Numerical Simulation of Red Mud Blended Raw Materials in a Precalciner

Numerical Simulation of Red Mud Blended Raw Materials in a Precalciner | 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 Numerical Simulation of Red Mud Blended Raw Materials in a Precalciner Kai Huang, Hongtao Kao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9145137/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 cement industry is a major contributor to global carbon emissions and pollutants, so reducing emissions while utilizing industrial wastes is critical for its green development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO2, Al2O3, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s threestage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075~0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results,this study uses ANSYS Fluent to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors <5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Results show red mud slightly alters the internal temperature field, reduces raw meal decomposition rate (all values within the 85%~95% industrial range), and effectively decreases CO2 and NOx emissions. A 5% ratio is recommended, which balances waste utilization, decomposition efficiency, and emission reduction, providing solid technical support for red mud’s large-scale use in cement production. Precalciner Red Mud Numerical Simulation Alternative raw material Full Text 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-9145137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619294940,"identity":"da267f2b-e8aa-49ee-ab30-8052e11d1713","order_by":0,"name":"Kai Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNvmDjQ8+/rOR42dvPvggoaKGsBY+CeZmwxlsacaSPceSDR6cOUZYi5wEe5s0D9vhRIMbPmaSD1uYiXCYdGOzMQ/P4QTJGTxmFYkNbAz87d0J+LXIHGx8OEciPY9fuq3sRuIOGQaJM2c34NfCkNhs8MbAulhyzuFtNxLPsDEYSOQS1NImwZPAnLjhRoJZQWIbMxFaJBLbJHkOOAO1pJgxEKeF52Cz4cwGSCBLJJw5xkPQL/Lt7Q8ffGyAROXHHxU1cvztvfi1YAAe0pSPglEwCkbBKMAKAMG2TijAiGlpAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-8598-160X","institution":"Nanjing Tech University","correspondingAuthor":true,"prefix":"","firstName":"Kai","middleName":"","lastName":"Huang","suffix":""},{"id":619294941,"identity":"14d95f0f-dffd-488a-a76a-c2823ed87673","order_by":1,"name":"Hongtao Kao","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Kao","suffix":""}],"badges":[],"createdAt":"2026-03-17 06:56:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9145137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9145137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108988490,"identity":"c9a591b4-d03f-4e40-94d6-69f6e26acb29","added_by":"auto","created_at":"2026-05-11 13:06:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":898913,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9145137/v1_covered_86243096-4bde-4ba0-82ce-351aa24bc4ef.pdf"}],"financialInterests":"","formattedTitle":"Numerical Simulation of Red Mud Blended Raw Materials in a Precalciner","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":"Precalciner, Red Mud, Numerical Simulation, Alternative raw material","lastPublishedDoi":"10.21203/rs.3.rs-9145137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9145137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The cement industry is a major contributor to global carbon emissions and pollutants, so reducing emissions while utilizing industrial wastes is critical for its green development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO2, Al2O3, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s threestage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075~0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results,this study uses ANSYS Fluent to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors \u0026lt;5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Results show red mud slightly alters the internal temperature field, reduces raw meal decomposition rate (all values within the 85%~95% industrial range), and effectively decreases CO2 and NOx emissions. 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