Ensemble Machine Learning for CO 2 Corrosion Rate Prediction with Heterogeneous Datasets

Ensemble Machine Learning for CO 2 Corrosion Rate Prediction with Heterogeneous Datasets | 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 Ensemble Machine Learning for CO 2 Corrosion Rate Prediction with Heterogeneous Datasets Joan Ejeta, Tolu Emiola-Sadiq, Robert Eshun, Kristen Rhinehardt This is a preprint; it has not been peer reviewed by a journal.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-8896642/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 19 You are reading this latest preprint version Abstract Corrosion accounts for billions of dollars in financial losses across the energy industry. However, limited access to quality, publicly available pipeline corrosion data significantly hinders accurate prediction, prevention, and the development of effective, data-driven maintenance strategies. This study develops an ensemble machine-learning framework to predict CO 2 corrosion rates in carbon-steel pipelines. It uses a heterogeneous dataset that integrates simulated data, experimental results, and field measurements to train ensemble machine learning models. Data preprocessing involved removal of outliers and imputation of missing values using simple imputer with Gaussian Mixture Model Expectation–Maximization, which preserves multivariate dependencies. To improve sensitivity to rare, high-consequence corrosion events, Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise (SMOGN) was used to address target imbalances. Feature selection identified CO 2 partial pressure, pH, flow velocity, and temperature as dominant predictors. Hyperparameters of four ensemble regressors (Extra Trees, Gradient Boosting Regressor, Random Forest, XGBoost) were tuned using grid search and 3-fold cross-validation. The Gradient Boosting Regressor outperformed other models with accuracy and generalization on the test set (R 2 test = 0.70; MSE = 6.43 mm/yr). Model validation yields R 2 = 0.82 across 0–22 mm/yr and median absolute percentage errors below 50% in operationally critical regimes (≥ 1 mm/yr). The proposed machine learning framework offers a cost-effective, data-driven approach for improving pipeline integrity management on heterogeneous datasets. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Machine learning CO2 corrosion model evaluation ensemble machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationUpdatedmanuscript1.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 25 Feb, 2026 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. 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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-8896642","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":600251541,"identity":"64f79d6d-7556-40b9-bfb3-f6c5e384228b","order_by":0,"name":"Joan Ejeta","email":"","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":false,"prefix":"","firstName":"Joan","middleName":"","lastName":"Ejeta","suffix":""},{"id":600251542,"identity":"eaab6631-8929-4d1b-ac3f-d6bf978f24b9","order_by":1,"name":"Tolu Emiola-Sadiq","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"prefix":"","firstName":"Tolu","middleName":"","lastName":"Emiola-Sadiq","suffix":""},{"id":600251543,"identity":"840335be-8806-4076-a803-ac9e80c7f73d","order_by":2,"name":"Robert Eshun","email":"","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Eshun","suffix":""},{"id":600251544,"identity":"94e604bf-f1e8-4ce8-92ca-9369e3a64354","order_by":3,"name":"Kristen Rhinehardt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACCXYGNoYEBoYEfqgAYwNBLcxQLZIgpQeI1gIECQYHiNUi2cz77MHDHTZ5xrebH37+wGAju+EAAS3SzOzmBoln0orN7hwzljjAkGZMUIscMxubRGLb4cRtN3IYgFoOJxKr5X/i5hk5zD8OMPwnrEUaouVA4gaJHDagLQcIa5FsBmtJTpxxI83M4oxBsvFMQlokjrexSf5ss0vsn5H8+EZFhZ1sHyEtaMCANOWjYBSMglEwCnAAADxrQewc/KNBAAAAAElFTkSuQmCC","orcid":"","institution":"North Carolina Agricultural and Technical State University","correspondingAuthor":true,"prefix":"","firstName":"Kristen","middleName":"","lastName":"Rhinehardt","suffix":""}],"badges":[],"createdAt":"2026-02-16 23:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8896642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8896642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104402127,"identity":"74aaf8d3-46ae-435d-a8d7-9594f8310b03","added_by":"auto","created_at":"2026-03-11 12:14:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480105,"visible":true,"origin":"","legend":"","description":"","filename":"MLcorrosionpaperUpdatedv4resubManuscriptclean.