Machine Learning Algorithms Optimize Prediction Accuracy in Submersible Ballast Tank High-pressure Air Blowing Processes

Machine Learning Algorithms Optimize Prediction Accuracy in Submersible Ballast Tank High-pressure Air Blowing Processes | 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 Machine Learning Algorithms Optimize Prediction Accuracy in Submersible Ballast Tank High-pressure Air Blowing Processes Xi-Guang HE, Bin HUANG, Jing-jun LOU, Jia-bao CHEN, Li-kun PENG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4534614/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 The optimization of prediction accuracy in the high-pressure air blowing processes of submersible ballast tanks is crucial for enhancing the safety and efficiency of submersible operations. This study presents a comparative analysis between machine learning algorithms and traditional numerical simulation methods for predicting the dynamics of air blowing in submersible ballast tanks. The investigation focuses on the performance of the Back Propagation Neural Network (BPNN) and the Radial Basis Function Neural Network (RBFNN), employing a series of experimental data to train and validate the models.The results indicate that while traditional numerical simulations exhibit a maximum relative error of 124.805%, the RBFNN, in particular, achieves a remarkable linear correlation coefficient of 1, a mean square error of 5.6758e-23, and a mean absolute percentage error of 7.4888e-11. These findings demonstrate the superior predictive capability of the RBFNN, suggesting that machine learning algorithms can significantly improve the accuracy of predictions concerning the high-pressure air blowing process. The study advocates for the adoption of machine learning techniques in submersible engineering, potentially transforming the reliability and safety of deep-sea exploration endeavors. Submersible Ballast Tank High-Pressure Air Blowing Machine Learning Safety and Reliability Numerical Simulation Back Propagation Neural Network Radial Basis Function Neural Network 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-4534614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317932678,"identity":"0546ac1c-3bf8-4060-8727-1e979accff4e","order_by":0,"name":"Xi-Guang HE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACNmb+jw8+VEgw27c3EKmFj53B2HDGGRt2A54DRGqR42cwk+ZtS+M3kEgg2mEMCRI8bIelzSUfb7zBUGMTTYyWAwYSPIeNLWenFVswHEvLbSCshbEhwUDicDLD7RwzCcaGw8RoAVqTYHC4vuHmGaK1sDE2HEhIYza4wUO0Fh6g0w7YMEv2AP2SQIxf5PvPsP/++0+CmZ/98MYbH2psCGtBBsRHDZIWUnWMglEwCkbByAAAU084mnP9PT4AAAAASUVORK5CYII=","orcid":"","institution":"Naval University of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Xi-Guang","middleName":"","lastName":"HE","suffix":""},{"id":317932679,"identity":"c96b9f1c-bb39-427b-9910-96eb25b1b6db","order_by":1,"name":"Bin HUANG","email":"","orcid":"","institution":"Naval University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"HUANG","suffix":""},{"id":317932680,"identity":"2f2a4c14-761b-4394-9440-27a5d26f10e2","order_by":2,"name":"Jing-jun LOU","email":"","orcid":"","institution":"Naval University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jing-jun","middleName":"","lastName":"LOU","suffix":""},{"id":317932681,"identity":"7f687bfb-f2be-49df-967f-46813d344109","order_by":3,"name":"Jia-bao CHEN","email":"","orcid":"","institution":"Naval University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jia-bao","middleName":"","lastName":"CHEN","suffix":""},{"id":317932682,"identity":"4344f236-d2c2-4de7-b896-709cefde6187","order_by":4,"name":"Li-kun PENG","email":"","orcid":"","institution":"Naval University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Li-kun","middleName":"","lastName":"PENG","suffix":""}],"badges":[],"createdAt":"2024-06-05 13:57:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4534614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4534614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95226242,"identity":"e96a11d9-ac4f-4dae-a4fd-ac0cbfddecc9","added_by":"auto","created_at":"2025-11-05 16:30:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855236,"visible":true,"origin":"","legend":"","description":"","filename":"02Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4534614/v1_covered_4fe6ccde-f2cf-48f4-883d-44986304e20c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Algorithms Optimize Prediction Accuracy in Submersible Ballast Tank High-pressure Air Blowing Processes","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":"Submersible Ballast Tank, High-Pressure Air Blowing, Machine Learning, Safety and Reliability, Numerical Simulation, Back Propagation Neural Network, Radial Basis Function Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-4534614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4534614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe optimization of prediction accuracy in the high-pressure air blowing processes of submersible ballast tanks is crucial for enhancing the safety and efficiency of submersible operations. This study presents a comparative analysis between machine learning algorithms and traditional numerical simulation methods for predicting the dynamics of air blowing in submersible ballast tanks. The investigation focuses on the performance of the Back Propagation Neural Network (BPNN) and the Radial Basis Function Neural Network (RBFNN), employing a series of experimental data to train and validate the models.The results indicate that while traditional numerical simulations exhibit a maximum relative error of 124.805%, the RBFNN, in particular, achieves a remarkable linear correlation coefficient of 1, a mean square error of 5.6758e-23, and a mean absolute percentage error of 7.4888e-11. These findings demonstrate the superior predictive capability of the RBFNN, suggesting that machine learning algorithms can significantly improve the accuracy of predictions concerning the high-pressure air blowing process. 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