A multi-fluid machine learning framework for leakage prediction in pressurized pipeline systems

A multi-fluid machine learning framework for leakage prediction in pressurized pipeline systems | 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 Research Article A multi-fluid machine learning framework for leakage prediction in pressurized pipeline systems Koyndrik Bhattacharjee, Pronab Roy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8810769/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 Pipeline leakage to this day is considered one of the most serious operational issues on the municipal, industrial, chemical, and fuel-transport networks. Conventional leak modeling methods use a model that is water-based in nature and thus such models cannot be used to model fluids with different density-viscosity behavior. This research suggests a hybrid approach of predicting multi-fluids leaks through the combination of laminar outputs simulations, density-augmented analytical equations, and machine learning using multiple models. To isolate hydraulic effects of the fluids, four fluids, industry-influenced water, ethanol, diesel and glycerin, have been simulated at the same geometries of a pipeline consisting of a stainless-steel pipeline. The classical equations of leak size and leak position were re-parameterized to include fluid density and characterize the leak in fluid formations besides water-based models. Ridge regression, random forest, gradient boosting regression (GBR), support vector regression (SVR) and multi-layer perceptron (MLP) which are the five supervised regression models were trained to predict pressure and flow rate produced by leaks. Findings indicate that ensemble models, especially the random forest and GBR are the most accurate models and have better cross-fluids stability ensured by low error variance as well as high agreement between feature-importance with the hydraulic theory. Pipeline leakage multi-fluid modeling leak detection machine learning density-augmented equations pressure prediction. 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. 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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-8810769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588498389,"identity":"cde44e2c-01de-44a5-9171-a30d4057bebc","order_by":0,"name":"Koyndrik Bhattacharjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACCRDBwyBhwA9iJBSQokWyAaTFgHgtDAYGB0AsYrRItveYPXi7x8LY+PzqxA8PDBjk+cUO4NcizXPG3HDOMwkzsxtvN0sAHWY4c3YCfi1yEjlm0jwHJGzMbpzdANKSYHCbWC3GM85u/kGUFmmoFjMD/t5txNki2XOsTHLOAQljiRu82ywSDCQI+0XiePM2iTcH6gz7+89uvvmjwkaeX5qAFgYGDmhcSIBVShBSDgLsDyA0/wFiVI+CUTAKRsFIBABrHz+SwHnanQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Koyndrik","middleName":"","lastName":"Bhattacharjee","suffix":""},{"id":588498390,"identity":"593f8191-bf8c-4596-a14a-bca5ab869039","order_by":1,"name":"Pronab Roy","email":"","orcid":"","institution":"National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Pronab","middleName":"","lastName":"Roy","suffix":""}],"badges":[],"createdAt":"2026-02-06 21:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8810769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8810769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103197657,"identity":"14d76dd3-7d89-4b68-a9bb-9ad3b45dcc33","added_by":"auto","created_at":"2026-02-23 04:40:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":949477,"visible":true,"origin":"","legend":"","description":"","filename":"AMultiFluidMachineLearningFrameworkforLeakagePredictioninPressurizedPipelineSystems321.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8810769/v1_covered_40035b8a-047e-4a60-987f-c37a14eb7572.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A multi-fluid machine learning framework for leakage prediction in pressurized pipeline systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Pipeline leakage, multi-fluid modeling, leak detection, machine learning, density-augmented equations, pressure prediction.","lastPublishedDoi":"10.21203/rs.3.rs-8810769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8810769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePipeline leakage to this day is considered one of the most serious operational issues on the municipal, industrial, chemical, and fuel-transport networks. 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