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8896642/v1_covered_9a929fc9-e277-4396-aa06-058dcc1df7a3.pdf"},{"id":104002184,"identity":"f964b818-11c5-49ed-bd09-b325958099dc","added_by":"auto","created_at":"2026-03-05 14:12:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":635973,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationUpdatedmanuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8896642/v1/8886b87d7869ecdfa60bfc68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble Machine Learning for CO 2 Corrosion Rate Prediction with Heterogeneous Datasets","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, CO2 corrosion, model evaluation, ensemble machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8896642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8896642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCorrosion accounts for billions of dollars in financial losses across the energy industry. However, limited access to quality, publicly available pipeline corrosion data significantly hinders accurate prediction, prevention, and the development of effective, data-driven maintenance strategies. This study develops an ensemble machine-learning framework to predict CO\u003csub\u003e2\u003c/sub\u003e corrosion rates in carbon-steel pipelines. It uses a heterogeneous dataset that integrates simulated data, experimental results, and field measurements to train ensemble machine learning models. Data preprocessing involved removal of outliers and imputation of missing values using simple imputer with Gaussian Mixture Model Expectation\u0026ndash;Maximization, which preserves multivariate dependencies. To improve sensitivity to rare, high-consequence corrosion events, Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise (SMOGN) was used to address target imbalances. Feature selection identified CO\u003csub\u003e2\u003c/sub\u003e partial pressure, pH, flow velocity, and temperature as dominant predictors. Hyperparameters of four ensemble regressors (Extra Trees, Gradient Boosting Regressor, Random Forest, XGBoost) were tuned using grid search and 3-fold cross-validation. The Gradient Boosting Regressor outperformed other models with accuracy and generalization on the test set (R\u003csup\u003e2\u003c/sup\u003e test\u0026thinsp;=\u0026thinsp;0.70; MSE\u0026thinsp;=\u0026thinsp;6.43 mm/yr). Model validation yields R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.82 across 0\u0026ndash;22 mm/yr and median absolute percentage errors below 50% in operationally critical regimes (\u0026ge;\u0026thinsp;1 mm/yr). The proposed machine learning framework offers a cost-effective, data-driven approach for improving pipeline integrity management on heterogeneous datasets.\u003c/p\u003e","manuscriptTitle":"Ensemble Machine Learning for CO 2 Corrosion Rate Prediction with Heterogeneous Datasets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 14:12:06","doi":"10.21203/rs.3.rs-8896642/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T15:17:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T10:25:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T07:53:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T05:36:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T10:17:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290789517482830227416120148241988800063","date":"2026-03-20T14:06:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236914261190109993891251765052003926782","date":"2026-03-16T14:16:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259512743443414784168955361930802110091","date":"2026-03-16T05:37:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T04:52:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253511405473121416092639066034207325457","date":"2026-03-15T14:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304683300229592970087931961707436709324","date":"2026-03-14T18:00:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22731826994214061324064976907797687152","date":"2026-03-14T16:06:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326646191219457401112932708299273211355","date":"2026-03-14T14:48:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163213749708099170073656363403916305598","date":"2026-03-14T13:49:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188956209073250870645819133403373201705","date":"2026-03-03T10:44:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T01:41:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T09:47:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T18:01:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-25T17:55:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d20c8f45-d434-4853-99b2-a0eab9c195fb","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":63874333,"name":"Physical sciences/Engineering"},{"id":63874334,"name":"Physical sciences/Materials science"},{"id":63874335,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-30T15:26:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 14:12:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8896642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8896642","identity":"rs-8896642","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}