Optimization of Iron Ore Flotation Parameters Using Response Surface Methodology

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Abstract This study proposes a method to optimize the flotation parameters of quartz for concentrating iron ore into pellets. With the reduction in mineral deposits with high iron contents and the increasing demand for low ore contaminants for steel production, it is important to evaluate the effects of parameters that influence flotation performance. Seventeen bench flotation tests were conducted using a Central Composite Design (CCD) to identify the process response conditions. Optimization using the Generalized Reduced Gradient (GRG) algorithm resulted in dosage values of 500 g/t for feed, 75 g/t for SiO2, and a pH of 9.80 for starch, amine, and pH, respectively. The calculated iron recovery was 95.21%, with a SiO2 content in the concentrate of 0.46%. Tests under optimal conditions confirmed an iron recovery of 93.86% and a SiO2 of 0.51% in the flotation concentrate. These findings contribute to maximizing the utilization of mineral resources and obtaining high-quality ores.
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Optimization of Iron Ore Flotation Parameters Using Response Surface Methodology | 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 Optimization of Iron Ore Flotation Parameters Using Response Surface Methodology TARCISIO GONCALVES DE BRITO, Tiago Caixeta Nunes, Emerson José de Paiva, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5183292/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 proposes a method to optimize the flotation parameters of quartz for concentrating iron ore into pellets. With the reduction in mineral deposits with high iron contents and the increasing demand for low ore contaminants for steel production, it is important to evaluate the effects of parameters that influence flotation performance. Seventeen bench flotation tests were conducted using a Central Composite Design (CCD) to identify the process response conditions. Optimization using the Generalized Reduced Gradient (GRG) algorithm resulted in dosage values of 500 g/t for feed, 75 g/t for SiO 2 , and a pH of 9.80 for starch, amine, and pH, respectively. The calculated iron recovery was 95.21%, with a SiO 2 content in the concentrate of 0.46%. Tests under optimal conditions confirmed an iron recovery of 93.86% and a SiO 2 of 0.51% in the flotation concentrate. These findings contribute to maximizing the utilization of mineral resources and obtaining high-quality ores. Central Composite Design (CCD) flotation iron ore optimization Response Surface Methodology (RSM) Full Text Supplementary Files Graficalabstract.jpg 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-5183292","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":365017456,"identity":"d3814974-0f10-4b3b-a483-7dc4db666702","order_by":0,"name":"TARCISIO GONCALVES DE 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