Predicting CO₂ Corrosion of Natural Gas Pipeline Transport using Supervised Machine Learning Models

Predicting CO₂ Corrosion of Natural Gas Pipeline Transport using Supervised Machine Learning Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting CO₂ Corrosion of Natural Gas Pipeline Transport using Supervised Machine Learning Models Joan Ejeta, Tolu Emiola, Robert Eshun, Kristen Rhinehardt This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7077550/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study develops a robust machine learning model to predict the CO₂ corrosion rates of wet natural gas pipelines, a crucial aspect of pipeline management in energy systems. It uses a combination of field inspection and HYSYS simulation data to train and evaluate eight regression models, including Linear Regression (LR), Ridge (R), Decision Trees (DT), Bagging (B), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). The expectation-maximization method is applied to fill in the missing values in the combined dataset. Additionally, each model undergoes k-fold cross-validation and hyperparameter tuning to ensure high performance and accuracy. Feature selection identified key corrosion predictors such as temperature, CO₂ partial pressure, pH, and inhibitor efficiency for predicting the CO 2 corrosion rate. The bagging regression model outperformed the other models, achieving R 2 scores of 0.978 for the combined datasets. The proposed machine learning framework offers a cost-effective, data-driven approach for improving pipeline design and integrity management. Physical sciences/Materials science/Materials for energy and catalysis/Corrosion Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Scientific data Machine learning CO2 corrosion model evaluation bagging regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7077550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492599332,"identity":"2d1caae6-9279-4988-8be8-390d0c9b9d26","order_by":0,"name":"Joan Ejeta","email":"","orcid":"","institution":"North Carolina A \u0026 T State University","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Ejeta","suffix":""},{"id":492599333,"identity":"bb8f74f4-4179-4cfd-9115-f0cb92edf1f4","order_by":1,"name":"Tolu Emiola","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"prefix":"","firstName":"Tolu","middleName":"","lastName":"Emiola","suffix":""},{"id":492599334,"identity":"530520aa-1853-4f1e-86f9-5bd7fbdbe1b1","order_by":2,"name":"Robert Eshun","email":"","orcid":"","institution":"North Carolina A \u0026 T State University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Eshun","suffix":""},{"id":492599335,"identity":"dd2d898c-11f2-4543-bff2-7d67eeba1fa4","order_by":3,"name":"Kristen Rhinehardt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACCXYGNoYEBoYEfqgAYwNBLcxQLZIgpQeI1gIECQYHiNUi2cz77MHDHTZ5xrebH37+wGAju+EAAS3SzOzmBoln0orN7hwzljjAkGZMUIscMxubRGLb4cRtN3IYgFoOJxKr5X/i5hk5zD8OMPwnrEUaouVA4gaJHDagLQcIa5FsBmtJTpxxI83M4oxBsvFMQlokjrexSf5ss0vsn5H8+EZFhZ1sHyEtaMCANOWjYBSMglEwCnAAADxrQewc/KNBAAAAAElFTkSuQmCC","orcid":"","institution":"North Carolina A \u0026 T State University","correspondingAuthor":true,"prefix":"","firstName":"Kristen","middleName":"","lastName":"Rhinehardt","suffix":""}],"badges":[],"createdAt":"2025-07-08 18:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7077550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7077550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752754,"identity":"3941de76-7bc7-434f-81ea-36953a781ee8","added_by":"auto","created_at":"2026-02-03 10:29:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":666338,"visible":true,"origin":"","legend":"","description":"","filename":"MLcorrosionpaperFinalDraft07162025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7077550/v1_covered_6bcb84e2-2467-44ca-b6f1-a824475cfe66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting CO₂ Corrosion of Natural Gas Pipeline Transport using Supervised Machine Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, CO2 corrosion, model evaluation, bagging regression","lastPublishedDoi":"10.21203/rs.3.rs-7077550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7077550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops a robust machine learning model to predict the CO₂ corrosion rates of wet natural gas pipelines, a crucial aspect of pipeline management in energy systems. It uses a combination of field inspection and HYSYS simulation data to train and evaluate eight regression models, including Linear Regression (LR), Ridge (R), Decision Trees (DT), Bagging (B), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF). The expectation-maximization method is applied to fill in the missing values in the combined dataset. Additionally, each model undergoes k-fold cross-validation and hyperparameter tuning to ensure high performance and accuracy. Feature selection identified key corrosion predictors such as temperature, CO₂ partial pressure, pH, and inhibitor efficiency for predicting the CO\u003csub\u003e2\u003c/sub\u003e corrosion rate. The bagging regression model outperformed the other models, achieving R\u003csup\u003e2\u003c/sup\u003e scores of 0.978 for the combined datasets. 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