression models in order to impute or reduce the number of variables involved in a model, which can improve predictions [ 1 ], [ 2 ],[ 43 ].…”

Section: Constructing Atmospheric Corrosion Mapsmentioning

confidence: 99%

Atmospheric corrosion maps as a tool for designing and maintaining building materials: A review

Santa

Tamayo

Correa

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…The early step of this strategy is to determine the type and dose of CI that are right for the operating conditions so that the product can provide a corrosion-preventive coating on the internal parts of hydrocarbon pipelines that carry corrosive substances (such as carbon dioxide gas, hydrogen sulfide gas, produced water content, etc.). The performance of CI injection can be evaluated through the corrosion rate value in the pipe inspection program (Song et al, 2022). The value is expected to be as low as possible as an indicator of maintained pipe integrity.…”

Section: Introductionmentioning

confidence: 99%

Hydrogen Sulfide Measurement of Degraded Corrosion Inhibitor with Glass Tube Detector in Oil & Gas Industry

Sastra

Susilowati²

2022

IJOSH

View full text Add to dashboard Cite

Introduction: Corrosion inhibitor (CI) is injected as carbon steel pipe corrosion protection with sulfur-containing substances in the product. One type of them is thioglycolic acid (TGA). Besides having benefits in maintaining pipe integrity, TGA can be decomposed to HS (hydrogen sulfide) due to changes in ambient temperature during storage, such as direct sunlight exposure on the field. This irritant gas can pose a risk to the health of chemical workers. Therefore, this study aims to measure the concentration of H2S in a CI product containing TGA. Method: The data were collected from an oil and gas company measurement report on 12 CI drums with 1-3%w of TGA content by using a glass tube detector. Measurements were performed by varying the measurement distance (0 and 10 cm from the mouth of the drum), observing the condition of the inflated drum surface, and determining the existence of internal pressure. Results: All samples were contained H2S, and the inflated drums had higher H2S content than those that were not inflated up to more than 200 ppm in the drum bore. At this concentration, workers can experience pulmonary edema significantly prolonged exposure. Biological monitoring can be done by analyzing thiosulfate content in urine and blood after exposure or routine examination at the end of the work shift. Conclusion: CI with TGA content has the potential of high H2S concentration, and it requires risk control such as engineering control, administration control, and PPE application to minimize the health impact of H2S exposure to the workers.

show abstract

Multi-factor mining and corrosion rate prediction model construction of carbon steel under dynamic atmospheric corrosion environment

Cited by 22 publications

References 37 publications

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Interpretable machine learning for maximum corrosion depth and influence factor analysis

Atmospheric corrosion maps as a tool for designing and maintaining building materials: A review

Hydrogen Sulfide Measurement of Degraded Corrosion Inhibitor with Glass Tube Detector in Oil & Gas Industry

Contact Info

Product

Resources

About