Machine learning unlocks robust convergence for chemical process simulations

Machine learning unlocks robust convergence for chemical process simulations | 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 unlocks robust convergence for chemical process simulations Dion Jakobs, Lucas F. Santos, Gonzalo Guillén-Gosálbez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8262500/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 Convergence failures in process simulation are a critical barrier to designing the novel chemical processes required for a sustainable economy, often leaving engineers unable to distinguish numerical instability from true physical infeasibility. Here we introduce COSMIC, a machine-learning copilot that reframes convergence as a surrogate-based optimization problem. Numerical examples show that COSMIC can converge industrial flowsheets where conventional solvers may fail. This enhanced reliability can significantly expand the volume of explorable design space by more than 200% relative to conventional solver’s feasible region. By transforming convergence from a fragile bottleneck into a systematic, data-driven step, COSMIC enables reliable design space exploration and accelerates the innovation of next-generation chemical processes. Chemical Engineering Process Simulation Machine Learning Simulation Convergence Surrogate-based Optimization Full Text Additional Declarations The authors declare no competing interests. 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-8262500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554189146,"identity":"5e5247a7-c722-4ab6-8759-9fb2d4c8227a","order_by":0,"name":"Dion Jakobs","email":"","orcid":"https://orcid.org/0009-0001-1994-9780","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Dion","middleName":"","lastName":"Jakobs","suffix":""},{"id":554190567,"identity":"8fec8239-3156-48fd-a799-a02bf4b4e26f","order_by":1,"name":"Lucas F. Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie3RPWrDMBTA8ScE6vJoVhVDfYKChUGhNKVXsTF4SqFjh5AaBJl6gEAv4cmQTfDAOUOhQ0sgc4IXFzLUTZZmkJsxg/6DPkA/JBCAz3eGXR7tNmD3M/8dIgcRf1dsfiDMnEw4nkRk9rVtYRoOQ0PNaPJxfVPw5eppB+HQSfI4QCC1mIk8GNfrWFuRmjmCWhQuMoYAwLKyRs0fC0ori8qghCSyTsK/u4c9dCRubgt6qexgazDqJUIi8LQjUcAKSrRFZjDpIbjWdxhRVtZ5fvVak6pIqDe0UpUOMrjIVu/t8/S+JKJNO6FQL81ng7tR6Lrl0NEX7L8eZN95n8/n8/3TD/dhVYa9ljGJAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5931-4272","institution":"ETH Zürich","correspondingAuthor":true,"prefix":"","firstName":"Lucas","middleName":"F.","lastName":"Santos","suffix":""},{"id":554190568,"identity":"96e5b294-9463-4935-b555-c4b51df57eb1","order_by":2,"name":"Gonzalo Guillén-Gosálbez","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6074-8473","institution":"ETH Zürich","correspondingAuthor":true,"prefix":"","firstName":"Gonzalo","middleName":"","lastName":"Guillén-Gosálbez","suffix":""}],"badges":[],"createdAt":"2025-12-02 16:05:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8262500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8262500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98778207,"identity":"80cb37e4-6d1b-4a28-9841-e5bc6033bac7","added_by":"auto","created_at":"2025-12-22 12:29:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478221,"visible":true,"origin":"","legend":"","description":"","filename":"COSMICpreprintfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8262500/v1_covered_c8ffdc24-d227-4351-bf92-1987bb90b931.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMachine learning unlocks robust convergence for chemical process simulations\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[{"identity":"b0a78da6-5476-47a0-8eae-80f3748becd7","identifier":"10.13039/501100001711","name":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","awardNumber":"214877","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ETH Zurich","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":"Process Simulation, Machine Learning, Simulation Convergence, Surrogate-based Optimization","lastPublishedDoi":"10.21203/rs.3.rs-8262500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8262500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConvergence failures in process simulation are a critical barrier to designing the novel chemical processes required for a sustainable economy, often leaving engineers unable to distinguish numerical instability from true physical infeasibility. Here we introduce COSMIC, a machine-learning copilot that reframes convergence as a surrogate-based optimization problem. Numerical examples show that COSMIC can converge industrial flowsheets where conventional solvers may fail. This enhanced reliability can significantly expand the volume of explorable design space by more than 200% relative to conventional solver’s feasible region. By transforming convergence from a fragile bottleneck into a systematic, data-driven step, COSMIC enables reliable design space exploration and accelerates the innovation of next-generation chemical processes.\u003c/p\u003e","manuscriptTitle":"Machine learning unlocks robust convergence for chemical process simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 07:16:35","doi":"10.21203/rs.3.rs-8262500/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":"dbc27cec-c11f-44de-813b-b3d7b891b6cd","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59954827,"name":"Chemical Engineering"}],"tags":[],"updatedAt":"2025-12-22T07:16:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 07:16:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8262500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8262500","identity":"rs-8262500","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}