Physics-guided multi-well forecasting of uranium concentration in pregnant leach solutions from in-situ leaching wellfields: reducing hydrogeochemical peak-lag for process optimization

Physics-guided multi-well forecasting of uranium concentration in pregnant leach solutions from in-situ leaching wellfields: reducing hydrogeochemical peak-lag for process optimization | 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 Physics-guided multi-well forecasting of uranium concentration in pregnant leach solutions from in-situ leaching wellfields: reducing hydrogeochemical peak-lag for process optimization Qiang Li, Zhifeng Liu, Yipeng Zhou, Jianfeng He, Zhenhua Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8420555/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 Hydrogeochemical response lag significantly complicates process control in uranium in-situ leaching (ISL), particularly within heterogeneous wellfields. To address this, a Physics-Guided State-Space Model (PG-SSM) is proposed to explicitly encode seven-spot topology and decouple fast operational signals from slow concentration dynamics. The model integrates soft physical regularizations to ensure hydrogeochemical plausibility. Evaluated on field data, PG-SSM achieves high accuracy (R 2 = 0.928, RMSE = 0.043 mg·L⁻¹) and reduces peak-timing lag to approximately + 1.5 days, significantly outperforming baselines. These results demonstrate that integrating topological priors and physical constraints effectively mitigates lag, providing a reliable basis for proactive ISL monitoring and control. In-situ leaching of uranium Hydrogeochemical response lag Multi-well forecasting Physics-guided deep learning Process optimization 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8420555","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571880385,"identity":"7230c06b-8c9a-4a14-9e7c-13e693329a07","order_by":0,"name":"Qiang Li","email":"","orcid":"","institution":"East China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""},{"id":571880386,"identity":"14477867-6aff-4dab-9653-d4ba488be055","order_by":1,"name":"Zhifeng 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optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"In-situ leaching of uranium, Hydrogeochemical response lag, Multi-well forecasting, Physics-guided deep learning, Process optimization","lastPublishedDoi":"10.21203/rs.3.rs-8420555/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8420555/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrogeochemical response lag significantly complicates process control in uranium in-situ leaching (ISL), particularly within heterogeneous wellfields. To address this, a Physics-Guided State-Space Model (PG-SSM) is proposed to explicitly encode seven-spot topology and decouple fast operational signals from slow concentration dynamics. The model integrates soft physical regularizations to ensure hydrogeochemical plausibility. Evaluated on field data, PG-SSM achieves high accuracy (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.928, RMSE\u0026thinsp;=\u0026thinsp;0.043 mg\u0026middot;L⁻\u0026sup1;) and reduces peak-timing lag to approximately\u0026thinsp;+\u0026thinsp;1.5 days, significantly outperforming baselines. These results demonstrate that integrating topological priors and physical constraints effectively mitigates lag, providing a reliable basis for proactive ISL monitoring and control.\u003c/p\u003e","manuscriptTitle":"Physics-guided multi-well forecasting of uranium concentration in pregnant leach solutions from in-situ leaching wellfields: reducing hydrogeochemical peak-lag for process optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 12:59:00","doi":"10.21203/rs.3.rs-8420555/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":"e5021699-b1ef-42d9-b505-9c25ca5a6e96","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 12:59:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8420555","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8420555","identity":"rs-8420555","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}