{"paper_id":"4923b4b6-e898-4027-8ff3-c2d3cbbdeeed","body_text":"Advancing Wine Fermentation: Extended Kalman Filter for Early Fault Detection | 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 Advancing Wine Fermentation: Extended Kalman Filter for Early Fault Detection Bruno Lima, Ricardo Luna, Daniel Lima, Julio Normey-Rico, Jose Perez-Correa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4419796/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract This work proposes an Extended Kalman Filter (EKF) state estimation approach for early detection of stuck and sluggish wine fermentations. The goal is to provide accurate information to enologists during fermentation to facilitate timely intervention and decision making. The study investigates the sensitivity of the fermentation process to various factors such as model parameters and initial conditions, especially for unmeasured nitrogen. It also shows how the estimation depends on meaningful sugar measurements, which are not available during the lag phase of fermentation. According to Monte Carlo simulations, the estimation algorithm was able to predict 95% of the problematic fermentations within the first few days. When initial nitrogen measurements are taken into account, a reliable prediction is available on the first day in 80% of the cases, justifying the additional cost. These results support the use of advanced control and monitoring methods in wine production and other alcoholic fermentation processes. Extended Kalman Filter Fault Detection Uncertainty Propagation Wine Fermentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 16 May, 2024 Submission checks completed at journal 16 May, 2024 First submitted to journal 14 May, 2024 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-4419796\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":303163591,\"identity\":\"b19e3120-0349-4b14-8716-0b0aef08c205\",\"order_by\":0,\"name\":\"Bruno Lima\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universidade Federal de Santa Catarina\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bruno\",\"middleName\":\"\",\"lastName\":\"Lima\",\"suffix\":\"\"},{\"id\":303163595,\"identity\":\"3e7fa776-a121-4029-9a9c-be9698fcf8c6\",\"order_by\":1,\"name\":\"Ricardo 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