ANN and SVR Modeling to predict Bio-surfactant Enhanced Pool Boiling Heat Transfer

ANN and SVR Modeling to predict Bio-surfactant Enhanced Pool Boiling Heat Transfer | 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 ANN and SVR Modeling to predict Bio-surfactant Enhanced Pool Boiling Heat Transfer Prashant Kumar Jha, Abhilas Swain, Radha Kanta Sarangi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7907453/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 focuses on predicting saturated pool boiling heat transfer enhanced with a nonionic biosurfactant (coco glucoside) using machine learning models. Pool boiling experiments were conducted on a copper surface using methanol, ethanol, and acetone as working fluids. Each fluid was mixed with different concentrations of coco glucoside (0.5, 1.0, and 1.5 milliliters per 100 milliliters) and tested under heat fluxes ranging from 13,636 to 60,000 watts per square meter. The heat transfer coefficients obtained from these experiments served as target outputs for model training. Two supervised machine learning models, Artificial Neural Network (ANN) and Support Vector Regression (SVR), were developed to capture the complex relationships among thermophysical properties, biosurfactant concentration, and heat flux. Both models demonstrated excellent predictive performance, with SVR achieving an R² of 0.99, a mean absolute percentage error (MAPE) of 2.8%, and a root mean square error (RMSE) of 117.8 W/m 2 K, while ANN achieved an R² of 0.98, a MAPE of 3.4%, and an RMSE of 147.6 W/m 2 K. Parity plots confirmed strong agreement between predicted and experimental heat transfer coefficient values. This study highlights the effectiveness of integrating biosurfactant-enhanced boiling with machine learning models to optimize thermal systems and provide a reliable predictive framework for different fluids and operating conditions. Pool boiling heat transfer Biosurfactant Artificial neural network Support vector regression Heat transfer coefficient Machine learning 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. 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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-7907453","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558559177,"identity":"49383e15-18a9-4bba-b9c7-f1f18a0342b4","order_by":0,"name":"Prashant Kumar Jha","email":"","orcid":"","institution":"Kalinga Institute of Industrial Technology","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"Kumar","lastName":"Jha","suffix":""},{"id":558559180,"identity":"f558c4b7-4b77-4f24-880d-b193a57c10bc","order_by":1,"name":"Abhilas 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Transfer","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":"Pool boiling heat transfer, Biosurfactant, Artificial neural network, Support vector regression, Heat transfer coefficient, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7907453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7907453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on predicting saturated pool boiling heat transfer enhanced with a nonionic biosurfactant (coco glucoside) using machine learning models. Pool boiling experiments were conducted on a copper surface using methanol, ethanol, and acetone as working fluids. Each fluid was mixed with different concentrations of coco glucoside (0.5, 1.0, and 1.5 milliliters per 100 milliliters) and tested under heat fluxes ranging from 13,636 to 60,000 watts per square meter. The heat transfer coefficients obtained from these experiments served as target outputs for model training. Two supervised machine learning models, Artificial Neural Network (ANN) and Support Vector Regression (SVR), were developed to capture the complex relationships among thermophysical properties, biosurfactant concentration, and heat flux. Both models demonstrated excellent predictive performance, with SVR achieving an R\u0026sup2; of 0.99, a mean absolute percentage error (MAPE) of 2.8%, and a root mean square error (RMSE) of 117.8 W/m\u003csup\u003e2\u003c/sup\u003eK, while ANN achieved an R\u0026sup2; of 0.98, a MAPE of 3.4%, and an RMSE of 147.6 W/m\u003csup\u003e2\u003c/sup\u003eK. Parity plots confirmed strong agreement between predicted and experimental heat transfer coefficient values. This study highlights the effectiveness of integrating biosurfactant-enhanced boiling with machine learning models to optimize thermal systems and provide a reliable predictive framework for different fluids and operating conditions.\u003c/p\u003e","manuscriptTitle":"ANN and SVR Modeling to predict Bio-surfactant Enhanced Pool Boiling Heat Transfer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 20:12:39","doi":"10.21203/rs.3.rs-7907453/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8dc8728e-923a-462a-bfeb-d449702d7dd8","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T20:12:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 20:12:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7907453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7907453","identity":"rs-7907453","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}