Recurrent Neural Networks-Based Model Predictive Control for Continuous Fermentation Process in 1G/2G Ethanol Production Plant

Recurrent Neural Networks-Based Model Predictive Control for Continuous Fermentation Process in 1G/2G Ethanol Production Plant | 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 Recurrent Neural Networks-Based Model Predictive Control for Continuous Fermentation Process in 1G/2G Ethanol Production Plant Felipi Bezerra, Rubens Maciel Filho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8787739/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study presents the application of a Recurrent Neural Network-based Model Predictive Control (RNN-MPC) strategy for optimizing a continuous fermentation process. The RNN model, trained with a Mean Squared Error (MSE) of 7.14× 10 -5 , accurately predicted process dynamics and was integrated into the MPC framework. Optimal control parameters, including a prediction horizon of 10 hours and a control horizon of 1 hour, were identified to balance system stability with computational efficiency. Comparative analysis demonstrated that the RNN-MPC outperformed traditional PID control, achieving faster settling times (20 hours vs. 90 hours), reduced overshoot (0.2 vs. 2–5), and enhanced robustness to disturbances. These results underscore the effectiveness of RNN-MPC in managing complex bioprocesses and highlight its potential for broader industrial application. Model Predictive Control Recurrent Neural Network Continuous Fermentation Neural Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 04 Feb, 2026 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-8787739","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585769736,"identity":"3da6b962-82cc-4c89-8c5a-f2c83964b80a","order_by":0,"name":"Felipi Bezerra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3Rv2rCQBzA8d9xoMsvdL2S4L3CFQcRB1/FLHHNJB1KGynEJXFOofQxOp/8IC7iEzjYJVMHoUsLFZrEDBaS4Ohw3+VyBx/uTwBMpiusG/BAg9LV9L5YKT46AKqBoGbnZAMC9QUkHyrCwktIdz4n39/15CLODj9v9Ih2vIfDjGBg63qCq4ASlfVZtO6/xO8k0FkrlmwJhstJLRkLNyBU5D4Lj4OVk7HwgFshgdo0HEx+lOQplBlnx9d8l4Ic24hgJZmg6HBuBRVhbQTd8i53CXqcO+n0NnJSWEXbKQ6jphcj+vJ/d1IuUs4+H0Y3aIds/z0b9QZYT4r4/6k4/aYWUEdMJpPJdN4fiOZa6/M64cMAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Technology – Paraná","correspondingAuthor":true,"prefix":"","firstName":"Felipi","middleName":"","lastName":"Bezerra","suffix":""},{"id":585769737,"identity":"7c1f5325-38b6-4141-8f64-9a122f7895c5","order_by":1,"name":"Rubens Maciel Filho","email":"","orcid":"","institution":"State University of Campinas","correspondingAuthor":false,"prefix":"","firstName":"Rubens","middleName":"Maciel","lastName":"Filho","suffix":""}],"badges":[],"createdAt":"2026-02-04 14:27:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295029,"identity":"2fe42b2f-a6bd-4c9e-b263-1c60ab94e278","added_by":"auto","created_at":"2026-02-10 10:07:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244342,"visible":true,"origin":"","legend":"","description":"","filename":"RNNethanolcompleto.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787739/v1_covered_5ceb0f45-c14f-407c-8e8d-01df27965652.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recurrent Neural Networks-Based Model Predictive Control for Continuous Fermentation Process in 1G/2G Ethanol Production Plant ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"brazilian-journal-of-chemical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bjce","sideBox":"Learn more about [Brazilian Journal of Chemical Engineering](http://link.springer.com/journal/43153)","snPcode":"43153","submissionUrl":"https://www.editorialmanager.com/bjce/default2.aspx","title":"Brazilian Journal of Chemical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Model Predictive Control, Recurrent Neural Network, Continuous Fermentation, Neural Networks","lastPublishedDoi":"10.21203/rs.3.rs-8787739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThis study presents the application of a Recurrent Neural Network-based Model Predictive Control (RNN-MPC) strategy for optimizing a continuous fermentation process. 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